Trends in Male Infertility Over the Past 50 Years: Landscape Analysis and the Emerging Role of Large Language Models

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In recent years, with the development and advancement of next-generation sequencing technologies and high-resolution mass spectrometry, the volume of male infertility-related literature in scientific databases such as Scopus and PubMed has rapidly increased, and its topics have undergone complex changes over the past 50 years. Additionally, the advent of large language models (LLMs) has provided new tools for enhancing traditional literature analysis and topic modeling. Objective: This study aims to investigate the changes and trends in research hotspots on male infertility over the past 50 years. Furthermore, to explore the potential of large language models (LLMs) in decision support systems for the clinical translation of male infertility research, we also evaluated the information enhancement capabilities of LLMs in the context of research hotspots on male infertility. Methods: Various methods, including bibliometrics, topic modeling, Gemini' and ChatGPT's question-answer approach, were employed to compare male infertility hotspots between real-world and virtual world data. Additionally, the study investigated LLMs's ability to enhance information in summarizing male infertility hotspots. Conclusions: Under the literature evidence of 14,852 male infertility-related publications (12,884 article-type publications and 1,968 review-type publications), traditional bibliometric analyses such as annual analysis, country analysis, and high-impact author analysis show that countries like the United States, China, and Italy are major publishers in infertility research, with the United States being the leading technical influencer in male infertility research. Subsequently, results from topic modeling analysis have effectively mapped out the research themes in male infertility over the past 50 years, this analysis highlights key subjects such as "the impact of gene expression on male infertility", "the effect of age on sperm parameters", and "pathogenic genes of male infertility", marking them as recent research hotspots. However, this method falls short in clearly presenting the latest hotspots in male infertility research. Lastly, the integration of LLMs information enhancement offers a new dimension in this research. This approach successfully presents the recent hotspots in male infertility, encompassing not only the impact of risk factors like "Environmental Exposures", "Genetics", "Immunological Factors", "Hormonal Imbalances" on sperm count and quality but also highlighting emerging areas such as "Precision Medicine" and "Artificial Intelligence (AI)" in male infertility research. Therefore, combining real-world literature evidence with the capabilities of LLMs is crucial for understanding and mapping future trends in this field. Male Infertility large language models (LLMs) Bibliometrics Topic Modeling Information Enhancement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction In recent years, due to the deterioration of the global environment and intensified local pollution, the incidence of male infertility has sharply increased, becoming a global public health issue affecting approximately 20% of couples with a desire to conceive[ 1 , 2 ]. Compared to female infertility, the etiology and pathogenesis of male infertility remain not fully clear due to the complexity of sperm production[ 3 ]. Biomedical literature has been a valuable resource for mining medical information and clinical applications[ 4 ]. With the advancements and widespread application of high-throughput sequencing technologies, such as exome sequencing, genome sequencing, transcriptome sequencing, proteome sequencing, and methylation sequencing, these techniques have been extensively utilized in the research of male infertility and the identification of disease biomarkers, significantly enhancing the knowledge of male infertility and enriching related medical literature[ 5 ]. Therefore, mining data resources in existing medical literature not only holds significant value for constructing broadly applied knowledge bases, knowledge graphs, chatbots, and clinical decision support systems, but also plays a crucial role in understanding the research limitations and future development trends of male infertility. Exploring the application of artificial intelligence in medical decision support systems has been one of the key pathways for its clinical translation. In 2022, within the field of artificial intelligence, ChatGPT 3.5 emerged as a landmark large language model (LLMs), trained on over 175 billion parameters. The corpus of this model not only originated from large text corpora but also included WebText2, Wikipedia, and a vast array of book materials [ 6 ]. The advent of ChatGPT 3.5 has had profound implications for multiple medical disciplines, such as pharmacology, radiology, dentistry, and otolaryngology. Some scholars have even suggested that ChatGPT might change the current paradigms of medical education and practice, potentially leading to the unemployment of many doctors[ 7 – 9 ]. However, there are also views that, as a Chat Generative Pre-trained Transformer model, all of ChatGPT’s understandings and inferences are fundamental. It is primarily used for querying and deducing basic knowledge and is unlikely to offer innovative insights. In certain specialized domains, its performance may not even surpass existing artificial intelligence models[ 8 ]. To gain a deeper understanding of LLMs's integration and information enhancement capabilities in reproductive medicine literature, particularly in the field of male infertility, this study selected the Scopus database as the primary data source. We utilized three core terms associated with male infertility -"Azoospermia", "Oligospermia", "Asthenospermia", "Teratozoospermia", and "Teratospermia" - as thematic search keywords, and employed bibliometrics and topic modeling as research methods, along with Gemini' and ChatGPT's question-answer feature as the main investigative approach. Initially, we explored the publication volume, publishing institutions, countries, and their collaboration networks, top scholars and their co-occurrence networks, and hot topics in the realm of male infertility based on scientific citation data (real world) as the data source. Subsequently, using Gemini and ChatGPT, we examined its capabilities in summarizing research hotspots in male infertility (virtual world). Finally, through comparative analysis, we investigated the information enhancement ability of LLMs in literature hotspot summarization and trend assessment. 2 Methods 2.1 Literature Retrieval Strategy and Basic Data Collection The Scopus database, owned by Elsevier Publishing Group, is a comprehensive abstract and citation database and one of the world's largest biomedical literature databases. Its inclusion of strictly peer-reviewed literature has garnered widespread attention in the industry. To systematically collect literature related to male infertility, we used high-frequency terms in the field of male infertility research - "Azoospermia", "Oligospermia", "Asthenospermia", "Teratozoospermia", or "Teratospermia" - as search keywords in the Scopus database, selecting related publications where these keywords appeared in the titles or abstracts. Considering the advent of PCR technology in the 1980s [ 10 , 11 ], the inclusion period for the literature was set from January 1, 1970, to December 31, 2022. Additionally, given the study's focus on English-language literature, we excluded articles categorized as communications, comments, or errata, considering only publications in English. Additionally, this study focused on scientific publications with human subjects. Therefore, only those publications that specified "human", "humans", or "humankind" as the research subjects were included. After filtering out irrelevant literature records, we categorized the retrieved male infertility-related records into review-type records and article-type records. The data for both categories were then exported as complete records in '*.BibTex' format [ 12 ]. These records comprised basic information, including authors, titles, DOI numbers, keywords, citation counts, publication years, journal sources, volumes, and issues. Additionally, they contained detailed information such as abstracts, author keywords, affiliations of the first author, and the corresponding author's institution[ 12 ]. While exporting citation records, we also exported preliminary analysis data from the Scopus database, including the annual number of publications, the distribution of publication subjects, the number of publications from academic institutions, and the number of publications from journal sources. 2.2 Bibliometric Analysis To gain a deeper understanding of the primary hotspots and developmental trends in male infertility research, we initially converted the research-type literature records into '*.BibTex' format into data frames using the bibliometrix package. We initially utilized the extensive bibliometric functions provided by the bibliometrix package to analyze annual publication volumes, annual citation counts, scholars' H-index, scholars' highest citation counts, total number of male infertility publications by scholars, total number of male infertility publications by academic institutions, total number of male infertility publications by countries, total number of male infertility publications by journals, and the frequency of international collaborations among other indicators [ 12 ]. Subsequently, we employed ranking methods to identify the top 10 publishing institutions, top 10 publishing journals, top 10 authors, and the annual changes in literature output of the top 10 authors. For further computational details such as the H-index and average citation count, we applied standard calculation methods. The H-index is calculated using \(\:h=max\left\{k:{p}_{k}\ge\:k\right\}\) , where \(\:{p}_{1},{p}_{2},\cdots\:,{p}_{k},\cdots\:,{p}_{n}\:\) are a researcher's list of papers, and these papers are sorted in descending order of their citation counts, with \(\:{p}_{1}\) being the most cited paper and \(\:{p}_{n}\) the least cited paper. In this study, scholars' H-index was directly calculated using the Hindex function provided by the bibliometrix package. For the number of average citation (AC), we calculated it using the standard method, \(\:\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{c}\text{i}\text{t}\text{a}\text{t}\text{i}\text{o}\text{n}\left(AC\right)=\frac{TC}{NP}\) , where \(\:TC\) represents the total number of citations, \(\:NP\) represents the total number of publications. Furthermore, to assess the contribution of journals related to male infertility to the development in the fields of biology and medicine, we calculated the contribution of each relevant journal based on the Influence Score. The specific calculation formula is as follows: $$\:\:{Influence\:Score}_{Journal}={JCI}_{Journal}*{N}_{record\:}\:\:\:\:$$ Where \(\:{JCI}_{Journal}\) ​ represents the Journal Citation Indicator of the journal, indicating the average CNCI (Category Normalized Citation Impact) value for articles published by the journal over the past three years, with data sourced from the Clarivate database[ 13 , 14 ]. Additionally, \(\:{N}_{record\:}\) ​is derived from the total number of publication records of that journal in this study. 2.3 Summary of Male Infertility Research Hotspots from Topic Modeling Analysis Topic modeling analysis is a commonly used method for exploring research hotspots and trends in academic disciplines. To investigate and interpret the research hotspots and trends in male infertility[ 15 , 16 ], we attempted to use Latent Dirichlet Allocation (LDA) [ 17 ] as our research methodology. In this study, we used abstracts from the Scopus database, which contain keywords such as "Azoospermia", "Oligospermia", "Asthenospermia", "Teratozoospermia", and "Teratospermia" in the titles and abstracts of male infertility research publications, as our data source. LDA was employed as the topic modeling method to explore a summary of hotspots in male infertility research based on topic modeling. For constructing the corpus, we first excluded numbers, punctuation marks, conjunctions, English stop words, 1% of rare vocabulary, and words with a frequency over 99%, and then built the corpus with the quanteda package [ 18 ], then we used the dfm function from the quanteda package to convert the corpus into a document matrix, incorporating metadata such as publication year, titles, and others. Furthermore, we employ the LDA topic modeling based on Gibbs sampling described by Ponweiser et al. [ 19 ]. However, our method differs from the one described in the original literature in the choice of the optimal number of topics. In this study, we attempt to select the optimal number of topics using the metrics in the ldatuning package [ 20 ]: the Griffiths2004 index [ 21 ], CaoJuan2009 index [ 22 ], Arun2010 index [ 23 ], and Deveaud2014 index [ 24 ]. Additionally, to avoid difficulties in effectively visualizing and summarizing temporal trends due to excessively large numbers of topics, we specifically set the range of potential optimal topics between 2 to 15. We use a gradient ascent method (with a step size of 1) to select the optimal number of topics, aiming for the smallest values of the Griffiths2004 index and CaoJuan2009 index, and the largest values of the Arun2010 index and Deveaud2014 index as the criteria for the best topic number. Subsequently, with the optimal number of topics determined, we performed LDA topic modeling using the Gibbs sampling method. The parameters were set according to the defaults in the topicmodels package, with the number of iterations set to 2000, sampling times to 10000, and testing frequency every 500 iterations. To ensure the accuracy of the posterior results, we discarded the first 4000 samples [ 18 ]. 2.4 Analysis of Male Infertility Research Hotspots Enhanced by ChatGPT To explore the augmentative effects of LLMs in the analysis of male infertility literature, as well as to compare the differences in themes and hotspots in male infertility research based on real-world (literature) data and virtual-world (LLMs ) data, we initiated our study with the query prompt "Could you list the current hotspots in male infertility research? ". We entered this prompt into various language models, including Gemini, GPT-3.5, GPT-4, and GPT-4o. Subsequently, we extracted the hotspots and their descriptions related to male infertility research from the responses provided by ChatGPT [25] or Gemini [ 26 ]. The precise dates of data extraction for these hotspots were April 10, 2024, and December 10, 2023. Next, we compared the overlap between the hotspots derived from literature data and those provided by LLMs to assess the additive benefits of large language models in analyzing themes and hotspots within male infertility research. To ensure the reliability of our analysis, we repeated the query three times. 2.5 Data Statistics and Visualization The aforementioned data analysis and visualization have been completed in the R environment [ 27 ]. We used the bibliometrix package to process '*.BibTex' format data and conduct basic bibliometric analyses [ 12 ], which included statistics such as annual publication volume, annual citation counts, top 10 publishing institutions, top 10 publishing journals, top 10 authors ranked by H-index, and annual changes in literature output of the top 10 authors, as well as analyses of international collaboration networks. For topic modeling, the Dirichlet distribution method from the topicmodels package was employed [ 18 ]. In the process of data preprocessing and corpus construction, packages such as dplyr [ 28 ], quanteda [ 29 ], and ldatuning [ 20 ] were used. The visualization of results primarily relied on the ggplot2 [ 30 ] and sf [ 31 ] packages. Subsequent graphic enhancements have been made using the Inkscape tool, with world map data sourced from the open data of the Natural Earth network. 3 Results 3.1 Analysis of Male Infertility Research Hotspots and Trends Based on Bibliometric Evidence As of December 31, 2022, we have retrieved a total of 21,263 scientific publications records from the Scopus database that contained 'Azoospermia', 'Oligospermia', 'Asthenospermia', 'Teratozoospermia', or 'Teratospermia' in their titles or abstracts. After excluding non-timeline citations, non-manuscript-type records, non-English language records, and records not focusing on humans as research subjects, we ultimately included 14,852 scientific publications related to male infertility. This collection comprised 12,884 article-type publications and 1,968 review-type publications. For more details on the publications, refer to Figure 1. 3.1.1 Bibliometric Analysis of Annual Publication Numbers in Male Infertility Research According to the number of scientific publications on male infertility, the number has increased from 10 per year in 1970 (including 10 article-type publications and 0 review-type publications) to 792 per year in 2022 (including 665 article-type publications and 127 review-type publications). The average annual growth rate of publications is 8.77%, with article-type publications growing at an average annual rate of 8.40%, and review-type publications at a rate of 9.03% (Figure 2A). The analysis of the annual growth rate of publications reveals that article-type publications have experienced three near-exponential growth periods: in the 1970s to 1980s (1970-1980), the 1990s (1990-2000), and around 2020 (2015-present). These growth periods are speculated to correspond to three major technological revolutions in nucleic acid testing: the advent of PCR testing technology[10, 11], the development of Next-generation sequencing (NGS) technology[32], and the development of single-cell sequencing technology[33, 34] (Figure 2A). Review-type publications, on the other hand, show only two near-exponential growth periods, occurring from the 1990s to the early 21st century (1990-2006) and around 2020 (2017-present). This growth is presumed to be due to an increase in systematic summaries and theoretical generalizations triggered by a large increase in article-type publications related to male infertility (Figure 2A). Comparing the ratio of review-type publications to article-type publications, it is found that there was a rapid growth period during the 1990s to the early 21st century (1990-2002) and around 2020 (2017-present), peaking in 2021 (Figure 2B). This peak is speculated to be related to the advent and development of PCR testing technology[10, 11] and the maturation of Next-generation sequencing (NGS) technology[32], marking the maturation and perfection of the first layer of basic theories in male infertility scientific literature, inseparably linked to the development and maturation of these technologies. Undoubtedly, the recently emerged single-cell omics technology is an effective method for understanding the basic theories and mechanisms of male infertility, which will trigger a new wave of theoretical summaries. An analysis of the disciplinary composition of male infertility research shows that article-type publications on male infertility have primarily been published in the fields of medicine (60.6%), biochemistry and molecular biology (28.3%), and pharmacology (2.3%). Other subjects such as agriculture, veterinary, chemical engineering, nursing, etc., only published a total of 8.80% (1134 publications) publications (Figure 2C). Similarly, review-type publications on male infertility have also been predominantly published in disciplines like Medicine, Biochemistry and Molecular Biology, and Pharmacology (Figure 2D). However, compared to article-type publications, the proportion of review-type publications published in the fields of medicine, biochemistry and molecular biology, and pharmacology. But compared with article-type publications, review-type publications published in the fields of Medicine are slightly higher (Figure 2D). An analysis of the disciplinary composition of male infertility research shows that article-type publications on male infertility have primarily been published in the fields of medicine (60.6%), biochemistry and molecular biology (28.3%), and pharmacology (2.3%). Other subjects such as agriculture, veterinary, chemical engineering, nursing, etc., only account for a total of 8.80% (1134 publications) (Figure 2C). Similarly, review-type publications on male infertility have also been predominantly published in disciplines like medicine, biochemistry and molecular biology, and pharmacology (Figure 2D). However, compared to article-type publications, the proportion of review-type publications published in the fields of medicine are slightly higher (Figure 2D). 3.1.2 Analysis of Top Scholars in Male Infertility and Their Collaboration Networks Assessment of author influence and publication trends is crucial for understanding an author's impact in their field and identifying top (top 10) scholars. Analysis of data on top scholars in male infertility publications reveals that researchers from the United States, Italy, and Belgium are leading in this area. In article-type publications, Italy (with three top authors, accounting for 30.00% of the total top 10), Belgium (also with three top authors, making up another 30.00% of the top 10), and the United States (with two top authors, comprising 20.00% of the top 10) dominate the field. Other countries like Germany and China have only one author each on the list. In review-type publications, Italian authors lead significantly with five (50.00% of the total top 10), followed by the United States (three authors, 30.00%), Brazil (one author, 10.00%), and Belgium (one top author, 10.00%).Assessing author influence and publication trends is crucial for exploring an author's disciplinary impact and identifying top scholars (top 10). An analysis of the top scholars (top 10) in male infertility publications reveals that researchers from the United States, Italy, and Belgium hold leadership positions in the field. For instance, in article-type publications, Italy (with three top authors, accounting for 30.00% of the top 10 total), Belgium (also three top authors, making up 30.00% of the top 10), and the United States (two top authors, representing 20.00% of the top 10) dominate, while other countries like Germany and China have only one author making the list. In review-type publications, Italian authors lead significantly with five (50.00% of the top 10), followed by the United States (three authors, 30.00% of the top 10), Brazil (one author, 10.00%), and Belgium (one top author, 10.00%)(Table 1). An interesting observation in the data for both article and review-type top 10 scholars is that four authors rank in the top 10 for both types of publications. These are Schlegel P. N. from Weill Cornell Medicine, Cornell University, USA; Ferlin A. from the University of Padova, Italy; Foresta C. also from the University of Padova, Italy; and Tournaye H. from Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium (Table 1). Table 1: Top Scholars in Male Infertility Based on H-index Influence Scores Rank Author Country Affiliation H-index PY_start a) TC b) NP c) AC d) Article-type 1 Schlegel P. N. USA Weill Cornell Medicine, Cornell University 54 1993 9401 105 89.53 2 Nieschlag E. Germany University Hospitals Münster, University of Münster 46 1978 7478 110 67.98 3 Tournaye H. Belgium Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel 41 1991 8008 75 106.77 4 Foresta C. Italy University of Padova 40 1992 4836 84 57.57 5 Devroey P. Belgium Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel 38 1986 9937 50 198.74 6 Lipshultz L. I. USA Baylor College of Medicine 37 1979 4670 65 71.85 7 Ferlin A. Italy University of Padova 34 1995 3599 57 63.14 8 Van Steirteghem A. Belgium Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel 34 1994 4531 47 96.40 9 Garolla A. Italy University of Padova 32 1996 3196 50 63.92 10 Wang X. China State Key Laboratory of Reproductive Medicine, Nanjing Medical University 32 2003 3664 148 24.76 Review-type 1 Agarwal A USA Cleveland Clinic Lerner College of Medicine, Case Western Reserve University 27 2004 3326 39 85.28 2 Esteves S. C. Brazil Universidade Estadual de Campinas 21 2011 1645 25 65.80 3 Krausz C. Italy University of Florence 21 1999 3774 24 157.25 4 Schlegel P. N. USA Weill Cornell Medicine, Cornell University 21 1997 1252 30 41.73 5 Ferlin A. Italy University of Padova 13 2000 1613 14 115.21 6 Foresta C. Italy University of Padova 13 2000 1605 15 107.00 7 Tournaye H. Belgium Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel 13 1994 1263 20 63.15 8 Calogero A. E. Italy University of Catania 12 2000 598 18 33.22 9 Ramasamy R. USA Leonard M. Miller School of Medicine, University of Miami 12 2012 468 18 26.00 10 Condorelli R. A. Italy University of Catania 11 2013 708 14 50.57 PY_start a) represents the year in which publications on male infertility were first published. TC b) represents the total number of citations for publications (either articles or reviews) on male infertility by a particular scholar. NP c) represents the total number of publications (either articles or reviews) by a particular scholar in the field of male infertility research. AC d) represents the average number of citations per publication (either articles or reviews) on male infertility by a particular scholar. The calculation formula is TC/NP. In article-type publications on male infertility, Schlegel P. N. from Weill Cornell Medicine (Cornell University, USA) ranks first with an H-index of 54. His earliest publication dates back to 1993, and over the past 30 years, he has published 105 article-type publications with a total of 9401 citations, averaging 89.53 citations per article (Table 1). An analysis of Schlegel P. N.'s publication trajectory shows his peak publishing years were between 1995 and 2015, averaging 5-10 articles per year with about 75 citations annually (Figure 3A). Schlegel P. N. is also a top 10 author in review-type publications, ranking fourth, with his earliest review publication dating back to 1997. Over nearly 30 years, he has published 30 review-type articles, totaling 1252 citations, averaging 41.73 citations per article (Table 1), and his reviews are evenly distributed over the 1997-2022 period (Figure 3B). The author with the highest number of article-type publications is from University Hospitals Münster (University of Münster, Germany), publishing 110 articles over more than 40 years (1978-2022), with a total of 7478 citations, averaging 67.98 citations per article (Table 1). His publication trajectory shows a steady output of review-type publications spread across the 1978-2022 period (Figure 3A). Additionally, Devroey P. from Universitair Ziekenhuis Brussel (Vrije Universiteit Brussel, Belgium) has the highest citation count for article-type publications among male infertility researchers, with an average citation rate of 198.74 per article, publishing 50 papers over 37 years (1986-2022) with a total of 9937 citations. In review-type publications, Agarwal A. from Cleveland Clinic Lerner College of Medicine (Case Western Reserve University, USA) ranks first with an H-index of 27, publishing 39 review-type articles over 18 years with a total of 3326 citations, averaging 85.28 citations per article (Table 1). A trajectory analysis of Agarwal A.'s reviews shows a significant increase in both the number of publications and citations since 2021, reaching up to 6 articles per year with over 100 citations annually (Figure 3B). The second highest H-index among top 10 review-type authors is Esteves S. C. from Universidade Estadual de Campinas (Brazil), who has published 25 review-type articles over 12 years (2011-2022) with a total of 1645 citations, averaging 65.80 citations per article (Table 1). The highest total and average citation counts among male infertility review-type authors belong to Krausz C. from the University of Florence (Italy), who has published 24 review-type articles over 24 years (1999-2022) with a total of 3774 citations, averaging 157.25 citations per article (Table 1). A study of the publication trajectories of Esteves S. C. and Krausz C. reveals an annual publication rate of 1-4 review articles, with an average annual citation rate of about 50 (Figure 3B). 3.1.3 Exploring the National Differences and Collaboration Networks in Male Infertility Research In the study of the nationalities of publications, it has been found that the United States leads with 2989 publications (including 2249 article-type publications and 740 review-type publications), while China ranks second with 1898 publications (comprising 1766 article-type publications and 132 review-type publications). In addition, Italy, the United Kingdom, Germany, India, Japan, France, Iran, and Turkey are significant publishing countries in the field of male infertility (Figure 4A). To further quantify the ability of countries to systematically review and summarize in male infertility, we used the mean ratio of review-type to article-type publications as a measure. The results show that 23 countries, including Iceland, Qatar, New Zealand, and the United States, exceed the average ratio of review-type to article-type publications. In contrast, traditional publishing powerhouses like China, India, Japan, France, Iran, and Turkey have ratios significantly below the average (Figure 4B). An analysis of the publication collaboration networks for article-type and review-type publications has revealed that, with a few exceptions like Trinidad and Tobago, Malta, and Albania, most of the world collaborates with each other, forming a complex and intricately interconnected collaboration network (Figures 4C and 4D). However, a comparison between the collaboration networks of article-type and review-type publications shows distinct differences in the main collaborating countries in male infertility research. For instance, in research publications, China is a major collaborator with the United States (Figure 4C), but in review publications, Canada, Italy, the United Kingdom, and Brazil emerge as primary collaborators, with China becoming a secondary partner. In the study of the top 10 research institutions by publication volume in research publications (totaling 1350 publications, accounting for 10.48% of the total male infertility publications), we have found that they belong to eight different countries, with European and American institutions predominating. Inserm in France leads with a total of 183 publications, followed by Nanjing Medical University (China) and Royan Institute (Iranian) with 165 and 145 publications, respectively (Table 2). Regarding the top 10 institutions in review paper publication volume (totaling 319 publications, accounting for 16.21% of the total male infertility publications), we have discovered that the majority are from the United States (5 research units or universities, making up 50.00%), with renowned institutions including Weill Cornell Medicine (53 publications), Cleveland Clinic Foundation (51 publications), Baylor College of Medicine (39 publications), Harvard Medical School (24 publications), and University of Washington (23 publications). Italy also has two institutions in the top 10, which are Sapienza Università di Roma (35 publications) and Università degli Studi di Firenze (31 publications). Table 2: Top 10 Academic Institutions Ranked by Total Publication number related to Male Infertility * Rank Academic Institutions a) Country Total number of Publications Article-type 1 Inserm France 183 2 Nanjing Medical University China 165 3 Royan Institute Iranian 145 4 Cairo University Egypt 143 5 Weill Cornell Medicine United States 133 6 Sapienza Università di Roma Italy 128 7 University of Münster Germany 126 8 Tel Aviv University Israel 116 9 Shanghai Jiao Tong University China 107 10 Tehran University of Medical Sciences Germany 104 Review-type 1 Weill Cornell Medicine United States 53 2 Cleveland Clinic Foundation United States 51 3 Baylor College of Medicine United States 39 4 Sapienza Università di Roma Italy 35 5 Università degli Studi di Firenze Italy 31 6 Harvard Medical School United States 24 7 University of Washington United States 23 8 Imperial College London United Kingdom 22 9 Centrum voor Reproductieve Geneeskunde Belgium 21 10 University of Toronto Canada 20 a) In the top 10 ranking, we have excluded two administrative institutions: the Ministry of Education of the People's Republic of China (191 publications) and the Iranian Academic Center for Education, Culture and Research (167 publications). 3.1.4 Analysis of Male Infertility Journals' Influence Based on Publication Numbers and Influence Scores Exploring the field influence and bias of journals is a crucial application in clarifying their academic domains. An analysis of the total number of Article-type publications and the disciplinary impact in male infertility journals has revealed that Fertility and Sterility , Andrologia , Human Reproduction , and Journal of Assisted Reproduction and Genetics are the leading journals in terms of publication volume in male infertility research, with respective total publications of 1106 (accounting for 8.58% of the total Article-type publications), 822 (6.38%), 731 (5.67%), and 322 (2.50%) (Table 3). However, when measured by impact factor, 5-year impact factor, and Journal Citation Indicator, Human Reproduction , Fertility and Sterility , Journal of Urology , and Andrology emerge as the most significant journals in male infertility (Table 3). Combining both publication numbers and journal impact, Fertility and Sterility , Human Reproduction , Andrologia , and Journal of Urology rank as the most important journals in male infertility, with influence scores of 2422.14, 1637.44, 706.92, and 537.66, respectively (Table 3). Table 3: Analysis of Male Infertility Journals' Influence Based on Publication Numbers and Influence Scores Rank Journal Total Number of Articles Journal Impact index Influence Index IF a) 5-Year IF b) JCI c) Article-type publications 1 Fertility And Sterility 1106 6.70 7.50 2.19 2422.14 2 Andrologia 822 2.40 2.50 0.86 706.92 3 Human Reproduction 731 6.10 7.10 2.24 1637.44 4 Journal of Assisted Reproduction and Genetics 322 3.10 3.50 0.93 299.46 5 International Journal of Andrology 276 3.70 3.27 NA NA 6 Journal of Urology 261 6.60 6.20 2.06 537.66 7 Journal of Andrology 239 2.47 2.56 NA NA 8 Asian Journal of Andrology 220 2.90 3.00 1.04 228.8 9 Andrology 196 4.60 4.30 1.57 307.72 10 Urology 188 2.10 2.20 0.74 139.12 Review-type publications 1 Asian Journal of Andrology 56 2.90 3.00 1.04 58.24 2 Urologic Clinics of North America 44 2.40 2.30 0.79 34.76 3 Translational Andrology and Urology 39 2.00 2.50 0.61 23.79 4 Fertility And Sterility 33 6.70 7.50 2.19 72.27 5 Human Reproduction Update 30 13.30 17.80 3.47 104.10 6 Human Reproduction 25 6.10 7.10 2.24 56.00 7 International Journal of Molecular Sciences 24 5.60 6.20 0.71 17.04 8 International Journal of Andrology 23 3.70 3.27 NA NA 9 Nature Reviews Urology 22 15.30 16.60 2.67 58.74 10 Journal of Andrology 22 2.47 2.56 NA NA a) Impact Factor（IF）measures the average number of citations received by articles published in a journal over the previous two years., with data sourced from the Clarivate database. b) 5-Year IF(5-Year Impact Factor) measures the average number of citations received by articles published in a journal over the past five years., with data sourced from the Clarivate database. c) The Journal Citation Indicator (JCI) quantifies the average citation impact of articles in a journal over the three-year period, with data sourced from the Clarivate database. Compared to article-type publications, review-type publications are relatively fewer in number and more dispersed across journals. For instance, Asian Journal of Andrology has published 56 Review-type articles on male infertility in the last 50 years (1974-2022), accounting for only 2.85% of the total Review-type articles. Other fertility-related journals such as Urologic Clinics of North America , Translational Andrology and Urology , and Fertility and Sterility have published even fewer articles. However, in terms of journal impact measured by the impact factor, 5-year impact factor, and Journal Citation Indicator, review-focused journals with significant influence in the field of male infertility, such as Human Reproduction Update and Nature Reviews Urology , have considerably higher impact scores compared to Article-type journals like Human Reproduction , Fertility and Sterility , Journal of Urology , and Andrology (Table 3). Considering both publication numbers and journal impact, Human Reproduction Update , Fertility and Sterility , Nature Reviews Urology , Asian Journal of Andrology , and Human Reproduction emerge as the most important journals in the field of male infertility reviews, with influence scores of 104.10, 72.27, 58.74, 58.24, and 56.00, respectively (Table 3). 3.2 Determining Research Hotspots in Male Infertility Using LDA Topic Modeling Analysis Topic modeling analysis based on LDA (Latent Dirichlet Allocation) is the most commonly used method for studying the distribution of research hotspots and their temporal changes based on literature evidence (in the real world). However, one challenge in topic modeling is choosing the most appropriate (or optimal) number of topics. In this study, to avoid the difficulties in effective visualization and summarizing temporal trends associated with an overly large number of topics, we set the potential optimal number of topics to range from 2 to 15. We used indices such as Griffiths2004 [21], CaoJuan2009 [22], Arun2010 [23], and Deveaud2014 [24] as selection criteria. When set to k=15, the indices for Griffiths2004 [21], CaoJuan2009 [22], Arun2010 [23], and Deveaud2014 [24] were all optimal (Figure 5A). A frequency analysis of keywords under the optimal number of topics (k=15) revealed that different topics have different keyword frequencies. For example, Topic 1, with keywords including "patient", "treatment", "age", "cancer", etc., indicates that the main research direction of this topic is the treatment of patients with conditions like "age", "cancer", etc. (Figure 5B). Other topics also show clear patterns of keyword variation. For instance, Topic 5, with keywords such as "Express", "Human", "Cell", "Protein", "Gene", etc., indicates that the main research direction is exploring the expression of different genes or proteins in human cells (Figure 5F). Topic 8, with keywords including "Chromosome", "Delete", "Microdelete", "Y", " AZF c", etc., indicates that the main focus is on exploring the effects of chromosomal variations, microdeletions, and AZF c deletions or microdeletions on male infertility (Figure 5I). Observations of the temporal patterns of topics related to male infertility revealed that different subjects experienced distinct temporal changes (Figure 5Q). For instance, topics 3 and 12 exhibited an initial increase followed by a decrease in trend, peaking in the mid-1970s and mid-1980s respectively, but their proportion of publications gradually declined entering the 1990s (Figure 5Q). Analysis of the word cloud for these topics showed that topic 3 includes keywords such as "sperm", "pregnanc*", "motil*", "rate", "fertil*", suggesting that this topic mainly focuses on exploring the impact of sperm motility rate on fertility outcomes (Figure 5D). Topic 12 includes keywords like "stud*", "use", "method", "reproduct*", "clinic*", "data", indicating a focus on exploring clinical data-driven methods in male reproductive medicine (Figure 5M). Unlike topics 3 and 12, topics 2 and 10 displayed a steadily increasing trend over the years (Figure 5Q). Topic 2 includes keywords such as "sperm", "spermatzoa", "motil*", "morpholog", "sampl*", "DNA", suggesting that it focuses on exploring characteristics of sperm DNA, such as mutations and diversity, and their correlation with sperm morphology or activity (Figure 5C). Topic 10 includes keywords like "testicular", "biopsi*", "cell", "spermatogenesi*", "spermatid", focusing on the anatomical dissection of the testes or investigation of testicular cell composition, such as spermatogonia, spermatocytes, and spermatids, which has seen rapid development entering the 21st century (Figure 5K). Long-term, the anatomical dissection and cellular composition investigation of the testes have been vital for exploring the pathological causes of male infertility [35, 36] or for investigating regenerative stem cell therapies [35, 37]. Recently, with advancements including single-cell genomic technologies [33, 34], a newer and more comprehensive understanding of the various cellular compositions in the testes, especially differentiating spermatogonial stem cells, has emerged. The development of these technologies and the discovery of new findings offer fresh perspectives and insights into investigating testicular cell composition or exploring regenerative stem cell-based treatments. 3.3 Analysis of Male Infertility Research Hotspots Enhanced by ChatGPT LLMs are super tools in the field of artificial intelligence, proven to have exceptional capabilities for summarizing trends. To explore the benefits of large language models in male infertility research, we attempted to investigate the enhancement effects of GPT-3.5, GPT-4, GPT-4o, and Gemine in hot topics of male infertility research through a question-and-answer format. The results show that LLMs total collectively addressed 24 hotspots in male infertility research, covering risk factors such as "Environmental Exposures", "Psychological and Social Aspects", "Genetics", "Immunological Factors", "Hormonal Imbalances" and their impacts on sperm count, quality, and male infertility (Table 4). Regarding the treatment and methods of male infertility, LLMs responded to topics such as "Assisted Reproductive Technologies (ART)", "Stem Cell Research", "Socioeconomic disparities", "Male Fertility Preservation", which are hotspots for the treatment and prevention of male infertility (Table 4). Additionally, concerning future directions in male infertility research, LLMs also mentioned the potential of "Precision Medicine" and "Artificial Intelligence (AI) " in male infertility research (Table 4). For the frequency statistics of responses to these topics, only 25.00% of the topics were recommended by a unique large language model (LLM). These topics include “Molecular and Cellular Pathways”, "Diet and Nutrition", "Omics Approaches", "Regenerative Medicine","Male Birth Control", and “Artificial Intelligence (AI)”, and “Precision Medicine in Male Infertility”. Specifically, "Diet and Nutrition", "Omics Approaches" and "Regenerative Medicine" were only mentioned by GPT-4o, while "Male Birth Control", "Artificial Intelligence" and "Precision Medicine" were exclusively mentioned by GPT-4 (Table 4). If we further consider that GPT-3.5, GPT-4 and GPT-4o are just different versions of ChatGPT, the number of topics uniquely recommended by a single LLM rises to 45.83%. In addition to "Socioeconomic disparities", "Artificial Intelligence", and "Precision Medicine", topics such as "Male Fertility Preservation", "Immunological Factors in Male Infertility", "Molecular and Cellular Pathways", "Sperm Quality and Function", "Male Contraception", and "Male Birth Control" were exclusively mentioned by ChatGPT (Table 4). Furthermore, a comparison of the number of topics recommended by different large language models reveals that Gemine recommended 21 topics (Including the same or repeated hotspots), which is significantly fewer than the 28 (Including the same or repeated hotspots and 25 topics (Including the same or repeated hotspots) recommended by GPT-3.5 and GPT-4, respectively (Table 4). Considering that the topic question-and-answer sessions all used the same prompts and the same number of repetitions (3 times), it is estimated that the number of topics recommended is related to the default temperature settings. Table 4: Evaluation of Male Infertility Research Hotspots Enhanced by ChatGPT Hotspots Hotspots Description Number total a) Large language models (LLMs) Gemine b) GPT - 3.5 c) GPT - 4 d) GPT-4 o e ) Environmental Exposures and Male Fertility Investigating the effects of endocrine-disrupting chemicals, lifestyle factors, and environmental toxins on male reproductive health. 12 3 3 3 3 Assisted Reproductive Technologies (ART) Developing and improving ART techniques like intracytoplasmic sperm injection (ICSI) and testicular sperm extraction (TESE). 10 2 3 2 3 Genetics and Male Infertility Researching genetic factors that may contribute to male infertility and potential genetic treatments or interventions. 9 2 3 1 3 Epigenetics and Sperm Health Further exploration of how epigenetic modifications in sperm may impact fertility and offspring health. 9 2 2 2 3 Psychological and Social Aspects of Male Infertility Exploring the psychological impact of male infertility on individuals and couples, as well as interventions to address these issues. 9 1 3 2 3 Male Hormonal Imbalances Exploring the role of hormones in male infertility and developing hormone-based therapies. 8 1 2 2 3 Stem Cell Research Exploring the potential use of stem cells in treating male infertility. 8 1 1 3 3 Oxidative Stress and Sperm DNA Damage Examining how oxidative stress contributes to infertility by damaging sperm DNA, and exploring potential antioxidant therapies to mitigate this damage. 7 3 1 0 3 Sperm Health and Quality Understanding factors that affect sperm quality and ways to improve it. 6 1 1 1 3 Male Fertility Preservation Research on techniques for preserving male fertility, especially in individuals undergoing cancer treatments or other medical procedures. 6 0 2 1 3 Immunological Factors in Male Infertility Understanding the role of the immune system in male infertility and potential immune-based treatments. 6 0 2 1 3 Novel diagnostic tools Researchers are developing new tools for diagnosing male infertility. This includes exploring the use of advanced sperm analysis techniques and biomarkers to better assess sperm health and identify potential causes of infertility. 5 2 0 0 3 Molecular and Cellular Pathways Delving into the molecular and cellular mechanisms underlying male reproductive function to develop targeted therapies. 5 0 2 0 0 Sperm Quality and Function Investigating ways to improve sperm quality, including lifestyle factors (such as diet, exercise, and exposure to environmental toxins) that impact sperm health. 5 0 1 1 3 Male Contraception Ongoing efforts to develop new methods of male contraception that are safe, effective, and reversible. 5 0 1 1 3 Socioeconomic disparities in infertility treatment Investigate studies on how access to infertility diagnosis and treatment can vary depending on socioeconomic status, highlighting potential healthcare inequalities. 4 1 0 0 3 Diet and Nutrition Investigating how different diets and nutritional supplements affect male fertility. 3 0 0 0 3 Omics Approaches Utilizing Next-generation sequencing (NGS) or other omics technologies to identify genetic causes of infertility and to understand the complex genetics of spermatogenesis. 3 0 0 0 3 Regenerative Medicine Developing regenerative medicine approaches to repair or regenerate damaged reproductive tissues. 3 0 0 0 3 Male Birth Control Researching new methods of male contraception. 2 0 0 2 0 Epidemiological and Demographic Studies Conducting large-scale studies to understand the prevalence, causes, and trends in male infertility across different populations and regions. 2 1 1 0 0 Ethical and Legal Considerations Discussion of ethical and legal issues surrounding fertility treatments, sperm donation, and genetic testing. 2 1 0 1 0 Artificial Intelligence (AI) and Male Fertility Assessment The use of AI and machine learning algorithms for analyzing sperm quality and predicting fertility outcomes. 1 0 0 1 0 Precision Medicine in Male Infertility Tailoring infertility treatments based on an individual's genetic and molecular profile to optimize outcomes. 1 0 0 1 0 Number total a) represents the total number of this hotspot were both predicted by Google Gemine, GPT -3.5 and GPT-4. Gemine b) represents the total number of this hotspot were predicted by Google Gemini. GPT-3.5 c) represents the total number of this hotspot was predicted by GPT-3.5. GPT-4 d) represents the total number of this hotspot was predicted by GPT-4. GPT-4o e) represents the total number of this hotspot was predicted by GPT-4o. Comparing the analysis of male infertility research hotspots based on real-world (literature evidence) and LLMs-enhanced data, we have found that the hotspots in male infertility research based on literature evidence are presented in the form of keywords. Through time series analysis, they clearly reflect the research hotspots and their trends in various periods but fail to clearly present the latest hotspots in male infertility research. However, LLMs perfectly presents the recent hot topics in male infertility. Yet, regardless of whether it's ChatGPT or Gemini , LLMs's summarization of hotspots is based on a principle of randomness, with its recommendation frequency unrelated to the degree of attention the hotspots received (Table 4). Therefore, exploring hotspot analysis based on real-world (literature evidence) and LLMs-enhanced data holds significant importance and value for studying future trends in male infertility research. 4. Discussion 4.1 Principal Results In recent years, with the development of LLMs, exploring the potential applications of such models, including ChatGPT and Google Gemini, in translational medicine has emerged as a cutting-edge and challenging task [ 38 ]. Several studies have shown that ChatGPT can not only accelerate the integration of scientific research and clinical practice but also enhance the quality and efficiency of healthcare services [ 39 – 41 ]. However, instances of ChatGPT being used in translational informatics research on male infertility have not yet been seen. To explore and compare the differences in themes and hotspots in male infertility research based on real-world (literature) data and virtual world data (LLMs), we employed bibliometrics, topic modeling analysis, and LLMs information enhancement as our research methods. We used high-frequency vocabulary in male infertility research such as "Azoospermia", "Oligospermia", "Asthenospermia", "Teratozoospermia", and "Teratospermia" as keywords for title and abstract searches. Through the data analysis of 14,852 male infertility publications, it has been shown that LLMs are effective tools and methods for information enhancement in translational medical research on male infertility. They can systematically summarize high-frequency topics from traditional bibliometrics, such as "Environmental Exposures", "Psychological and Social Aspects", "Genetics", "Immunological Factors", and "Hormonal Imbalances". Furthermore, these models are also capable of addressing low-frequency topics that traditional bibliometrics may not effectively summarize, such as "Assisted Reproductive Technologies (ART)", "Stem Cell Research", "Male Fertility Preservation", "Precision Medicine", and "Artificial Intelligence (AI)", etc. Comparison of the results analysis based on literature evidence and LLMs Q&A reveals that LLMs's answers can not only cover the impacts on sperm quantity, quality, and function that are widely reported in literature data, such as "Environmental Exposures", "Genetic Factors", "Epigenetic Factors", "Hormonal Imbalances", etc., but also take into account evidence less reported in literature data, such as "Socioeconomic factors", "Precision Medicine", "Artificial Intelligence", "Fertility Preservation", and "Ethics and Law in Infertility".Therefore, LLMs is an effective tool for exploring and understanding research trends and hotspots in the field, with significant potential applications in quickly grasping disciplinary directions and formulating development strategies for the discipline. 4.2 Limitations This study attempts to explore the changing trends in male infertility research hotspots through methodologies based on bibliometrics and the information enhancement capabilities of LLMs. The results indicate that LLMs, including Google Gemini, GPT-3.5, and GPT-4, demonstrate outstanding abilities in summarizing the hotspots of male infertility research. However, as the methodologies of this study primarily rely on bibliometric evidence and the capabilities of LLMs, there are several limitations. One main limitation of the analysis is that the data resources in this study are limited to the Scopus database, neglecting literature from other citation databases such as PubMed and Web of Science. The omission of PubMed and Web of Science literature was not part of the initial design of this study. In the early stages of the project, we also attempted to merge citations based on DOI numbers. However, PubMed does not include complete citation records, and although Web of Science includes full citation records, it lacks filtering methods based on inclusion and exclusion of research subjects (e.g., filtering studies that focus only on human or human population research), which led to its exclusion. In the analysis of the literature growth trend, we attribute the increase in male infertility literature to the development and transformation of scientific and technical methodologies, including the advent of PCR technology, the popularity of NGS sequencing, and the spread of single-cell genomics. However, we also acknowledge the contributory role of socio-economic factors in the increase of male infertility literature, such as heightened societal awareness, an increase in the number of researchers, and ample research funding, which are significant drivers behind the rise in the number and publications of male infertility research. There are also limitations in summarizing research hotspots on male infertility based on LLMs, in that this study exclusively utilized Gemini, GPT-3.5, GPT-4, and GPT-4o as tools for analysis. The choice of Google Gemini, GPT-3.5, GPT-4, and GPT-4o as representatives of LLMs is due to their exceptional performance across various tasks and their widespread use [ 9 , 42 , 43 ]. Additionally, a comparative analysis of the correlation between the response frequencies of Google Gemini, GPT-3.5, and GPT-4 and the research hotspots of male infertility revealed a certain randomness in how these models summarize the research hotspots. There is no correlation between their recommendation frequencies and the level of attention to the hotspots. Tracing the cause, we found that the training corpora for large models such as Google Gemini and ChatGPT mainly come from sources like Documenting Large Webtext Corpora, WebText2, existing books, and Wikipedia, rather than specialized male infertility medical literature [ 6 ]. Therefore, the development of specialized LLMs focusing primarily on male infertility research could greatly improve the specificity, reduce the randomness and illusion issues, and enhance the capability of general LLMs in addressing specialized queries [ 44 – 47 ]. 4.3 Conclusions This study is the first to use male infertility as an example, employing bibliometric methods, topic modeling, and hot topic Q&A with LLMs to explore the summarization capabilities of models including Gemini, GPT-3.5, and GPT-4 in the context of male infertility. The results indicate that large language models are a beneficial supplement to traditional bibliometric analysis and topic modeling. They not only summarize the frequently occurring hot topics in traditional bibliometrics on male infertility but can also address low-frequency topics that traditional methods fail to effectively summarize. However, large language models also have issues such as illusion of knowledge, randomness, and weak specialization abilities. Therefore, exploring and developing specialized large language models could help improve the general issues of knowledge illusion, randomness, and weak specialization in general-purpose models. Declarations Conflicts of Interest The authors declare no competing interests. Author Contribution B. S. conceived the project. Y. Z and J. W. designed the entire analysis workflow. J. L., J. W., and X. L. collected the data. Y. Z., R. W., X. L., H. Z., and C. Z. organized the data. Y. Z. drafted the initial manuscript. J. W., R. W., and J. H. edited the manuscript. All authors participated in the discussion of the results. All authors reviewed the manuscript. Acknowledgments Bairong Shen conceived the project. Yingbo Zhang and Jiao Wang designed the entire analysis workflow. Junyu Lu, Jiao Wang, and Xingyun Liu collected the data. Yingbo Zhang, Rongrong Wu, Xingyun Liu, Hui Zong, and Chaoying Zhan organized the data. Yingbo Zhang drafted the initial manuscript. Jiao Wang, Rongrong Wu, and Jiang Huang edited the manuscript. All authors participated in the discussion of the results. This work was supported by the National Natural Science Foundation of China (grant number 32270690) and the Sichuan Science and Technology Program (grant number 2024YFHZ0205). The authors declare that they have no conflicts of interest. Data Availability Data and code for generating the pictures in this study have been deposited in the github (https://github.com/zyb1984/Landscape_sperm) References Agarwal A, Baskaran S, Parekh N, Cho CL, Henkel R, Vij S, Arafa M, Panner Selvam MK, Shah R: Male infertility . Lancet (London, England) 2021, 397 (10271):319-333. 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Zucker J, Paneri K, Mohammad-Taheri S, Bhargava S, Kolambkar P, Bakker C, Teuton J, Hoyt CT, Oxford K, Ness R et al : Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis . IEEE transactions on big data 2021, 7 (1):25-37. Chang D, Lin E, Brandt C, Taylor RA: Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison . JMIR medical informatics 2021, 9 (11):e23101. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6000333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414015613,"identity":"b33c2244-eeaa-4fc4-80ba-d375c1d1f940","order_by":0,"name":"Yingbo Zhang","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yingbo","middleName":"","lastName":"Zhang","suffix":""},{"id":414015614,"identity":"776e8251-3899-447a-ade0-9f8cfc3ead96","order_by":1,"name":"Jiao Wang","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Wang","suffix":""},{"id":414015615,"identity":"e85fb8b6-6c1d-439e-8daf-3947a23127f4","order_by":2,"name":"Rongrong Wu","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Wu","suffix":""},{"id":414015616,"identity":"ec549a08-8808-464b-a627-481af5329ee1","order_by":3,"name":"Xingyun Liu","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xingyun","middleName":"","lastName":"Liu","suffix":""},{"id":414015617,"identity":"78ba20ed-281f-4fd0-895b-c3cfae1d40da","order_by":4,"name":"Hui Zong","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zong","suffix":""},{"id":414015618,"identity":"c71b67cd-ad70-4388-9811-855d231e4bdb","order_by":5,"name":"Junyu Lu","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Junyu","middleName":"","lastName":"Lu","suffix":""},{"id":414015619,"identity":"a1db012b-1937-49a4-b860-ac602d869776","order_by":6,"name":"Chaoying Zhan","email":"","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chaoying","middleName":"","lastName":"Zhan","suffix":""},{"id":414015620,"identity":"07daa26f-010f-45b5-9aa8-cdcdcc1f3c61","order_by":7,"name":"Jiang huang","email":"","orcid":"","institution":"The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"huang","suffix":""},{"id":414015621,"identity":"33694f4c-e8a4-45ca-ac32-24ccc069c119","order_by":8,"name":"bairong shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAwbGBghLAog/wASJ1sI4gzgtMADUwsxDjBZz9sNtHz7uqGWQn9187LHNH7vEBvbmbRIMNXdwarHsSWyeOfPMcQaDO8fSjXPbkhMbeI6VSTAce4bbYQcSm5l5244xGEjkmEnnNhxIbAAyJBgbDuPWcv4hRIv8jPxv0hZ/gFrk3xDQcgNsSw0Dw40cNmkGNpAtPPi1WM542Mw4s+0Aj8GNNDPJ3rZk4zaetGKLhGO4tZjzpz9m+NhWJyc/I/mZxI8/drL97Ic33vhQg1sLFBzmgTPZQEQCIQ0MDHWElYyCUTAKRsHIBQBgnFKMysLfiwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"bairong","middleName":"","lastName":"shen","suffix":""}],"badges":[],"createdAt":"2025-02-10 15:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6000333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6000333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76168720,"identity":"cd0493ce-806f-4d08-98ed-b53aa84c0e96","added_by":"auto","created_at":"2025-02-13 04:58:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3618734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Inclusion and Exclusion of Male Infertility-Related Scientific Publications in this Study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/067ddcb2af433d8078724a96.png"},{"id":76168729,"identity":"1c3ae976-953e-4e73-bdc3-bbf33daf9fbe","added_by":"auto","created_at":"2025-02-13 04:58:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15632446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative analysis of the number of published articles on male infertility based on annual data.\u003c/strong\u003e Figure 2A shows the annual variation of the number of article-type and review-type scientific publications on male infertility from 1970 to 2022. Figure 2B shows the annual variation of the ratio of review-type publications to article-type publications.Figure 2C and Figure 2D show the disciplinary composition of male infertility article-type publications and review-type publications respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/edcc239287200b2dd24a0ae8.png"},{"id":76168726,"identity":"8ca5380a-d604-45fe-a377-85d4ef4009bf","added_by":"auto","created_at":"2025-02-13 04:58:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8786493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the publication patterns of top scholars in male infertility based on publication trajectory and collaboration networks. \u003c/strong\u003eFigure 3A and 3B respectively display the publication trajectories of top scholars in article-type and review-type publications on male infertility.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/08232c611b66a8e053f9a178.png"},{"id":76168731,"identity":"19bf4be1-b645-4155-8e75-00d7ca6342e1","added_by":"auto","created_at":"2025-02-13 04:58:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35943037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis the National Differences and Collaboration Networks in Male Infertility Research, where \u003c/strong\u003eFigure 4A shows the national differences in male infertility publications, Notes: (1) the one article-type publication published by the Democratic Republic Germany is merged with Germany; (2) the 61 article-type publications and 15 review-type publications published by Hong Kong are combined with China; (3) The 174 article-type publications and 6 review-type publications published by Taiwan are merged with China; (4) The 4 article-type publications published by Macao are combined with China; (4) Netherlands Antilles is listed as a separate country; (5) The two article-type publications published under Russia is merged with the Russian Federation.; (6) Macedonia and North Macedonia are merged as North Macedonia.; (7) The 17 article-type publicationspublished under Yugoslavia is merged with the Serbia; (8) the one article-type publication published by Netherlands Antilles is merged with Netherlands; (9) 306 publications with incomplete national information are deleted, including 280 article-type publications and 26 review-type publications.; Figure 4B displays countries with a Top ratio of review-type publications to article-type publications (threshold being the mean ratio of review-type publications to article-type publications); Figures 4C and 4D respectively present the national collaboration network analyses for article-type publications and review-type publications in male infertility.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/f84ac318776de693de753b27.png"},{"id":76168728,"identity":"545dd89b-c41a-4143-850f-b8f6c6e3185a","added_by":"auto","created_at":"2025-02-13 04:58:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8659590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend Analysis of Male Infertility Based on Latent Dirichlet Allocation (LDA), \u003c/strong\u003ewhere Figure 5A displays the optimal number of topics (K=15) based on Griffiths2004, CaoJuan2009, Arun2010, and Deveaud2014 metrics. Figure 5B to 5P depict the topics and their keyword frequencies derived from the LDA analysis. Figure 5Q illustrates the temporal distribution and changing patterns of different topics over time.\u003c/p\u003e\n\u003cp\u003eNote: In this study, the \u003cstrong\u003equanteda\u003c/strong\u003e package was used for stemming and lexical pruning to avoid the influence of tense or plural forms on the results. However, this method has a drawback: some English words are over-stemmed, resulting in non-standard forms, such as \"analysis\" being stemmed to \"analysi.\"\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/351645c5266e77cb7d4ff9a1.png"},{"id":76674820,"identity":"fa806790-a188-4d89-ad08-c6ada9d15a71","added_by":"auto","created_at":"2025-02-19 14:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":69902048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6000333/v1/1a51032c-2dff-46c8-b781-e9f8ad634e48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Male Infertility Over the Past 50 Years: Landscape Analysis and the Emerging Role of Large Language Models","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent years, due to the deterioration of the global environment and intensified local pollution, the incidence of male infertility has sharply increased, becoming a global public health issue affecting approximately 20% of couples with a desire to conceive[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared to female infertility, the etiology and pathogenesis of male infertility remain not fully clear due to the complexity of sperm production[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Biomedical literature has been a valuable resource for mining medical information and clinical applications[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With the advancements and widespread application of high-throughput sequencing technologies, such as exome sequencing, genome sequencing, transcriptome sequencing, proteome sequencing, and methylation sequencing, these techniques have been extensively utilized in the research of male infertility and the identification of disease biomarkers, significantly enhancing the knowledge of male infertility and enriching related medical literature[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, mining data resources in existing medical literature not only holds significant value for constructing broadly applied knowledge bases, knowledge graphs, chatbots, and clinical decision support systems, but also plays a crucial role in understanding the research limitations and future development trends of male infertility.\u003c/p\u003e \u003cp\u003eExploring the application of artificial intelligence in medical decision support systems has been one of the key pathways for its clinical translation. In 2022, within the field of artificial intelligence, ChatGPT 3.5 emerged as a landmark large language model (LLMs), trained on over 175\u0026nbsp;billion parameters. The corpus of this model not only originated from large text corpora but also included WebText2, Wikipedia, and a vast array of book materials [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The advent of ChatGPT 3.5 has had profound implications for multiple medical disciplines, such as pharmacology, radiology, dentistry, and otolaryngology. Some scholars have even suggested that ChatGPT might change the current paradigms of medical education and practice, potentially leading to the unemployment of many doctors[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, there are also views that, as a Chat Generative Pre-trained Transformer model, all of ChatGPT\u0026rsquo;s understandings and inferences are fundamental. It is primarily used for querying and deducing basic knowledge and is unlikely to offer innovative insights. In certain specialized domains, its performance may not even surpass existing artificial intelligence models[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo gain a deeper understanding of LLMs's integration and information enhancement capabilities in reproductive medicine literature, particularly in the field of male infertility, this study selected the Scopus database as the primary data source. We utilized three core terms associated with male infertility -\"Azoospermia\", \"Oligospermia\", \"Asthenospermia\", \"Teratozoospermia\", and \"Teratospermia\" - as thematic search keywords, and employed bibliometrics and topic modeling as research methods, along with Gemini' and ChatGPT's question-answer feature as the main investigative approach. Initially, we explored the publication volume, publishing institutions, countries, and their collaboration networks, top scholars and their co-occurrence networks, and hot topics in the realm of male infertility based on scientific citation data (real world) as the data source. Subsequently, using Gemini and ChatGPT, we examined its capabilities in summarizing research hotspots in male infertility (virtual world). Finally, through comparative analysis, we investigated the information enhancement ability of LLMs in literature hotspot summarization and trend assessment.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature Retrieval Strategy and Basic Data Collection\u003c/h2\u003e \u003cp\u003eThe Scopus database, owned by Elsevier Publishing Group, is a comprehensive abstract and citation database and one of the world's largest biomedical literature databases. Its inclusion of strictly peer-reviewed literature has garnered widespread attention in the industry. To systematically collect literature related to male infertility, we used high-frequency terms in the field of male infertility research - \"Azoospermia\", \"Oligospermia\", \"Asthenospermia\", \"Teratozoospermia\", or \"Teratospermia\" - as search keywords in the Scopus database, selecting related publications where these keywords appeared in the titles or abstracts. Considering the advent of PCR technology in the 1980s [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the inclusion period for the literature was set from January 1, 1970, to December 31, 2022. Additionally, given the study's focus on English-language literature, we excluded articles categorized as communications, comments, or errata, considering only publications in English. Additionally, this study focused on scientific publications with human subjects. Therefore, only those publications that specified \"human\", \"humans\", or \"humankind\" as the research subjects were included.\u003c/p\u003e \u003cp\u003eAfter filtering out irrelevant literature records, we categorized the retrieved male infertility-related records into review-type records and article-type records. The data for both categories were then exported as complete records in '*.BibTex' format [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These records comprised basic information, including authors, titles, DOI numbers, keywords, citation counts, publication years, journal sources, volumes, and issues. Additionally, they contained detailed information such as abstracts, author keywords, affiliations of the first author, and the corresponding author's institution[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile exporting citation records, we also exported preliminary analysis data from the Scopus database, including the annual number of publications, the distribution of publication subjects, the number of publications from academic institutions, and the number of publications from journal sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Bibliometric Analysis\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of the primary hotspots and developmental trends in male infertility research, we initially converted the research-type literature records into '*.BibTex' format into data frames using the bibliometrix package. We initially utilized the extensive bibliometric functions provided by the bibliometrix package to analyze annual publication volumes, annual citation counts, scholars' H-index, scholars' highest citation counts, total number of male infertility publications by scholars, total number of male infertility publications by academic institutions, total number of male infertility publications by countries, total number of male infertility publications by journals, and the frequency of international collaborations among other indicators [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Subsequently, we employed ranking methods to identify the top 10 publishing institutions, top 10 publishing journals, top 10 authors, and the annual changes in literature output of the top 10 authors. For further computational details such as the H-index and average citation count, we applied standard calculation methods. The H-index is calculated using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h=max\\left\\{k:{p}_{k}\\ge\\:k\\right\\}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{1},{p}_{2},\\cdots\\:,{p}_{k},\\cdots\\:,{p}_{n}\\:\\)\u003c/span\u003e\u003c/span\u003eare a researcher's list of papers, and these papers are sorted in descending order of their citation counts, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{1}\\)\u003c/span\u003e\u003c/span\u003e being the most cited paper and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{n}\\)\u003c/span\u003e\u003c/span\u003e the least cited paper. In this study, scholars' H-index was directly calculated using the Hindex function provided by the bibliometrix package. For the number of average citation (AC), we calculated it using the standard method, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{A}\\text{v}\\text{e}\\text{r}\\text{a}\\text{g}\\text{e}\\:\\text{c}\\text{i}\\text{t}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\left(AC\\right)=\\frac{TC}{NP}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:TC\\)\u003c/span\u003e\u003c/span\u003e represents the total number of citations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NP\\)\u003c/span\u003e\u003c/span\u003e represents the total number of publications.\u003c/p\u003e \u003cp\u003eFurthermore, to assess the contribution of journals related to male infertility to the development in the fields of biology and medicine, we calculated the contribution of each relevant journal based on the Influence Score. The specific calculation formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:{Influence\\:Score}_{Journal}={JCI}_{Journal}*{N}_{record\\:}\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{JCI}_{Journal}\\)\u003c/span\u003e\u003c/span\u003e​ represents the Journal Citation Indicator of the journal, indicating the average CNCI (Category Normalized Citation Impact) value for articles published by the journal over the past three years, with data sourced from the Clarivate database[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{record\\:}\\)\u003c/span\u003e\u003c/span\u003e​is derived from the total number of publication records of that journal in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Summary of Male Infertility Research Hotspots from Topic Modeling Analysis\u003c/h2\u003e \u003cp\u003eTopic modeling analysis is a commonly used method for exploring research hotspots and trends in academic disciplines. To investigate and interpret the research hotspots and trends in male infertility[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], we attempted to use Latent Dirichlet Allocation (LDA) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] as our research methodology. In this study, we used abstracts from the Scopus database, which contain keywords such as \"Azoospermia\", \"Oligospermia\", \"Asthenospermia\", \"Teratozoospermia\", and \"Teratospermia\" in the titles and abstracts of male infertility research publications, as our data source. LDA was employed as the topic modeling method to explore a summary of hotspots in male infertility research based on topic modeling. For constructing the corpus, we first excluded numbers, punctuation marks, conjunctions, English stop words, 1% of rare vocabulary, and words with a frequency over 99%, and then built the corpus with the quanteda package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], then we used the dfm function from the quanteda package to convert the corpus into a document matrix, incorporating metadata such as publication year, titles, and others. Furthermore, we employ the LDA topic modeling based on Gibbs sampling described by Ponweiser et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, our method differs from the one described in the original literature in the choice of the optimal number of topics. In this study, we attempt to select the optimal number of topics using the metrics in the ldatuning package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: the Griffiths2004 index [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], CaoJuan2009 index [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Arun2010 index [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and Deveaud2014 index [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, to avoid difficulties in effectively visualizing and summarizing temporal trends due to excessively large numbers of topics, we specifically set the range of potential optimal topics between 2 to 15. We use a gradient ascent method (with a step size of 1) to select the optimal number of topics, aiming for the smallest values of the Griffiths2004 index and CaoJuan2009 index, and the largest values of the Arun2010 index and Deveaud2014 index as the criteria for the best topic number. Subsequently, with the optimal number of topics determined, we performed LDA topic modeling using the Gibbs sampling method. The parameters were set according to the defaults in the topicmodels package, with the number of iterations set to 2000, sampling times to 10000, and testing frequency every 500 iterations. To ensure the accuracy of the posterior results, we discarded the first 4000 samples [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of Male Infertility Research Hotspots Enhanced by ChatGPT\u003c/h2\u003e \u003cp\u003eTo explore the augmentative effects of LLMs in the analysis of male infertility literature, as well as to compare the differences in themes and hotspots in male infertility research based on real-world (literature) data and virtual-world (LLMs ) data, we initiated our study with the query prompt \"Could you list the current hotspots in male infertility research? \". We entered this prompt into various language models, including Gemini, GPT-3.5, GPT-4, and GPT-4o. Subsequently, we extracted the hotspots and their descriptions related to male infertility research from the responses provided by ChatGPT \u003csup\u003e[25]\u003c/sup\u003e or Gemini [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The precise dates of data extraction for these hotspots were April 10, 2024, and December 10, 2023. Next, we compared the overlap between the hotspots derived from literature data and those provided by LLMs to assess the additive benefits of large language models in analyzing themes and hotspots within male infertility research. To ensure the reliability of our analysis, we repeated the query three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Statistics and Visualization\u003c/h2\u003e \u003cp\u003eThe aforementioned data analysis and visualization have been completed in the R environment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We used the bibliometrix package to process '*.BibTex' format data and conduct basic bibliometric analyses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which included statistics such as annual publication volume, annual citation counts, top 10 publishing institutions, top 10 publishing journals, top 10 authors ranked by H-index, and annual changes in literature output of the top 10 authors, as well as analyses of international collaboration networks. For topic modeling, the Dirichlet distribution method from the topicmodels package was employed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the process of data preprocessing and corpus construction, packages such as dplyr [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], quanteda [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and ldatuning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] were used. The visualization of results primarily relied on the ggplot2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and sf [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] packages. Subsequent graphic enhancements have been made using the Inkscape tool, with world map data sourced from the open data of the Natural Earth network.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Analysis of Male Infertility Research Hotspots and Trends Based on Bibliometric Evidence\u003c/h2\u003e\n\u003cp\u003eAs of December 31, 2022, we have retrieved a total of 21,263 scientific publications records from the Scopus database that contained \u0026apos;Azoospermia\u0026apos;, \u0026apos;Oligospermia\u0026apos;, \u0026apos;Asthenospermia\u0026apos;, \u0026apos;Teratozoospermia\u0026apos;, or \u0026apos;Teratospermia\u0026apos; in their titles or abstracts. After excluding non-timeline citations, non-manuscript-type records, non-English language records, and records not focusing on humans as research subjects, we ultimately included 14,852 scientific publications related to male infertility. This collection comprised 12,884 article-type publications and 1,968 review-type publications. For more details on the publications, refer to Figure 1.\u003c/p\u003e\n\u003ch3\u003e3.1.1 Bibliometric Analysis of Annual Publication Numbers in Male Infertility Research\u003c/h3\u003e\n\u003cp\u003eAccording to the number of scientific publications on male infertility, the number has increased from 10 per year in 1970 (including 10 article-type publications and 0 review-type publications) to 792 per year in 2022 (including 665 article-type publications and 127 review-type publications). The average annual growth rate of publications is 8.77%, with article-type publications growing at an average annual rate of 8.40%, and review-type publications at a rate of 9.03% (Figure 2A). The analysis of the annual growth rate of publications reveals that article-type publications have experienced three near-exponential growth periods: in the 1970s to 1980s (1970-1980), the 1990s (1990-2000), and around 2020 (2015-present). These growth periods are speculated to correspond to three major technological revolutions in nucleic acid testing: the advent of PCR testing technology[10, 11], the development of Next-generation sequencing (NGS) technology[32], and the development of single-cell sequencing technology[33, 34] (Figure 2A). Review-type publications, on the other hand, show only two near-exponential growth periods, occurring from the 1990s to the early 21st century (1990-2006) and around 2020 (2017-present). This growth is presumed to be due to an increase in systematic summaries and theoretical generalizations triggered by a large increase in article-type publications related to male infertility (Figure 2A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing the ratio of review-type publications to article-type publications, it is found that there was a rapid growth period during the 1990s to the early 21st century (1990-2002) and around 2020 (2017-present), peaking in 2021 (Figure 2B). This peak is speculated to be related to the advent and development of PCR testing technology[10, 11] and the maturation of Next-generation sequencing (NGS) technology[32], marking the maturation and perfection of the first layer of basic theories in male infertility scientific literature, inseparably linked to the development and maturation of these technologies. Undoubtedly, the recently emerged single-cell omics technology is an effective method for understanding the basic theories and mechanisms of male infertility, which will trigger a new wave of theoretical summaries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn analysis of the disciplinary composition of male infertility research shows that article-type publications on male infertility have primarily been published in the fields of medicine (60.6%), biochemistry and molecular biology (28.3%), and pharmacology (2.3%). Other subjects such as agriculture, veterinary, chemical engineering, nursing, etc., only published a total of 8.80% (1134 publications) publications (Figure 2C). Similarly, review-type publications on male infertility have also been predominantly published in disciplines like Medicine, Biochemistry and Molecular Biology, and Pharmacology (Figure 2D). However, compared to article-type publications, the proportion of review-type publications published in the fields of medicine, biochemistry and molecular biology, and pharmacology. But compared with article-type publications, review-type publications published in the fields of Medicine are slightly higher (Figure 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn analysis of the disciplinary composition of male infertility research shows that article-type publications on male infertility have primarily been published in the fields of medicine (60.6%), biochemistry and molecular biology (28.3%), and pharmacology (2.3%). Other subjects such as agriculture, veterinary, chemical engineering, nursing, etc., only account for a total of 8.80% (1134 publications) (Figure 2C). Similarly, review-type publications on male infertility have also been predominantly published in disciplines like medicine, biochemistry and molecular biology, and pharmacology (Figure 2D). However, compared to article-type publications, the proportion of review-type publications published in the fields of medicine are slightly higher (Figure 2D).\u003c/p\u003e\n\u003ch3\u003e3.1.2 Analysis of Top Scholars in Male Infertility and Their Collaboration Networks\u003c/h3\u003e\n\u003cp\u003eAssessment of author influence and publication trends is crucial for understanding an author\u0026apos;s impact in their field and identifying top (top 10) scholars. Analysis of data on top scholars in male infertility publications reveals that researchers from the United States, Italy, and Belgium are leading in this area. In article-type publications, Italy (with three top authors, accounting for 30.00% of the total top 10), Belgium (also with three top authors, making up another 30.00% of the top 10), and the United States (with two top authors, comprising 20.00% of the top 10) dominate the field. Other countries like Germany and China have only one author each on the list. In review-type publications, Italian authors lead significantly with five (50.00% of the total top 10), followed by the United States (three authors, 30.00%), Brazil (one author, 10.00%), and Belgium (one top author, 10.00%).Assessing author influence and publication trends is crucial for exploring an author\u0026apos;s disciplinary impact and identifying top scholars (top 10). An analysis of the top scholars (top 10) in male infertility publications reveals that researchers from the United States, Italy, and Belgium hold leadership positions in the field. For instance, in article-type publications, Italy (with three top authors, accounting for 30.00% of the top 10 total), Belgium (also three top authors, making up 30.00% of the top 10), and the United States (two top authors, representing 20.00% of the top 10) dominate, while other countries like Germany and China have only one author making the list. In review-type publications, Italian authors lead significantly with five (50.00% of the top 10), followed by the United States (three authors, 30.00% of the top 10), Brazil (one author, 10.00%), and Belgium (one top author, 10.00%)(Table 1).\u003c/p\u003e\n\u003cp\u003eAn interesting observation in the data for both article and review-type top 10 scholars is that four authors rank in the top 10 for both types of publications. These are Schlegel P. N. from Weill Cornell Medicine, Cornell University, USA; Ferlin A. from the University of Padova, Italy; Foresta C. also from the University of Padova, Italy; and Tournaye H. from Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Belgium (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Top Scholars in Male Infertility Based on H-index Influence Scores\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffiliation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH-index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePY_start\u003csup\u003ea)\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;b)\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;c)\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003csup\u003e\u0026nbsp;d)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 558px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArticle-type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eSchlegel P. N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eWeill Cornell Medicine, Cornell University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e89.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNieschlag E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity Hospitals M\u0026uuml;nster, University of M\u0026uuml;nster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e7478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e67.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTournaye H.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversitair Ziekenhuis Brussel, Vrije Universiteit Brussel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e106.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eForesta C.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Padova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e57.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eDevroey P.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversitair Ziekenhuis Brussel, Vrije Universiteit Brussel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e198.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eLipshultz L. I.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBaylor College of Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e71.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFerlin A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Padova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e63.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eVan Steirteghem A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversitair Ziekenhuis Brussel, Vrije Universiteit Brussel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e96.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eGarolla A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Padova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e63.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eWang X.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eState Key Laboratory of Reproductive Medicine, Nanjing Medical University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e24.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 558px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReview-type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eAgarwal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eCleveland Clinic Lerner College of Medicine, Case Western Reserve University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e3326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e85.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eEsteves S. C.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversidade Estadual de Campinas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e65.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eKrausz C.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Florence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e3774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e157.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eSchlegel P. N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eWeill Cornell Medicine, Cornell University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e41.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eFerlin A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Padova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e115.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eForesta C.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Padova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e107.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eTournaye H.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversitair Ziekenhuis Brussel, Vrije Universiteit Brussel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e1263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e63.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eCalogero A. E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Catania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e33.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eRamasamy R.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLeonard M. Miller School of Medicine, University of Miami\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003eCondorelli R. A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUniversity of Catania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e50.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePY_start\u003csup\u003ea)\u003c/sup\u003e represents the year in which publications on male infertility were first published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC\u003csup\u003e\u0026nbsp;b)\u003c/sup\u003e represents the total number of citations for publications (either articles or reviews) on male infertility by a particular scholar.\u003c/p\u003e\n\u003cp\u003eNP\u003csup\u003e\u0026nbsp;c)\u0026nbsp;\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;represents the total number of publications (either articles or reviews) by a particular scholar in the field of male infertility research.\u003c/p\u003e\n\u003cp\u003eAC \u003csup\u003ed)\u003c/sup\u003e represents the average number of citations per publication (either articles or reviews) on male infertility by a particular scholar. The calculation formula is TC/NP.\u003c/p\u003e\n\u003cp\u003eIn article-type publications on male infertility, Schlegel P. N. from Weill Cornell Medicine (Cornell University, USA) ranks first with an H-index of 54. His earliest publication dates back to 1993, and over the past 30 years, he has published 105 article-type publications with a total of 9401 citations, averaging 89.53 citations per article (Table 1). An analysis of Schlegel P. N.\u0026apos;s publication trajectory shows his peak publishing years were between 1995 and 2015, averaging 5-10 articles per year with about 75 citations annually (Figure 3A). Schlegel P. N. is also a top 10 author in review-type publications, ranking fourth, with his earliest review publication dating back to 1997. Over nearly 30 years, he has published 30 review-type articles, totaling 1252 citations, averaging 41.73 citations per article (Table 1), and his reviews are evenly distributed over the 1997-2022 period (Figure 3B).\u003c/p\u003e\n\u003cp\u003eThe author with the highest number of article-type publications is from University Hospitals M\u0026uuml;nster (University of M\u0026uuml;nster, Germany), publishing 110 articles over more than 40 years (1978-2022), with a total of 7478 citations, averaging 67.98 citations per article (Table 1). His publication trajectory shows a steady output of review-type publications spread across the 1978-2022 period (Figure 3A).\u003c/p\u003e\n\u003cp\u003eAdditionally, Devroey P. from Universitair Ziekenhuis Brussel (Vrije Universiteit Brussel, Belgium) has the highest citation count for article-type publications among male infertility researchers, with an average citation rate of 198.74 per article, publishing 50 papers over 37 years (1986-2022) with a total of 9937 citations.\u003c/p\u003e\n\u003cp\u003eIn review-type publications, Agarwal A. from Cleveland Clinic Lerner College of Medicine (Case Western Reserve University, USA) ranks first with an H-index of 27, publishing 39 review-type articles over 18 years with a total of 3326 citations, averaging 85.28 citations per article (Table 1). A trajectory analysis of Agarwal A.\u0026apos;s reviews shows a significant increase in both the number of publications and citations since 2021, reaching up to 6 articles per year with over 100 citations annually (Figure 3B).\u003c/p\u003e\n\u003cp\u003eThe second highest H-index among top 10 review-type authors is Esteves S. C. from Universidade Estadual de Campinas (Brazil), who has published 25 review-type articles over 12 years (2011-2022) with a total of 1645 citations, averaging 65.80 citations per article (Table 1).\u003c/p\u003e\n\u003cp\u003eThe highest total and average citation counts among male infertility review-type authors belong to Krausz C. from the University of Florence (Italy), who has published 24 review-type articles over 24 years (1999-2022) with a total of 3774 citations, averaging 157.25 citations per article (Table 1). A study of the publication trajectories of Esteves S. C. and Krausz C. reveals an annual publication rate of 1-4 review articles, with an average annual citation rate of about 50 (Figure 3B).\u003c/p\u003e\n\u003ch3\u003e3.1.3 Exploring the National Differences and Collaboration Networks in Male Infertility Research\u003c/h3\u003e\n\u003cp\u003eIn the study of the nationalities of publications, it has been found that the United States leads with 2989 publications (including 2249 article-type publications and 740 review-type publications), while China ranks second with 1898 publications (comprising 1766 article-type publications and 132 review-type publications). In addition, Italy, the United Kingdom, Germany, India, Japan, France, Iran, and Turkey are significant publishing countries in the field of male infertility (Figure 4A). To further quantify the ability of countries to systematically review and summarize in male infertility, we used the mean ratio of review-type to article-type publications as a measure. The results show that 23 countries, including Iceland, Qatar, New Zealand, and the United States, exceed the average ratio of review-type to article-type publications. In contrast, traditional publishing powerhouses like China, India, Japan, France, Iran, and Turkey have ratios significantly below the average (Figure 4B).\u003c/p\u003e\n\u003cp\u003eAn analysis of the publication collaboration networks for article-type and review-type publications has revealed that, with a few exceptions like Trinidad and Tobago, Malta, and Albania, most of the world collaborates with each other, forming a complex and intricately interconnected collaboration network (Figures 4C and 4D). However, a comparison between the collaboration networks of article-type and review-type publications shows distinct differences in the main collaborating countries in male infertility research. For instance, in research publications, China is a major collaborator with the United States (Figure 4C), but in review publications, Canada, Italy, the United Kingdom, and Brazil emerge as primary collaborators, with China becoming a secondary partner.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the study of the top 10 research institutions by publication volume in research publications (totaling 1350 publications, accounting for 10.48% of the total male infertility publications), we have found that they belong to eight different countries, with European and American institutions predominating. Inserm in France leads with a total of 183 publications, followed by Nanjing Medical University (China) and Royan Institute (Iranian) with 165 and 145 publications, respectively (Table 2). Regarding the top 10 institutions in review paper publication volume (totaling 319 publications, accounting for 16.21% of the total male infertility publications), we have discovered that the majority are from the United States (5 research units or universities, making up 50.00%), with renowned institutions including Weill Cornell Medicine (53 publications), Cleveland Clinic Foundation (51 publications), Baylor College of Medicine (39 publications), Harvard Medical School (24 publications), and University of Washington (23 publications). Italy also has two institutions in the top 10, which are Sapienza Universit\u0026agrave; di Roma (35 publications) and Universit\u0026agrave; degli Studi di Firenze (31 publications).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Top 10 Academic Institutions Ranked by Total Publication number related to Male Infertility\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Institutions\u003csup\u003ea)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of Publications\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 555px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArticle-type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eInserm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eNanjing Medical University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eRoyan Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eIranian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eCairo University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eWeill Cornell Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eSapienza Universit\u0026agrave; di Roma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eUniversity of M\u0026uuml;nster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eTel Aviv University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eShanghai Jiao Tong University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eTehran University of Medical Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 555px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReview-type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eWeill Cornell Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eCleveland Clinic Foundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eBaylor College of Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eSapienza Universit\u0026agrave; di Roma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eUniversit\u0026agrave; degli Studi di Firenze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eHarvard Medical School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eUniversity of Washington\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eImperial College London\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eCentrum voor Reproductieve Geneeskunde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 260px;\"\u003e\n \u003cp\u003eUniversity of Toronto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea)\u003c/sup\u003e\u003c/strong\u003e In the top 10 ranking, we have excluded two administrative institutions: the Ministry of Education of the People\u0026apos;s Republic of China (191 publications) and the Iranian Academic Center for Education, Culture and Research (167 publications).\u003c/p\u003e\n\u003ch3\u003e3.1.4 Analysis of Male Infertility Journals\u0026apos; Influence Based on Publication Numbers and Influence Scores\u003c/h3\u003e\n\u003cp\u003eExploring the field influence and bias of journals is a crucial application in clarifying their academic domains. An analysis of the total number of Article-type publications and the disciplinary impact in male infertility journals has revealed that \u003cem\u003eFertility and Sterility\u003c/em\u003e, \u003cem\u003eAndrologia\u003c/em\u003e, \u003cem\u003eHuman Reproduction\u003c/em\u003e, and \u003cem\u003eJournal of Assisted Reproduction and Genetics\u003c/em\u003e are the leading journals in terms of publication volume in male infertility research, with respective total publications of 1106 (accounting for 8.58% of the total Article-type publications), 822 (6.38%), 731 (5.67%), and 322 (2.50%) (Table 3). However, when measured by impact factor, 5-year impact factor, and Journal Citation Indicator, \u003cem\u003eHuman Reproduction\u003c/em\u003e, \u003cem\u003eFertility and Sterility\u003c/em\u003e, \u003cem\u003eJournal of Urology\u003c/em\u003e, and \u003cem\u003eAndrology\u003c/em\u003e emerge as the most significant journals in male infertility (Table 3). Combining both publication numbers and journal impact, \u003cem\u003eFertility and Sterility\u003c/em\u003e, \u003cem\u003eHuman Reproduction\u003c/em\u003e, \u003cem\u003eAndrologia\u003c/em\u003e, and \u003cem\u003eJournal of Urology\u003c/em\u003e rank as the most important journals in male infertility, with influence scores of 2422.14, 1637.44, 706.92, and 537.66, respectively (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Analysis of Male Infertility Journals\u0026apos; Influence Based on Publication Numbers and Influence Scores\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Number of Articles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal Impact index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluence Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIF\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;a)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-Year IF\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003eb)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJCI\u003csup\u003ec)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArticle-type\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;publications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eFertility And Sterility\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2422.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eAndrologia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e706.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Reproduction\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1637.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eJournal of Assisted Reproduction and Genetics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e299.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eInternational Journal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eJournal of Urology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e537.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eJournal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eAsian Journal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e228.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eAndrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e307.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eUrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e139.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReview-type publications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eAsian Journal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e58.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eUrologic Clinics of North America\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e34.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eTranslational Andrology and Urology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e23.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eFertility And Sterility\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e72.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Reproduction Update\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e13.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e104.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Reproduction\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e56.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e17.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eInternational Journal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eNature Reviews Urology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e58.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eJournal of Andrology\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003eImpact Factor（IF）measures the average number of citations received by articles published in a journal over the previous two years., with data sourced from the Clarivate database.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb)\u003c/sup\u003e5-Year IF(5-Year Impact Factor) measures the average number of citations received by articles published in a journal over the past five years., with data sourced from the Clarivate database.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec)\u003c/sup\u003eThe Journal Citation Indicator (JCI) quantifies the average citation impact of articles in a journal over the three-year period, with data sourced from the Clarivate database.\u003c/p\u003e\n\u003cp\u003eCompared to article-type publications, review-type publications are relatively fewer in number and more dispersed across journals. For instance, \u003cem\u003eAsian Journal of Andrology\u003c/em\u003e has published 56 Review-type articles on male infertility in the last 50 years (1974-2022), accounting for only 2.85% of the total Review-type articles. Other fertility-related journals such as \u003cem\u003eUrologic Clinics of North America\u003c/em\u003e, \u003cem\u003eTranslational Andrology and Urology\u003c/em\u003e, and \u003cem\u003eFertility and Sterility\u003c/em\u003e have published even fewer articles. However, in terms of journal impact measured by the impact factor, 5-year impact factor, and Journal Citation Indicator, review-focused journals with significant influence in the field of male infertility, such as \u003cem\u003eHuman Reproduction Update\u003c/em\u003e and \u003cem\u003eNature Reviews Urology\u003c/em\u003e, have considerably higher impact scores compared to Article-type journals like \u003cem\u003eHuman Reproduction\u003c/em\u003e, \u003cem\u003eFertility and Sterility\u003c/em\u003e, \u003cem\u003eJournal of Urology\u003c/em\u003e, and \u003cem\u003eAndrology\u003c/em\u003e (Table 3). Considering both publication numbers and journal impact, \u003cem\u003eHuman Reproduction Update\u003c/em\u003e, \u003cem\u003eFertility and Sterility\u003c/em\u003e, \u003cem\u003eNature Reviews Urology\u003c/em\u003e, \u003cem\u003eAsian Journal of Andrology\u003c/em\u003e, and \u003cem\u003eHuman Reproduction\u003c/em\u003e emerge as the most important journals in the field of male infertility reviews, with influence scores of 104.10, 72.27, 58.74, 58.24, and 56.00, respectively (Table 3).\u003c/p\u003e\n\u003ch2\u003e3.2 Determining Research Hotspots in Male Infertility Using LDA Topic Modeling Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTopic modeling analysis based on LDA (Latent Dirichlet Allocation) is the most commonly used method for studying the distribution of research hotspots and their temporal changes based on literature evidence (in the real world). However, one challenge in topic modeling is choosing the most appropriate (or optimal) number of topics. In this study, to avoid the difficulties in effective visualization and summarizing temporal trends associated with an overly large number of topics, we set the potential optimal number of topics to range from 2 to 15. We used indices such as Griffiths2004\u0026nbsp;[21], CaoJuan2009 [22], Arun2010 [23], and Deveaud2014 [24] as selection criteria. When set to k=15, the indices for Griffiths2004 [21], CaoJuan2009 [22], Arun2010 [23], and Deveaud2014 [24] were all optimal (Figure 5A).\u003c/p\u003e\n\u003cp\u003eA frequency analysis of keywords under the optimal number of topics (k=15) revealed that different topics have different keyword frequencies. For example, Topic 1, with keywords including \u0026quot;patient\u0026quot;, \u0026quot;treatment\u0026quot;, \u0026quot;age\u0026quot;, \u0026quot;cancer\u0026quot;, etc., indicates that the main research direction of this topic is the treatment of patients with conditions like \u0026quot;age\u0026quot;, \u0026quot;cancer\u0026quot;, etc. (Figure 5B). Other topics also show clear patterns of keyword variation. For instance, Topic 5, with keywords such as \u0026quot;Express\u0026quot;, \u0026quot;Human\u0026quot;, \u0026quot;Cell\u0026quot;, \u0026quot;Protein\u0026quot;, \u0026quot;Gene\u0026quot;, etc., indicates that the main research direction is exploring the expression of different genes or proteins in human cells (Figure 5F). Topic 8, with keywords including \u0026quot;Chromosome\u0026quot;, \u0026quot;Delete\u0026quot;, \u0026quot;Microdelete\u0026quot;, \u0026quot;Y\u0026quot;, \u0026quot;\u003cem\u003eAZF\u003c/em\u003ec\u0026quot;, etc., indicates that the main focus is on exploring the effects of chromosomal variations, microdeletions, and \u003cem\u003eAZF\u003c/em\u003ec deletions or microdeletions on male infertility (Figure 5I).\u003c/p\u003e\n\u003cp\u003eObservations of the temporal patterns of topics related to male infertility revealed that different subjects experienced distinct temporal changes (Figure 5Q). For instance, topics 3 and 12 exhibited an initial increase followed by a decrease in trend, peaking in the mid-1970s and mid-1980s respectively, but their proportion of publications gradually declined entering the 1990s (Figure 5Q). Analysis of the word cloud for these topics showed that topic 3 includes keywords such as \u0026quot;sperm\u0026quot;, \u0026quot;pregnanc*\u0026quot;, \u0026quot;motil*\u0026quot;, \u0026quot;rate\u0026quot;, \u0026quot;fertil*\u0026quot;, suggesting that this topic mainly focuses on exploring the impact of sperm motility rate on fertility outcomes (Figure 5D). Topic 12 includes keywords like \u0026quot;stud*\u0026quot;, \u0026quot;use\u0026quot;, \u0026quot;method\u0026quot;, \u0026quot;reproduct*\u0026quot;, \u0026quot;clinic*\u0026quot;, \u0026quot;data\u0026quot;, indicating a focus on exploring clinical data-driven methods in male reproductive medicine (Figure 5M).\u003c/p\u003e\n\u003cp\u003eUnlike topics 3 and 12, topics 2 and 10 displayed a steadily increasing trend over the years (Figure 5Q). Topic 2 includes keywords such as \u0026quot;sperm\u0026quot;, \u0026quot;spermatzoa\u0026quot;, \u0026quot;motil*\u0026quot;, \u0026quot;morpholog\u0026quot;, \u0026quot;sampl*\u0026quot;, \u0026quot;DNA\u0026quot;, suggesting that it focuses on exploring characteristics of sperm DNA, such as mutations and diversity, and their correlation with sperm morphology or activity (Figure 5C). Topic 10 includes keywords like \u0026quot;testicular\u0026quot;, \u0026quot;biopsi*\u0026quot;, \u0026quot;cell\u0026quot;, \u0026quot;spermatogenesi*\u0026quot;, \u0026quot;spermatid\u0026quot;, focusing on the anatomical dissection of the testes or investigation of testicular cell composition, such as spermatogonia, spermatocytes, and spermatids, which has seen rapid development entering the 21st century (Figure 5K).\u003c/p\u003e\n\u003cp\u003eLong-term, the anatomical dissection and cellular composition investigation of the testes have been vital for exploring the pathological causes of male infertility [35, 36] or for investigating regenerative stem cell therapies [35, 37]. Recently, with advancements including single-cell genomic technologies [33, 34], a newer and more comprehensive understanding of the various cellular compositions in the testes, especially differentiating spermatogonial stem cells, has emerged. The development of these technologies and the discovery of new findings offer fresh perspectives and insights into investigating testicular cell composition or exploring regenerative stem cell-based treatments.\u003c/p\u003e\n\u003ch2\u003e3.3 Analysis of Male Infertility Research Hotspots Enhanced by ChatGPT\u003c/h2\u003e\n\u003cp\u003eLLMs are super tools in the field of artificial intelligence, proven to have exceptional capabilities for summarizing trends. To explore the benefits of large language models in male infertility research, we attempted to investigate the enhancement effects of GPT-3.5, GPT-4, GPT-4o, and Gemine in hot topics of male infertility research through a question-and-answer format. The results show that LLMs total collectively addressed 24 hotspots in male infertility research, covering risk factors such as \u0026quot;Environmental Exposures\u0026quot;, \u0026quot;Psychological and Social Aspects\u0026quot;, \u0026quot;Genetics\u0026quot;, \u0026quot;Immunological Factors\u0026quot;, \u0026quot;Hormonal Imbalances\u0026quot; and their impacts on sperm count, quality, and male infertility (Table 4). Regarding the treatment and methods of male infertility, LLMs responded to topics such as \u0026quot;Assisted Reproductive Technologies (ART)\u0026quot;, \u0026quot;Stem Cell Research\u0026quot;, \u0026quot;Socioeconomic disparities\u0026quot;, \u0026quot;Male Fertility Preservation\u0026quot;, which are hotspots for the treatment and prevention of male infertility (Table 4). Additionally, concerning future directions in male infertility research, LLMs also mentioned the potential of \u0026quot;Precision Medicine\u0026quot; and \u0026quot;Artificial Intelligence (AI) \u0026quot; in male infertility research (Table 4).\u003c/p\u003e\n\u003cp\u003eFor the frequency statistics of responses to these topics, only 25.00% of the topics were recommended by a unique large language model (LLM). These topics include \u0026ldquo;Molecular and Cellular Pathways\u0026rdquo;, \u0026quot;Diet and Nutrition\u0026quot;, \u0026quot;Omics Approaches\u0026quot;, \u0026quot;Regenerative Medicine\u0026quot;,\u0026quot;Male Birth Control\u0026quot;, and \u0026ldquo;Artificial Intelligence (AI)\u0026rdquo;, and \u0026ldquo;Precision Medicine in Male Infertility\u0026rdquo;. Specifically, \u0026quot;Diet and Nutrition\u0026quot;, \u0026quot;Omics Approaches\u0026quot; and \u0026quot;Regenerative Medicine\u0026quot; were only mentioned by GPT-4o, while \u0026quot;Male Birth Control\u0026quot;, \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;Precision Medicine\u0026quot; were exclusively mentioned by GPT-4 (Table 4). If we further consider that GPT-3.5, GPT-4 and GPT-4o are just different versions of ChatGPT, the number of topics uniquely recommended by a single LLM rises to 45.83%. In addition to \u0026quot;Socioeconomic disparities\u0026quot;, \u0026quot;Artificial Intelligence\u0026quot;, and \u0026quot;Precision Medicine\u0026quot;, topics such as \u0026quot;Male Fertility Preservation\u0026quot;, \u0026quot;Immunological Factors in Male Infertility\u0026quot;, \u0026quot;Molecular and Cellular Pathways\u0026quot;, \u0026quot;Sperm Quality and Function\u0026quot;, \u0026quot;Male Contraception\u0026quot;, and \u0026quot;Male Birth Control\u0026quot; were exclusively mentioned by ChatGPT (Table 4). Furthermore, a comparison of the number of topics recommended by different large language models reveals that Gemine recommended 21 topics (Including the same or repeated hotspots), which is significantly fewer than the 28 (Including the same or repeated hotspots and 25 topics (Including the same or repeated hotspots) recommended by GPT-3.5 and GPT-4, respectively (Table 4). Considering that the topic question-and-answer sessions all used the same prompts and the same number of repetitions (3 times), it is estimated that the number of topics recommended is related to the default temperature settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Evaluation of Male Infertility Research Hotspots Enhanced by ChatGPT\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHotspots\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHotspots Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003etotal\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLarge language models (LLMs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGemine\u003csup\u003eb)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPT\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ec)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPT\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ed)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPT-4\u003c/strong\u003e\u003cstrong\u003eo\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental Exposures and Male Fertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eInvestigating the effects of endocrine-disrupting chemicals, lifestyle factors, and environmental toxins on male reproductive health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssisted Reproductive Technologies (ART)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDeveloping and improving ART techniques like intracytoplasmic sperm injection (ICSI) and testicular sperm extraction (TESE).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetics and Male Infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eResearching genetic factors that may contribute to male infertility and potential genetic treatments or interventions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpigenetics and Sperm Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eFurther exploration of how epigenetic modifications in sperm may impact fertility and offspring health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological and Social Aspects of Male Infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eExploring the psychological impact of male infertility on individuals and couples, as well as interventions to address these issues.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Hormonal Imbalances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eExploring the role of hormones in male infertility and developing hormone-based therapies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStem Cell Research\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eExploring the potential use of stem cells in treating male infertility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative Stress and Sperm DNA Damage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eExamining how oxidative stress contributes to infertility by damaging sperm DNA, and exploring potential antioxidant therapies to mitigate this damage.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSperm Health and Quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eUnderstanding factors that affect sperm quality and ways to improve it.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Fertility Preservation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eResearch on techniques for preserving male fertility, especially in individuals undergoing cancer treatments or other medical procedures.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunological Factors in Male Infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eUnderstanding the role of the immune system in male infertility and potential immune-based treatments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovel diagnostic tools\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eResearchers are developing new tools for diagnosing male infertility. This includes exploring the use of advanced sperm analysis techniques and biomarkers to better assess sperm health and identify potential causes of infertility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular and Cellular Pathways\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDelving into the molecular and cellular mechanisms underlying male reproductive function to develop targeted therapies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSperm Quality and Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eInvestigating ways to improve sperm quality, including lifestyle factors (such as diet, exercise, and exposure to environmental toxins) that impact sperm health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Contraception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eOngoing efforts to develop new methods of male contraception that are safe, effective, and reversible.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic disparities in infertility treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eInvestigate studies on how access to infertility diagnosis and treatment can vary depending on socioeconomic status, highlighting potential healthcare inequalities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiet and Nutrition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eInvestigating how different diets and nutritional supplements affect male fertility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmics Approaches\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eUtilizing Next-generation sequencing (NGS) or other omics technologies to identify genetic causes of infertility and to understand the complex genetics of spermatogenesis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegenerative Medicine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDeveloping regenerative medicine approaches to repair or regenerate damaged reproductive tissues.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Birth Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eResearching new methods of male contraception.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpidemiological and Demographic Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eConducting large-scale studies to understand the prevalence, causes, and trends in male infertility across different populations and regions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical and Legal Considerations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDiscussion of ethical and legal issues surrounding fertility treatments, sperm donation, and genetic testing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArtificial Intelligence (AI) and Male Fertility Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eThe use of AI and machine learning algorithms for analyzing sperm quality and predicting fertility outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision Medicine in Male Infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eTailoring infertility treatments based on an individual\u0026apos;s genetic and molecular profile to optimize outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNumber\u003csub\u003etotal\u003c/sub\u003e\u003csup\u003ea)\u0026nbsp;\u003c/sup\u003erepresents the total number of this hotspot were both predicted by Google Gemine, GPT -3.5 and GPT-4.\u003c/p\u003e\n\u003cp\u003eGemine\u003csup\u003eb)\u0026nbsp;\u003c/sup\u003erepresents the total number of this hotspot were predicted by Google Gemini.\u003c/p\u003e\n\u003cp\u003eGPT-3.5\u003csup\u003ec)\u0026nbsp;\u003c/sup\u003erepresents the total number of this hotspot was predicted by GPT-3.5.\u003c/p\u003e\n\u003cp\u003eGPT-4\u003csup\u003ed)\u0026nbsp;\u003c/sup\u003erepresents the total number of this hotspot was predicted by GPT-4.\u003c/p\u003e\n\u003cp\u003eGPT-4o\u003csup\u003ee)\u003c/sup\u003e represents the total number of this hotspot was predicted by GPT-4o.\u003c/p\u003e\n\u003cp\u003eComparing the analysis of male infertility research hotspots based on real-world (literature evidence) and LLMs-enhanced data, we have found that the hotspots in male infertility research based on literature evidence are presented in the form of keywords. Through time series analysis, they clearly reflect the research hotspots and their trends in various periods but fail to clearly present the latest hotspots in male infertility research. However, LLMs perfectly presents the recent hot topics in male infertility. Yet, regardless of whether it\u0026apos;s ChatGPT or Gemini , LLMs\u0026apos;s summarization of hotspots is based on a principle of randomness, with its recommendation frequency unrelated to the degree of attention the hotspots received (Table 4). Therefore, exploring hotspot analysis based on real-world (literature evidence) and LLMs-enhanced data holds significant importance and value for studying future trends in male infertility research.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal Results\u003c/h2\u003e \u003cp\u003eIn recent years, with the development of LLMs, exploring the potential applications of such models, including ChatGPT and Google Gemini, in translational medicine has emerged as a cutting-edge and challenging task [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several studies have shown that ChatGPT can not only accelerate the integration of scientific research and clinical practice but also enhance the quality and efficiency of healthcare services [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, instances of ChatGPT being used in translational informatics research on male infertility have not yet been seen. To explore and compare the differences in themes and hotspots in male infertility research based on real-world (literature) data and virtual world data (LLMs), we employed bibliometrics, topic modeling analysis, and LLMs information enhancement as our research methods. We used high-frequency vocabulary in male infertility research such as \"Azoospermia\", \"Oligospermia\", \"Asthenospermia\", \"Teratozoospermia\", and \"Teratospermia\" as keywords for title and abstract searches. Through the data analysis of 14,852 male infertility publications, it has been shown that LLMs are effective tools and methods for information enhancement in translational medical research on male infertility. They can systematically summarize high-frequency topics from traditional bibliometrics, such as \"Environmental Exposures\", \"Psychological and Social Aspects\", \"Genetics\", \"Immunological Factors\", and \"Hormonal Imbalances\". Furthermore, these models are also capable of addressing low-frequency topics that traditional bibliometrics may not effectively summarize, such as \"Assisted Reproductive Technologies (ART)\", \"Stem Cell Research\", \"Male Fertility Preservation\", \"Precision Medicine\", and \"Artificial Intelligence (AI)\", etc. Comparison of the results analysis based on literature evidence and LLMs Q\u0026amp;A reveals that LLMs's answers can not only cover the impacts on sperm quantity, quality, and function that are widely reported in literature data, such as \"Environmental Exposures\", \"Genetic Factors\", \"Epigenetic Factors\", \"Hormonal Imbalances\", etc., but also take into account evidence less reported in literature data, such as \"Socioeconomic factors\", \"Precision Medicine\", \"Artificial Intelligence\", \"Fertility Preservation\", and \"Ethics and Law in Infertility\".Therefore, LLMs is an effective tool for exploring and understanding research trends and hotspots in the field, with significant potential applications in quickly grasping disciplinary directions and formulating development strategies for the discipline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations\u003c/h2\u003e \u003cp\u003eThis study attempts to explore the changing trends in male infertility research hotspots through methodologies based on bibliometrics and the information enhancement capabilities of LLMs. The results indicate that LLMs, including Google Gemini, GPT-3.5, and GPT-4, demonstrate outstanding abilities in summarizing the hotspots of male infertility research. However, as the methodologies of this study primarily rely on bibliometric evidence and the capabilities of LLMs, there are several limitations. One main limitation of the analysis is that the data resources in this study are limited to the Scopus database, neglecting literature from other citation databases such as PubMed and Web of Science. The omission of PubMed and Web of Science literature was not part of the initial design of this study. In the early stages of the project, we also attempted to merge citations based on DOI numbers. However, PubMed does not include complete citation records, and although Web of Science includes full citation records, it lacks filtering methods based on inclusion and exclusion of research subjects (e.g., filtering studies that focus only on human or human population research), which led to its exclusion.\u003c/p\u003e \u003cp\u003eIn the analysis of the literature growth trend, we attribute the increase in male infertility literature to the development and transformation of scientific and technical methodologies, including the advent of PCR technology, the popularity of NGS sequencing, and the spread of single-cell genomics. However, we also acknowledge the contributory role of socio-economic factors in the increase of male infertility literature, such as heightened societal awareness, an increase in the number of researchers, and ample research funding, which are significant drivers behind the rise in the number and publications of male infertility research.\u003c/p\u003e \u003cp\u003eThere are also limitations in summarizing research hotspots on male infertility based on LLMs, in that this study exclusively utilized Gemini, GPT-3.5, GPT-4, and GPT-4o as tools for analysis. The choice of Google Gemini, GPT-3.5, GPT-4, and GPT-4o as representatives of LLMs is due to their exceptional performance across various tasks and their widespread use [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, a comparative analysis of the correlation between the response frequencies of Google Gemini, GPT-3.5, and GPT-4 and the research hotspots of male infertility revealed a certain randomness in how these models summarize the research hotspots. There is no correlation between their recommendation frequencies and the level of attention to the hotspots. Tracing the cause, we found that the training corpora for large models such as Google Gemini and ChatGPT mainly come from sources like Documenting Large Webtext Corpora, WebText2, existing books, and Wikipedia, rather than specialized male infertility medical literature [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, the development of specialized LLMs focusing primarily on male infertility research could greatly improve the specificity, reduce the randomness and illusion issues, and enhance the capability of general LLMs in addressing specialized queries [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Conclusions\u003c/h2\u003e \u003cp\u003eThis study is the first to use male infertility as an example, employing bibliometric methods, topic modeling, and hot topic Q\u0026amp;A with LLMs to explore the summarization capabilities of models including Gemini, GPT-3.5, and GPT-4 in the context of male infertility. The results indicate that large language models are a beneficial supplement to traditional bibliometric analysis and topic modeling. They not only summarize the frequently occurring hot topics in traditional bibliometrics on male infertility but can also address low-frequency topics that traditional methods fail to effectively summarize. However, large language models also have issues such as illusion of knowledge, randomness, and weak specialization abilities. Therefore, exploring and developing specialized large language models could help improve the general issues of knowledge illusion, randomness, and weak specialization in general-purpose models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB. S. conceived the project. Y. Z and J. W. designed the entire analysis workflow. J. L., J. W., and X. L. collected the data. Y. Z., R. W., X. L., H. Z., and C. Z. organized the data. Y. Z. drafted the initial manuscript. J. W., R. W., and J. H. edited the manuscript. All authors participated in the discussion of the results. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eBairong Shen conceived the project. Yingbo Zhang and Jiao Wang designed the entire analysis workflow. Junyu Lu, Jiao Wang, and Xingyun Liu collected the data. Yingbo Zhang, Rongrong Wu, Xingyun Liu, Hui Zong, and Chaoying Zhan organized the data. Yingbo Zhang drafted the initial manuscript. Jiao Wang, Rongrong Wu, and Jiang Huang edited the manuscript. All authors participated in the discussion of the results.\u003c/p\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 32270690) and the Sichuan Science and Technology Program (grant number 2024YFHZ0205). The authors declare that they have no conflicts of interest.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and code for generating the pictures in this study have been deposited in the github (https://github.com/zyb1984/Landscape_sperm)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal A, Baskaran S, Parekh N, Cho CL, Henkel R, Vij S, Arafa M, Panner Selvam MK, Shah R: \u003cstrong\u003eMale infertility\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2021, \u003cstrong\u003e397\u003c/strong\u003e(10271):319-333.\u003c/li\u003e\n\u003cli\u003eSun H, Gong TT, Jiang YT, Zhang S, Zhao YH, Wu QJ: \u003cstrong\u003eGlobal, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990-2017: results from a global burden of disease study, 2017\u003c/strong\u003e. \u003cem\u003eAging \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(23):10952-10991.\u003c/li\u003e\n\u003cli\u003eRay PF: \u003cstrong\u003eDeciphering the genetics of male infertility: progress and challenges\u003c/strong\u003e. \u003cem\u003eThe Journal of urology \u003c/em\u003e2011, \u003cstrong\u003e186\u003c/strong\u003e(4):1183-1184.\u003c/li\u003e\n\u003cli\u003eZhao S, Su C, Lu Z, Wang F: \u003cstrong\u003eRecent advances in biomedical literature mining\u003c/strong\u003e. \u003cem\u003eBriefings in bioinformatics \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eZHANG Y, ZHAN C, WANG J, LIU X, HE M, WU C, SHEN B: \u003cstrong\u003eBioinformatics for sperm phenotypic abnormalities:current situation and future trends (in Chinese)\u003c/strong\u003e. \u003cem\u003eSci Sin Vitae \u003c/em\u003e2023, \u003cstrong\u003e53\u003c/strong\u003e(2):274-286.\u003c/li\u003e\n\u003cli\u003eBommasani R, Liang P, Lee T: \u003cstrong\u003eHolistic Evaluation of Language Models\u003c/strong\u003e. \u003cem\u003eAnnals of the New York Academy of Sciences \u003c/em\u003e2023, \u003cstrong\u003e1525\u003c/strong\u003e(1):140-146.\u003c/li\u003e\n\u003cli\u003eClusmann J, Kolbinger FR, Muti HS, Carrero ZI, Eckardt JN, Laleh NG, L\u0026ouml;ffler CML, Schwarzkopf SC, Unger M, Veldhuizen GP\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe future landscape of large language models in medicine\u003c/strong\u003e. \u003cem\u003eCommunications medicine \u003c/em\u003e2023, \u003cstrong\u003e3\u003c/strong\u003e(1):141.\u003c/li\u003e\n\u003cli\u003eDave T, Athaluri SA, Singh S: \u003cstrong\u003eChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations\u003c/strong\u003e. \u003cem\u003eFrontiers in artificial intelligence \u003c/em\u003e2023, \u003cstrong\u003e6\u003c/strong\u003e:1169595.\u003c/li\u003e\n\u003cli\u003eThirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW: \u003cstrong\u003eLarge language models in medicine\u003c/strong\u003e. \u003cem\u003eNature medicine \u003c/em\u003e2023, \u003cstrong\u003e29\u003c/strong\u003e(8):1930-1940.\u003c/li\u003e\n\u003cli\u003eMullis K, Faloona F, Scharf S, Saiki R, Horn G, Erlich H: \u003cstrong\u003eSpecific enzymatic amplification of DNA in vitro: the polymerase chain reaction\u003c/strong\u003e. \u003cem\u003eCold Spring Harbor symposia on quantitative biology \u003c/em\u003e1986, \u003cstrong\u003e51 Pt 1\u003c/strong\u003e:263-273.\u003c/li\u003e\n\u003cli\u003eZhu H, Zhang H, Xu Y, La\u0026scaron;\u0026scaron;\u0026aacute;kov\u0026aacute; 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\u003cstrong\u003e9\u003c/strong\u003e(11):e23101.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Male Infertility, large language models (LLMs), Bibliometrics, Topic Modeling, Information Enhancement","lastPublishedDoi":"10.21203/rs.3.rs-6000333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6000333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eInfertility is a significant negative factor affecting societal population growth and economic stability, with male infertility being a major cause of infertility. In recent years, with the development and advancement of next-generation sequencing technologies and high-resolution mass spectrometry, the volume of male infertility-related literature in scientific databases such as Scopus and PubMed has rapidly increased, and its topics have undergone complex changes over the past 50 years. Additionally, the advent of large language models (LLMs) has provided new tools for enhancing traditional literature analysis and topic modeling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eThis study aims to investigate the changes and trends in research hotspots on male infertility over the past 50 years. Furthermore, to explore the potential of large language models (LLMs) in decision support systems for the clinical translation of male infertility research, we also evaluated the information enhancement capabilities of LLMs in the context of research hotspots on male infertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eVarious methods, including bibliometrics, topic modeling, Gemini' and ChatGPT's question-answer approach, were employed to compare male infertility hotspots between real-world and virtual world data. Additionally, the study investigated LLMs's ability to enhance information in summarizing male infertility hotspots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eUnder the literature evidence of 14,852 male infertility-related publications (12,884 article-type publications and 1,968 review-type publications), traditional bibliometric analyses such as annual analysis, country analysis, and high-impact author analysis show that countries like the United States, China, and Italy are major publishers in infertility research, with the United States being the leading technical influencer in male infertility research. Subsequently, results from topic modeling analysis have effectively mapped out the research themes in male infertility over the past 50 years, this analysis highlights key subjects such as \"the impact of gene expression on male infertility\", \"the effect of age on sperm parameters\", and \"pathogenic genes of male infertility\", marking them as recent research hotspots. However, this method falls short in clearly presenting the latest hotspots in male infertility research. Lastly, the integration of LLMs information enhancement offers a new dimension in this research. This approach successfully presents the recent hotspots in male infertility, encompassing not only the impact of risk factors like \"Environmental Exposures\", \"Genetics\", \"Immunological Factors\", \"Hormonal Imbalances\" on sperm count and quality but also highlighting emerging areas such as \"Precision Medicine\" and \"Artificial Intelligence (AI)\" in male infertility research. Therefore, combining real-world literature evidence with the capabilities of LLMs is crucial for understanding and mapping future trends in this field.\u003c/p\u003e","manuscriptTitle":"Trends in Male Infertility Over the Past 50 Years: Landscape Analysis and the Emerging Role of Large Language Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 04:58:45","doi":"10.21203/rs.3.rs-6000333/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5db1d02-79ef-4f20-8bc0-ae035655aa90","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-14T07:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 04:58:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6000333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6000333","identity":"rs-6000333","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}