Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary

Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary Malik Sallam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4241528/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bibliometric analysis is a useful tool to assess influential publications on ChatGPT utility in healthcare, an emerging research topic. The aim of this study was to identify the top ten cited healthcare-related ChatGPT publications. The study employed an advanced search on three databases: Scopus, Web of Science, and Google Scholar to identify ChatGPT-related records in healthcare education, research, and practice by 30 November 2023. Ranking was based on the retrieved citation count in each database. The alternative metrics evaluated included PlumX metrics and Altmetric Attention Scores (AASs). A total of 22 unique records were identified in the three databases. Only two publications were found in the top 10 list across the three databases. The range of citation count varied per database with the highest range identified in Google Scholar (1019–121) followed by Scopus (242–88), and Web of Science (171–23). Google Scholar citations were correlated significantly with and the following metrics: Semantic Scholar highly influential citations (Spearman’s correlation coefficient (ρ) = .840, P < .001), PlumX captures (ρ = .831, P < .001), PlumX mentions (ρ = .609, P = .004), and AASs (ρ = .542, P = .009). Despite the several acknowledged limitations, bibliometric analysis in this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics highlighting its usefulness as a supplement to gauge publication impact even in a rapidly growing research field. ChatGPT in healthcare bibliometric analysis citation metrics publication impact AI in healthcare Figures Figure 1 BACKGROUND The accelerated advancement in artificial intelligence (AI) could have a transformative impact in different scientific and societal aspects [ 1 , 2 ]. In particular, the utility of AI-based conversational chatbots can be paradigm-shifting in healthcare [ 3 , 4 ]. Consequently, AI-based models’ adoption in healthcare education, research, and practice offers unique and unprecedented transformative opportunities [ 5 ]. For example, AI-based models can help in data analysis, refinement of clinical decision-making, and improving personalized medicine and health literacy [ 5 – 8 ]. Additionally, integration of these AI-based models in healthcare settings can help streamline the workflow with subsequent efficient and cost-effective delivery of timely care [ 5 , 6 , 9 ]. In healthcare education, the AI-based conversational chatbots can offer personalized learning tailored to individual student needs and simulate complex medical scenarios for training purposes at lower costs [ 5 , 10 , 11 ]. In healthcare-related research, AI-based models can aid in organizing and analyzing massive datasets with expedited novel insights, besides its ability to aid in medical writing [ 5 , 12 , 13 ]. Since its public release on 30 November 2022, ChatGPT has emerged as the prime, popular, and widely used example of AI-based conversational models. This was highlighted in various studies that investigated its utility including various studies addressing ChatGPT applications in healthcare [ 5 , 14 ]. Based on its perceived usefulness and ease-of-use, ChatGPT developed by OpenAI (San Francisco, California, the United States) demonstrated considerable potential in various healthcare related applications [ 5 , 14 , 15 ]. These applications include facilitating the health professional-patient interactions, helping in medical documentation, assisting in various research aspects, and offering medical education support [ 5 , 6 , 16 – 18 ]. The recent rapid growth of studies exploring the potential of ChatGPT in healthcare demonstrates its imminent significant impact in this field [ 5 , 6 , 19 , 20 ]. However, several studies revealed valid concerns and weaknesses that should be addressed for successful and responsible use of ChatGPT in healthcare [ 5 , 6 , 13 ]. These limitations were mainly related to ethics, privacy, cybersecurity issues, and potential biases in ChatGPT algorithms [ 5 , 6 , 21 ]. Therefore, it is crucial to address these issues to ensure the safe, responsible, ethical, and effective utilization of these AI-based tools in healthcare [ 5 , 6 , 22 ]. Bibliometric analysis is a helpful and widely used approach to assess the impact and trends of academic literature [ 23 – 25 ]. This analysis involves the measurement of various aspects of scientific records, such as citation counts, authorship features, and publication outreach; thus, it provides insights into the impact and trends of research within a specific field [ 26 ]. Several bibliometrics are used to assess publication impact and outreach [ 23 ]. For example, the Semantic Scholar (SS) highly influential citations (HICs) can be used to highlight references with significant impact [ 27 , 28 ]. PlumX from Plum Analytics (Philadelphia, Pennsylvania, the United States) offer the following metrics to highlight publication impact [ 29 – 33 ]: “PlumX Captures” metric that measures engagement via tracking publication downloads, saves, and bookmarks; “PlumX Mentions” metric which shows the publication relevance in society highlighted by the frequency of publication use by various digital media platforms; “PlumX Social Media” metric which assess the social media interactions [ 34 ]. The Altmetric Attention Score (Altmetric Limited, London, the United Kingdom) aggregates attention across diverse platforms with different weights of different sources, indicating the publication social and news impact [ 35 – 37 ]. Bibliometric analysis could serve as a valuable tool to systematically map the landscape of ChatGPT applications in healthcare [ 19 ]. Such an approach can help to provide an overview of the key research themes and influential publications, within this emerging and swiftly evolving research subject [ 38 ]. Additionally, bibliometric analysis can help to identify gaps in research and shape the trajectory of ongoing and future studies addressing the utility of ChatGPT in healthcare [ 19 , 39 ]. Therefore, the aim of this study was to conduct a bibliometric analysis of the top ten healthcare-related ChatGPT publications across prominent and widely used scientific databases (i.e., Scopus, Web of Science, and Google Scholar) in the first anniversary of ChatGPT public release [ 40 , 41 ]. One year following ChatGPT public inauguration, the surge in publications investigating its utility in healthcare was notable. Thus, a robust bibliometric analysis in this growing research field can offer valuable insights into the research trends involving ChatGPT applications and challenges in healthcare. Bibliometric analysis can help to identify the topics which received most attention by researchers, media, and the general public. Additionally, identification of the most influential publications in this growing field can help in delineating the current and future research priorities, which in turn can help to facilitate the successful integration of AI technologies, including ChatGPT in healthcare. METHODS Study Design This descriptive bibliometric analysis study was designed to analyze the top ten healthcare related publications on ChatGPT published over a period of one year. The classification was based on the citation count in three databases as follows: Scopus, Web of Science, and Google Scholar [ 40 , 41 ]. These databases were chosen for their extensive coverage of scholarly literature including healthcare and technology [ 40 , 41 ]. While PubMed/MEDLINE is considered a significant and widely-used database in healthcare research, the decision to exclude this relevant database from the search process was based on the lack of a clear feature for direct retrieval of citation ranking. The search concluded on 27 November 2023, ensuring the inclusion of all relevant publications up to the first anniversary of ChatGPT public release [ 14 ]. Detailed Search Strategies In Scopus, the search strategy focused on the article title, abstract, and the keywords. The exact search string was as follows: (TITLE-ABS-KEY ("ChatGPT" OR "GPT-3" OR "GPT-3.5" OR "GPT-4") AND TITLE-ABS-KEY ("healthcare" OR "medical" OR "health care")). The search in Scopus was conducted at 14:08 Amman local time on 27 November 2023. For the Web of Science database, the search was conducted using the topic search (TS) field. The exact search was as follows: TS=("ChatGPT" OR "GPT-3" OR "GPT-3.5" OR "GPT-4") AND TS=("healthcare" OR "health care"). This search was completed at 14:27 Amman local time on 27 November 2023. The Google Scholar search was conducted using the Publish or Perish software (Version 8) [ 42 ]. The search covered the years 2022–2023 and was concluded at 13:36 Amman local time on 27 November 2023. The search string used was: ("ChatGPT" OR "GPT-3" OR "GPT-3.5" OR "GPT-4") AND ("healthcare" OR "health care"). The data from the three databases were retrieved separately as comma-separated values (CVS) files, and the results were sorted based on citation counts in a descending order. Then, the top 10 records in each database were identified based on the screening of the title and abstract. For inclusion in this study, the record must have evaluated any aspect of ChatGPT applications in healthcare education, research, or practice [ 5 ]. Data on the 2022 journal impact factor was obtained via the Journal Citation Reports [ 43 ], while the 2022 CiteScore data were obtained directly from Scopus [ 44 ]. Alternative Metrics Retrieval For the top ten records identified in each database, a manual search for the alternative metrics was conducted. These alternative metrics included (1) the highly influential citations (SS HICs) identified through Semantic Scholar [ 45 ]; Altmetric Attention Scores (AASs) were procured directly from each respective record if available [ 35 ]; and the PlumX metrics were sourced from Scopus [ 34 , 44 ]. In details, the SS HICs are references characterized by having a significant impact on the citing record as determined by machine-learning model that analyze multiple factors including the frequency this reference was cited as well as the context of using this reference [ 45 , 46 ]. For the PlumX metrics, the “PlumX Captures” tracks and aggregates the frequency of downloads, saves, or bookmarks of a record, giving an indication of engagement in the scientific community [ 34 ]. The “PlumX Mentions” is a metric that assesses the frequency with which a publication is being mentioned or referenced in news media, blogs, and Wikipedia, reflecting the broader societal engagement [ 34 ]. The “PlumX Social Media” metric assesses the social media engagement via tracking shares, likes, posts, and other forms of social media interactions to measure the publication visibility and impact in social media (e.g., X, Facebook, Reddit, etc.) [ 34 ]. The Altmetric Attention Score is a composite metric by (Altmetric Limited, London, the United Kingdom) which measures the attention received by a publication across various social media and digital platforms, including news media, social media, policy documents, and online forums, reflecting a broad visibility [ 35 , 37 ]. To unify the final comparisons, Google Scholar citations as of 30 November 2023 was used for the final included publications, with data retrieved directly from Google Scholar for each publication approximately between 02:00 and 03:00 Amman time. This decision was made since all the retrieved records were available on Google Scholar with the exception of a single reference, for which the citation count was obtained directly from through Crossref on the publication website [ 47 ]. Statistical and Data Analysis The statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp, Armonk, New York, the United States). The level of statistical significance was P = .05. Correlations was assessed using the Kendall’s tau-b (τb) correlation coefficient and the Spearman’s rank-order correlation coefficient (ρ) based on non-normality of metrics for the majority of variables as assessed using the Shapiro–Wilk test. For the correlation between publication metrics as scale variables and the region of the corresponding authors, the Kruskal Wallis H (K-W) test was used. RESULTS Top 10 Records in Scopus, Web of Science, and Google Scholar by Citation Count As shown in ( Table 1 ), the top 10 identified records in Scopus varied in citation count from 242 to 88 citations. Based on the first affiliations of the corresponding authors, the records were mostly U.S.-based (n=5, 50%). Record types varied from editorial/comment (n=3, 30%), special/brief report or perspective (n=3, 30%), original article/investigation (n=2, 20%), and review (n=2, 20%). The 10 records were published in 9 different scientific journals with a 2022 CiteScores ranging from 0.9 to 134.4, and the journals were published by 9 different publishers. Table 1. Top ten ChatGPT records in healthcare in the Scopus database. Authors Title Scopus citation count Record type Country of the corresponding author Journal, (CiteScore), Publisher Sallam [5] ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns 242 Review The University of Jordan, Jordan Healthcare (Switzerland), (2.7), MDPI Gilson et al. [48] How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment 225 Article Yale University, the U.S. JMIR Medical Education, (5.0), JMIR Publications Inc. Lee et al. [49] Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine 174 Special report The U.S. New England Journal of Medicine, (134.4), Massachusetts Medical Society Shen et al. [50] ChatGPT and Other Large Language Models Are Double-edged Swords 157 Editorial New York University, the U.S. Radiology, (34.2), Radiological Society of North America Inc. Patel & Lam [16] ChatGPT: the future of discharge summaries? 145 Comment St Mary's Hospital, the U.K. The Lancet Digital Health, (33.1), Elsevier Ltd Liebrenz et al. [51] Generating scholarly content with ChatGPT: ethical challenges for medical publishing 131 Comment University of Bern, Switzerland The Lancet Digital Health, (33.1), Elsevier Ltd Ayers et al. [52] Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum 129 Original Investigation University of California, the U.S. JAMA Internal Medicine, (43.2), American Medical Association Biswas [53] ChatGPT and the Future of Medical Writing 124 Perspective University of Tennessee, the U.S. Radiology, (34.2), Radiological Society of North America Inc. Cascella et al. [54] Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios 112 Brief report University of Parma, Italy Journal of Medical Systems, (11.8), Springer Ray [55] ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope 88 Review Sikkim University, India Internet of Things and Cyber-Physical Systems, (0.9), KeAi Communications Co. The top 10 identified records in Web of Science varied in citation count from 171 to 23 citations ( Table 2 ). Based on the first affiliations of the corresponding authors, the records were variable with two U.S.-based (n=2/9, 22.2%) and two India-based (n=2/9, 22.2%) records. Record types varied from editorial/comment (n=4, 40%), review (n=3, 30%), original article (n=2, 20%), and brief report (n=1, 10%). The 10 records were published in 8 different scientific journals with a 2022 Impact Factor ranging from 1.2 to 82.9, and the journals were published by 6 different publishers. Table 2. Top ten ChatGPT records in healthcare in the Web of Science database. Authors Title WoS a Core citation count Record type Country of the corresponding author Journal, (IF b ), Publisher Sallam [5] ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns 171 Review The University of Jordan, Jordan Healthcare (Switzerland), (2.8), MDPI Alkaissi & McFarlane [56] Artificial Hallucinations in ChatGPT: Implications in Scientific Writing 102 Editorial Kings County Hospital Center, the U.S. Cureus J Med Science, (1.2), Springer Cascella et al. [54] Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios 78 Brief report University of Parma, Italy Journal of Medical Systems, (5.3), Springer Nature Medicine Editorial [47] Will ChatGPT transform healthcare? 48 Editorial NA Nature Medicine, (82.9), Nature portfolio Korngiebel & Mooney [57] Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery 45 Comment The Hastings Center Garrison, the U.S. npj Digital Medicine, (15.2), Nature research Dave et al. [17] ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations 34 Review Bukovinian State Medical University, Ukraine Frontiers in Artificial Intelligence, (4.0), Frontiers Media SA Vaishya et al. [58] ChatGPT: Is this version good for healthcare and research? 31 Article Indraprastha Apollo Hospitals, India Diabetes & Metabolic Syndrome-Clinical Research & Reviews, (10.0), Oxford Univ Press Hopkins et al. [59] Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift 26 Commentary Flinders University, Australia JNCI Cancer Spectrum, (4.4), Oxford Univ Press Sinha et al. [60] Applicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology 24 Article All India Institute of Medical Sciences, India Cureus J Med Science, (1.2), Springer Temsah et al. [61] Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts 23 Review Universiti Sains Malaysia, Malaysia Cureus J Med Science, (1.2), Springer a WoS: Web of Science; b IF: Impact factor. The top 10 identified records in Google Scholar varied in citation count from 1019 to 121 citations ( Table 3 ). Based on the first affiliations of the corresponding authors, the records were variable with three U.S.-based (30%) and two Italy-based (20%) records. Record types varied from editorial/comment (n=4, 40%), brief report/perspective/special communication (n=3, 30%), original article (n=2, 20%), and review (n=1, 10%). The 10 records were published in 8 different scientific journals and the journals were published by 8 different publishers. Table 3. Top ten ChatGPT records in healthcare in the Google Scholar database. Authors Title GS a Citation count Record type Country of the corresponding author Journal, Publisher Kung et al. [62] Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models 1019 Article AnsibleHealth, Inc Mountain View, the U.S. PLOS Digital Health, PLOS Sallam [5] ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns 523 Review The University of Jordan, Jordan Healthcare (Switzerland), MDPI Gilson et al. [48] How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment 430 Article Yale University, the U.S. JMIR Medical Education, JMIR Publications Inc. Shen et al. [50] ChatGPT and Other Large Language Models Are Double-edged Swords 309 Editorial New York University, the U.S. Radiology, Radiological Society of North America Inc. Patel & Lam [16] ChatGPT: the future of discharge summaries? 255 Comment St Mary's Hospital, the U.K. The Lancet Digital Health, Elsevier Ltd Liebrenz et al. [51] Generating scholarly content with ChatGPT: ethical challenges for medical publishing 255 Comment University of Bern, Switzerland The Lancet Digital Health, Elsevier Ltd Cascella et al. [54] Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios 249 Brief report University of Parma, Italy Journal of Medical Systems, Springer Khan et al. [63] ChatGPT - Reshaping medical education and clinical management 180 Special Communication PharmEvo (Pvt) Ltd, Pakistan Pakistan Journal of Medical Sciences, Professional Medical Publications Eysenbach [11] The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers 179 Editorial JMIR Publications, Canada JMIR Medical Education, JMIR Publications Inc. De Angelis et al. [64] ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health 121 Perspective University of Pisa, Italy Frontiers in Public Health, Frontiers Media SA a GS: Google Scholar. The Compiled List of Top Unique Records Across the Three Databases The number of unique records identified in the three databases were 22. Only two records appeared in the top ten list in the three databases out of the 22 records (9.1%) [5,54], while four appeared in two databases (18.2%) [16,48,50,51]. As shown in ( Figure 1 ), the geographic distribution of the top records across the three databases based on the affiliations of the corresponding authors varied with the most common being U.S.-based. Please insert Figure 1 here Figure 1. The top 10 healthcare related ChatGPT records based on citation count across Scopus, Web of Science, and Google Scholar databases. Records in Scopus are shown in orange color, Web of Science in violet color, and in Google Scholar in black color. The font size of the authors is relative to the citation count. The map was generated in Microsoft Excel, powered by Bing, © GeoNames, Microsoft, Navinfo, TomTom, Wikipedia. I declare neutrality with regard to jurisdictional claims in this map. The Correlation between Google Scholar Citation Count and Alternative Metrics To determine the possible correlations between the latest Google Scholar citations as of 30 November 2023, with the alternative metrics (PlumX, SS HICs, and AASs) the Kendall’s tau-b (τb) correlation coefficient and Spearman’s rank-order correlation coefficient (ρ) were used. Significant positive correlations were detected between the Google Scholar citations and SS HICs (τb=.696, ρ=.84, P <.001 for both), PlumX captures (τb=.67, ρ=.831, P <.001 for both), PlumX mentions (τb=.456, P =.006, ρ=.609, P =.004), and AASs (τb=.396, P =.01, ρ=.542, P =.009, Table 4 ). Table 4. Correlation between Google Scholar citation count and alternative metrics. Metric Kendall’s tau-b (τb) correlation coefficient GS citation count SS HICs PlumX captures PlumX mentions PlumX social media AAS Spearman’s correlation coefficient (ρ) τb τb τb τb τb GS a citation count ρ - .696** .670** .456** .144 .396* P value <.001 <.001 .006 .418 .01 SS HICs b ρ .840** - .554** .295 .034 .19 P value <.001 .001 .081 .853 .231 PlumX captures ρ .831** .739** - .406* .195 .237 P value <.001 <.001 .013 .269 .144 PlumX mentions ρ .609** .411 .547* - .007 .745** P value .004 .072 .013 .971 <.001 PlumX social media ρ .169 .056 .244 .005 - .072 P value .476 .813 .299 .984 .685 AAS c ρ .542** .27 .287 .805** .092 - P value .009 .225 .219 <.001 .699 a GS: Google Scholar; b SS HICs: Semantic Scholar highly influential citations; c AAS: Altmetric Attention Score; ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed). The PlumX mentions and Altmetric Attention Scores were significantly associated with the region of the corresponding author affiliation, with the highest being in the United States or Canada ( Table 5 ). Table 5. Association of publication metrics with the region of the affiliation of the corresponding author. Region United States or Canada Australia, Italy, Switzerland, U.K., or Ukraine India, Jordan, Malaysia, or Pakistan P value, K-W e H ( df ) Metric Mean±SD d Mean±SD Mean±SD GS a citation count 341.1±268.83 182±83.08 205.83±189.66 .214, 3.079 (2) SS HICs b 8.1±10.2 2.83±2.48 6±6.36 .456, 1.569 (2) PlumX captures 279.44±137.93 227.33±102.77 343.8±330.74 .884, .246 (2) PlumX mentions 100.89±182.22 4±3.69 4.8±8.58 .007, 9.873 (2) PlumX social media 6.78±17.22 15±17.32 7.4±10.85 .291, 2.470 (2) AAS c 1026.7±1830.26 66.17±48.02 14.5±18.43 <.001, 16.106 (2) a GS: Google Scholar; b SS HICs: Semantic Scholar highly influential citations; c AAS: Altmetric Attention Score; d SD: Standard deviation; e K-W: Kruskal Wallis H test. DISCUSSION In the current study, bibliometric analysis was used to scrutinize the growing ChatGPT-related healthcare literature over a single year. The methodological approach involved a search across three prominent academic databases, with the primary criterion for ranking publications being the frequency of citations these records received [ 65 – 67 ]. The major finding was the elucidation of the rapid growth of literature and its swift impact in this emerging research field. Marking the first anniversary of ChatGPT public release and its recognition as the fastest growing application with active users, the current study pointed to the intricate interplay between AI and healthcare. Central to the findings of this study was the identification of the seminal study by Kung et al. that highlighted the impressive ChatGPT performance in the United States Medical Licensing Examination (USMLE) [ 62 ]. In less than a year, the impact of Kung et al.’s study was highlighted by more than 1K citations in Google Scholar, underlining the potential of ChatGPT in medical education which is gaining a huge momentum [ 5 , 13 , 48 , 62 , 68 – 70 ]. Notably, the publication by Kung et al. was absent in Scopus and Web of Science which can be attributed to its publication in the newly established, yet-to-be-indexed scientific journal, PLOS Digital Health [ 62 ]. This highlights the importance of inclusion of Google Scholar in bibliometric analysis and systematic reviews considering the comprehensive coverage and immediate indexing of Google Scholar for various scholarly sources [ 71 ]. Additionally, a review I authored, which explored the applications of ChatGPT in healthcare education, research, and practice, has been found as one of the most frequently cited publications across the three databases, being the most commonly cited publication in Web of Science and Scopus [ 5 ]. Despite being published in a journal with relatively modest impact factor and Citescore, the aforementioned review achieved a significant level of citations within short period of time, which may suggest that influential research can transcend the traditional metrics of journal impact [ 72 , 73 ]. The geographical analysis of the source of the top publications identified in the current study revealed noticeable variability in spite of relative predominance of research originating from the U.S. [ 47 – 50 , 52 , 53 , 56 , 57 , 62 ]. This result can be related to the forefront role and influence of U.S.-based research with advanced research infrastructure and funding opportunities [ 74 ]. Nevertheless, the presence of additional ten countries as origins of influential papers can point to the global interest in this emerging research field. Such a diversity appears of utmost value since the utility of ChatGPT in healthcare should be guided by the consideration of varied healthcare systems and patient demographics worldwide. The current study identified 22 unique records in the top publications list, a figure that surpassed the anticipated number of 10 across the three searched databases. Such a result demonstrates the notable variation in citation counts across different scientific databases [ 75 ]. Therefore, this result highlights the necessity for reliance on multiple databases in citation analysis to avoid biases in evaluation of publication impact [ 76 ]. While a high citation count can be indicative of a high impact, the observed discrepancies in citation counts and absence of uniform presence of the top publications across the databases suggest that conventional publication metrics may not be an adequate measure of the publication influence, particularly in emerging research subjects such as ChatGPT in healthcare [ 77 ]. In this study, a wide range of publication types were identified among the most influential publications. These types ranged from editorials, commentaries, and perspectives to original research articles and reviews, reflecting the dynamic nature of scholarly communication on ChatGPT role in healthcare. Importantly, the vast majority of top-ranked publications found in this study were published in open access journals, which may hint to the possible impact of open access policies on publication influence despite the need for more evidence to support such a tentative link [ 78 – 80 ]. In this study, the strong correlation found between citation count and alternative metrics such as Semantic Scholar HICs, PlumX metrics, and AASs emphasizes the potential of these publication metrics in the assessment of scholarly and societal influence [ 36 ]. Thus, these alternative publication metrics can complement the citation count to assess the outreach and influence of publications involving ChatGPT in healthcare similar to its use in other fields [ 81 , 82 ]. Finally, the influential publications identified in the current study pointed to three primary application areas of ChatGPT in healthcare. First, enhancing healthcare practice through improved workflows and patient engagement. Second, augmenting healthcare education with personalized learning and clinical simulations. Third, supporting medical research in areas like academic writing and data management. However, these applications should be done in light of challenges including the generation of inaccurate content, ethical concerns, and potential biases. Additionally, future research should prioritize establishing standard methodologies for design and reporting to ensure the reliability and credibility of assessing ChatGPT performance in various healthcare settings [ 59 , 83 – 86 ]. Future research should focus on multidisciplinary approaches involving AI developers, computer scientists, healthcare professionals, experts in healthcare education, and ethicists [ 86 , 87 ]. Limitations I clearly and explicitly have to point out that the use of citation count or alternative metrics is by no means a direct measure of the quality of publications ranked in this study or a reflection of their direct impact. These metrics can only be viewed as a surrogate marker of the publication trends in this newly emerging research field. Other several caveats in this study should be highlighted clearly and taken into consideration for any attempt to interpret the study results as follows: First, this study used Scopus, Web of Science, and Google Scholar as the databases for publication selection. Despite the extensive coverage of these databases, it is imperative to consider that this approach might overlooked publications in less prominent or regional journals due to differing indexing criteria and inherent coverage biases. The incorporation of Google Scholar as an additional source for publications characterized by comprehensive and immediate indexing was done to mitigate this limitation as much as possible. Second, the search strategy focused on the titles and abstracts of the records. This approach may have resulted in inadvertent exclusion of publications that addressed ChatGPT utility in healthcare in the main text but not explicitly in the title/abstract. Third, the geographic allocation of publications based on the affiliation of the corresponding authors could be viewed as a source of selection bias since this approach might not be fully representative of the authorship and collaboration networks, potentially causing bias in the interpretation of publication sources. Fourth, it is important to reiterate that the use of citation counts and alternative metrics, such as Semantic Scholar HICs, PlumX, and Altmetric AAS for publication ranking is influenced by a variety of factors such as scientific journal perceived impact and visibility and the date of publication and indexing for the records. For example, more recently published articles might have lower citation counts due to a limited time frame for acknowledging their results. Thus, this approach of ranking should be carefully considered since it does not represent a direct reflection of the scientific quality or impact of the included publications. Finally, based on the descriptive nature of the current study, the results were confined to descriptive and subjective identification of trends and correlations, without the ability to elucidate the underlying reasons for such observed attributes of the publications. CONCLUSIONS In summary, the bibliometric analysis conducted in this study highlighted the dynamic nature of ChatGPT-related research in healthcare. The range of publication types and the variability in citation patterns across databases highlighted the complexity of the scholarly discourse in the newly emerging field namely ChatGPT in healthcare. The current study identified 22 influential publications addressing ChatGPT in healthcare across three scientific databases. The findings revealed robust correlations between GS citations and various alternative metrics, such as SS HICs, PlumX captures and mentions, and AAS, demonstrating the versatile impact of the identified publications and the usefulness of alternative metrics as an approach for gauging the publication impact. However, the regional affiliations of corresponding authors of the identified records, particularly in the U.S. and Canada, were correlated with higher PlumX mentions and AAS, suggesting the possible influence of research origin on its news coverage and public visibility. As AI-based conversational models continue to infiltrate various aspects of healthcare, the insights and recommendation of the identified publications can be invaluable in guiding future research, policy development, and practical applications in this rapidly evolving research field. This includes the need for a multidisciplinary collaboration among AI developers, healthcare researchers, education, ethics, and communication experts. Such a collaboration is essential for successful integration of ChatGPT as an example of AI models in healthcare. This approach can help to ensure maximizing ChatGPT benefits in various aspects of healthcare while successfully addressing any possible challenges. Abbreviations AAS Altmetric Attention Score AI Artificial intelligence GS Google Scholar HIC Highly influential citation K-W Kruskal Wallis H test SS Semantic Scholar USMLE The United States Medical Licensing Examination Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Data Availability Statement The datasets analyzed during the current study are available in the original records included in the study. Competing interests The author declares no conflict of interest. Funding This research received no funding. 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In particular, the utility of AI-based conversational chatbots can be paradigm-shifting in healthcare [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, AI-based models\u0026rsquo; adoption in healthcare education, research, and practice offers unique and unprecedented transformative opportunities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, AI-based models can help in data analysis, refinement of clinical decision-making, and improving personalized medicine and health literacy [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, integration of these AI-based models in healthcare settings can help streamline the workflow with subsequent efficient and cost-effective delivery of timely care [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In healthcare education, the AI-based conversational chatbots can offer personalized learning tailored to individual student needs and simulate complex medical scenarios for training purposes at lower costs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In healthcare-related research, AI-based models can aid in organizing and analyzing massive datasets with expedited novel insights, besides its ability to aid in medical writing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince its public release on 30 November 2022, ChatGPT has emerged as the prime, popular, and widely used example of AI-based conversational models. This was highlighted in various studies that investigated its utility including various studies addressing ChatGPT applications in healthcare [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on its perceived usefulness and ease-of-use, ChatGPT developed by OpenAI (San Francisco, California, the United States) demonstrated considerable potential in various healthcare related applications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These applications include facilitating the health professional-patient interactions, helping in medical documentation, assisting in various research aspects, and offering medical education support [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe recent rapid growth of studies exploring the potential of ChatGPT in healthcare demonstrates its imminent significant impact in this field [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, several studies revealed valid concerns and weaknesses that should be addressed for successful and responsible use of ChatGPT in healthcare [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These limitations were mainly related to ethics, privacy, cybersecurity issues, and potential biases in ChatGPT algorithms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, it is crucial to address these issues to ensure the safe, responsible, ethical, and effective utilization of these AI-based tools in healthcare [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBibliometric analysis is a helpful and widely used approach to assess the impact and trends of academic literature [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This analysis involves the measurement of various aspects of scientific records, such as citation counts, authorship features, and publication outreach; thus, it provides insights into the impact and trends of research within a specific field [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral bibliometrics are used to assess publication impact and outreach [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, the Semantic Scholar (SS) highly influential citations (HICs) can be used to highlight references with significant impact [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. PlumX from Plum Analytics (Philadelphia, Pennsylvania, the United States) offer the following metrics to highlight publication impact [\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: \u0026ldquo;PlumX Captures\u0026rdquo; metric that measures engagement via tracking publication downloads, saves, and bookmarks; \u0026ldquo;PlumX Mentions\u0026rdquo; metric which shows the publication relevance in society highlighted by the frequency of publication use by various digital media platforms; \u0026ldquo;PlumX Social Media\u0026rdquo; metric which assess the social media interactions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The Altmetric Attention Score (Altmetric Limited, London, the United Kingdom) aggregates attention across diverse platforms with different weights of different sources, indicating the publication social and news impact [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBibliometric analysis could serve as a valuable tool to systematically map the landscape of ChatGPT applications in healthcare [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Such an approach can help to provide an overview of the key research themes and influential publications, within this emerging and swiftly evolving research subject [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, bibliometric analysis can help to identify gaps in research and shape the trajectory of ongoing and future studies addressing the utility of ChatGPT in healthcare [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to conduct a bibliometric analysis of the top ten healthcare-related ChatGPT publications across prominent and widely used scientific databases (i.e., Scopus, Web of Science, and Google Scholar) in the first anniversary of ChatGPT public release [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne year following ChatGPT public inauguration, the surge in publications investigating its utility in healthcare was notable. Thus, a robust bibliometric analysis in this growing research field can offer valuable insights into the research trends involving ChatGPT applications and challenges in healthcare. Bibliometric analysis can help to identify the topics which received most attention by researchers, media, and the general public. Additionally, identification of the most influential publications in this growing field can help in delineating the current and future research priorities, which in turn can help to facilitate the successful integration of AI technologies, including ChatGPT in healthcare.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis descriptive bibliometric analysis study was designed to analyze the top ten healthcare related publications on ChatGPT published over a period of one year. The classification was based on the citation count in three databases as follows: Scopus, Web of Science, and Google Scholar [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These databases were chosen for their extensive coverage of scholarly literature including healthcare and technology [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While PubMed/MEDLINE is considered a significant and widely-used database in healthcare research, the decision to exclude this relevant database from the search process was based on the lack of a clear feature for direct retrieval of citation ranking. The search concluded on 27 November 2023, ensuring the inclusion of all relevant publications up to the first anniversary of ChatGPT public release [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDetailed Search Strategies\u003c/h2\u003e \u003cp\u003eIn Scopus, the search strategy focused on the article title, abstract, and the keywords. The exact search string was as follows: (TITLE-ABS-KEY (\"ChatGPT\" OR \"GPT-3\" OR \"GPT-3.5\" OR \"GPT-4\") AND TITLE-ABS-KEY (\"healthcare\" OR \"medical\" OR \"health care\")). The search in Scopus was conducted at 14:08 Amman local time on 27 November 2023.\u003c/p\u003e \u003cp\u003eFor the Web of Science database, the search was conducted using the topic search (TS) field. The exact search was as follows: TS=(\"ChatGPT\" OR \"GPT-3\" OR \"GPT-3.5\" OR \"GPT-4\") AND TS=(\"healthcare\" OR \"health care\"). This search was completed at 14:27 Amman local time on 27 November 2023.\u003c/p\u003e \u003cp\u003eThe Google Scholar search was conducted using the Publish or Perish software (Version 8) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The search covered the years 2022\u0026ndash;2023 and was concluded at 13:36 Amman local time on 27 November 2023. The search string used was: (\"ChatGPT\" OR \"GPT-3\" OR \"GPT-3.5\" OR \"GPT-4\") AND (\"healthcare\" OR \"health care\").\u003c/p\u003e \u003cp\u003eThe data from the three databases were retrieved separately as comma-separated values (CVS) files, and the results were sorted based on citation counts in a descending order. Then, the top 10 records in each database were identified based on the screening of the title and abstract. For inclusion in this study, the record must have evaluated any aspect of ChatGPT applications in healthcare education, research, or practice [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData on the 2022 journal impact factor was obtained via the Journal Citation Reports [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], while the 2022 CiteScore data were obtained directly from Scopus [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAlternative Metrics Retrieval\u003c/h2\u003e \u003cp\u003eFor the top ten records identified in each database, a manual search for the alternative metrics was conducted. These alternative metrics included (1) the highly influential citations (SS HICs) identified through Semantic Scholar [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; Altmetric Attention Scores (AASs) were procured directly from each respective record if available [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; and the PlumX metrics were sourced from Scopus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn details, the SS HICs are references characterized by having a significant impact on the citing record as determined by machine-learning model that analyze multiple factors including the frequency this reference was cited as well as the context of using this reference [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the PlumX metrics, the \u0026ldquo;PlumX Captures\u0026rdquo; tracks and aggregates the frequency of downloads, saves, or bookmarks of a record, giving an indication of engagement in the scientific community [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The \u0026ldquo;PlumX Mentions\u0026rdquo; is a metric that assesses the frequency with which a publication is being mentioned or referenced in news media, blogs, and Wikipedia, reflecting the broader societal engagement [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The \u0026ldquo;PlumX Social Media\u0026rdquo; metric assesses the social media engagement via tracking shares, likes, posts, and other forms of social media interactions to measure the publication visibility and impact in social media (e.g., X, Facebook, Reddit, etc.) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Altmetric Attention Score is a composite metric by (Altmetric Limited, London, the United Kingdom) which measures the attention received by a publication across various social media and digital platforms, including news media, social media, policy documents, and online forums, reflecting a broad visibility [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo unify the final comparisons, Google Scholar citations as of 30 November 2023 was used for the final included publications, with data retrieved directly from Google Scholar for each publication approximately between 02:00 and 03:00 Amman time. This decision was made since all the retrieved records were available on Google Scholar with the exception of a single reference, for which the citation count was obtained directly from through Crossref on the publication website [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and Data Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp, Armonk, New York, the United States). The level of statistical significance was \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05. Correlations was assessed using the Kendall\u0026rsquo;s tau-b (τb) correlation coefficient and the Spearman\u0026rsquo;s rank-order correlation coefficient (ρ) based on non-normality of metrics for the majority of variables as assessed using the Shapiro\u0026ndash;Wilk test. For the correlation between publication metrics as scale variables and the region of the corresponding authors, the Kruskal Wallis H (K-W) test was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eTop 10 Records in Scopus, Web of Science, and Google Scholar by Citation Count\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in (\u003cstrong\u003eTable 1\u003c/strong\u003e), the top 10 identified records in Scopus varied in citation count from 242 to 88 citations. Based on the first affiliations of the corresponding authors, the records were mostly U.S.-based (n=5, 50%). Record types varied from editorial/comment (n=3, 30%), special/brief report or perspective (n=3, 30%), original article/investigation (n=2, 20%), and review (n=2, 20%). The 10 records were published in 9 different scientific journals with a 2022 CiteScores ranging from 0.9 to 134.4, and the journals were published by 9 different publishers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Top ten ChatGPT records in healthcare in the Scopus database.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScopus citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecord type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of the corresponding author\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal, (CiteScore), Publisher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eSallam\u0026nbsp;[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eThe University of Jordan, Jordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eHealthcare (Switzerland), (2.7), MDPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eGilson et al.\u0026nbsp;[48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eHow Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eYale University, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eJMIR Medical Education, (5.0), JMIR Publications Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eLee et al.\u0026nbsp;[49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eBenefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eSpecial report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eThe U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eNew England Journal of Medicine, (134.4), Massachusetts Medical Society\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eShen et al.\u0026nbsp;[50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT and Other Large Language Models Are Double-edged Swords\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eEditorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eNew York University, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eRadiology, (34.2), Radiological Society of North America Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003ePatel \u0026amp; Lam\u0026nbsp;[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT: the future of discharge summaries?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eSt Mary\u0026apos;s Hospital, the U.K.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eThe Lancet Digital Health, (33.1), Elsevier Ltd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eLiebrenz et al.\u0026nbsp;[51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eGenerating scholarly content with ChatGPT: ethical challenges for medical publishing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Bern, Switzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eThe Lancet Digital Health, (33.1), Elsevier Ltd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eAyers et al.\u0026nbsp;[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eComparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eOriginal Investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of California, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eJAMA Internal Medicine, (43.2), American Medical Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eBiswas\u0026nbsp;[53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT and the Future of Medical Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003ePerspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Tennessee, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eRadiology, (34.2), Radiological Society of North America Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eCascella et al.\u0026nbsp;[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eEvaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eBrief report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Parma, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eJournal of Medical Systems, (11.8), Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eRay\u0026nbsp;[55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\" valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.580645161290322%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.483870967741936%\" valign=\"top\"\u003e\n \u003cp\u003eSikkim University, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eInternet of Things and Cyber-Physical Systems, (0.9), KeAi Communications Co.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe top 10 identified records in Web of Science varied in citation count from 171 to 23 citations (\u003cstrong\u003eTable 2\u003c/strong\u003e). Based on the first affiliations of the corresponding authors, the records were variable with two U.S.-based (n=2/9, 22.2%) and two India-based (n=2/9, 22.2%) records. Record types varied from editorial/comment (n=4, 40%), review (n=3, 30%), original article (n=2, 20%), and brief report (n=1, 10%). The 10 records were published in 8 different scientific journals with a 2022 Impact Factor ranging from 1.2 to 82.9, and the journals were published by 6 different publishers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Top ten ChatGPT records in healthcare in the Web of Science database.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWoS \u003csup\u003ea\u003c/sup\u003e Core citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecord type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of the corresponding author\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal, (IF \u003csup\u003eb\u003c/sup\u003e), Publisher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eSallam\u0026nbsp;[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eThe University of Jordan, Jordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eHealthcare (Switzerland), (2.8), MDPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eAlkaissi \u0026amp; McFarlane\u0026nbsp;[56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eArtificial Hallucinations in ChatGPT: Implications in Scientific Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eEditorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eKings County Hospital Center, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eCureus J Med Science, (1.2), Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eCascella et al.\u0026nbsp;[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eEvaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eBrief report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Parma, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eJournal of Medical Systems, (5.3), Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eNature Medicine Editorial\u0026nbsp;[47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eWill ChatGPT transform healthcare?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eEditorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eNature Medicine, (82.9), Nature portfolio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eKorngiebel \u0026amp; Mooney\u0026nbsp;[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eConsidering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eThe Hastings Center Garrison, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003enpj Digital Medicine, (15.2), Nature research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eDave et al.\u0026nbsp;[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eBukovinian State Medical University, Ukraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eFrontiers in Artificial Intelligence, (4.0), Frontiers Media SA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eVaishya et al.\u0026nbsp;[58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT: Is this version good for healthcare and research?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eIndraprastha Apollo Hospitals, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes \u0026amp; Metabolic Syndrome-Clinical Research \u0026amp; Reviews, (10.0), Oxford Univ Press\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eHopkins et al.\u0026nbsp;[59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eArtificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eCommentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eFlinders University, Australia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eJNCI Cancer Spectrum, (4.4), Oxford Univ Press\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eSinha et al.\u0026nbsp;[60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eApplicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eAll India Institute of Medical Sciences, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eCureus J Med Science, (1.2), Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.240506329113924%\" valign=\"top\"\u003e\n \u003cp\u003eTemsah et al.\u0026nbsp;[61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.537974683544302%\" valign=\"top\"\u003e\n \u003cp\u003eOverview of Early ChatGPT\u0026apos;s Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.341772151898734%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.822784810126583%\" valign=\"top\"\u003e\n \u003cp\u003eUniversiti Sains Malaysia, Malaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eCureus J Med Science, (1.2), Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e WoS: Web of Science; \u003csup\u003eb\u003c/sup\u003e IF: Impact factor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe top 10 identified records in Google Scholar varied in citation count from 1019 to 121 citations (\u003cstrong\u003eTable 3\u003c/strong\u003e). Based on the first affiliations of the corresponding authors, the records were variable with three U.S.-based (30%) and two Italy-based (20%) records. Record types varied from editorial/comment (n=4, 40%), brief report/perspective/special communication (n=3, 30%), original article (n=2, 20%), and review (n=1, 10%). The 10 records were published in 8 different scientific journals and the journals were published by 8 different publishers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Top ten ChatGPT records in healthcare in the Google Scholar database.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGS \u003csup\u003ea\u003c/sup\u003e Citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecord type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of the corresponding author\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal, Publisher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eKung et al.\u0026nbsp;[62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003ePerformance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eAnsibleHealth, Inc Mountain View, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003ePLOS Digital Health, PLOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eSallam\u0026nbsp;[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eThe University of Jordan, Jordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eHealthcare (Switzerland), MDPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eGilson et al.\u0026nbsp;[48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eHow Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eYale University, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eJMIR Medical Education, JMIR Publications Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eShen et al.\u0026nbsp;[50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT and Other Large Language Models Are Double-edged Swords\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eEditorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eNew York University, the U.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eRadiology, Radiological Society of North America Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003ePatel \u0026amp; Lam\u0026nbsp;[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT: the future of discharge summaries?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eSt Mary\u0026apos;s Hospital, the U.K.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eThe Lancet Digital Health, Elsevier Ltd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eLiebrenz et al.\u0026nbsp;[51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eGenerating scholarly content with ChatGPT: ethical challenges for medical publishing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Bern, Switzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eThe Lancet Digital Health, Elsevier Ltd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eCascella et al.\u0026nbsp;[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eEvaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eBrief report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Parma, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eJournal of Medical Systems, Springer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eKhan et al.\u0026nbsp;[63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT - Reshaping medical education and clinical management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eSpecial Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003ePharmEvo (Pvt) Ltd, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003ePakistan Journal of Medical Sciences, Professional Medical Publications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eEysenbach\u0026nbsp;[11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eThe Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003eEditorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eJMIR Publications, Canada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eJMIR Medical Education, JMIR Publications Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003eDe Angelis et al.\u0026nbsp;[64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.712460063897762%\" valign=\"top\"\u003e\n \u003cp\u003eChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.945686900958467%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.099041533546325%\" valign=\"top\"\u003e\n \u003cp\u003ePerspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Pisa, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57188498402556%\" valign=\"top\"\u003e\n \u003cp\u003eFrontiers in Public Health, Frontiers Media SA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e GS: Google Scholar.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Compiled List of Top Unique Records Across the Three Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of unique records identified in the three databases were 22. Only two records appeared in the top ten list in the three databases out of the 22 records (9.1%)\u0026nbsp;[5,54], while four appeared in two databases (18.2%)\u0026nbsp;[16,48,50,51]. As shown in (\u003cstrong\u003eFigure 1\u003c/strong\u003e), the geographic distribution of the top records across the three databases based on the affiliations of the corresponding authors varied with the most common being U.S.-based.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlease insert Figure 1 here\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e The top 10 healthcare related ChatGPT records based on citation count across Scopus, Web of Science, and Google Scholar databases.\u003c/p\u003e\n\u003cp\u003eRecords in Scopus are shown in orange color, Web of Science in violet color, and in Google Scholar in black color. The font size of the authors is relative to the citation count. The map was generated in Microsoft Excel, powered by Bing, \u0026copy; GeoNames, Microsoft, Navinfo, TomTom, Wikipedia. I declare neutrality with regard to jurisdictional claims in this map.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Correlation between Google Scholar Citation Count and Alternative Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the possible correlations between the latest Google Scholar citations as of 30 November 2023, with the alternative metrics (PlumX, SS HICs, and AASs) the Kendall\u0026rsquo;s tau-b (\u0026tau;b) correlation coefficient and Spearman\u0026rsquo;s rank-order correlation coefficient (\u0026rho;) were used.\u003c/p\u003e\n\u003cp\u003eSignificant positive correlations were detected between the Google Scholar citations and SS HICs (\u0026tau;b=.696, \u0026rho;=.84, \u003cem\u003eP\u003c/em\u003e\u0026lt;.001 for both), PlumX captures (\u0026tau;b=.67, \u0026rho;=.831, \u003cem\u003eP\u003c/em\u003e\u0026lt;.001 for both), PlumX mentions (\u0026tau;b=.456, \u003cem\u003eP\u003c/em\u003e=.006, \u0026rho;=.609, \u003cem\u003eP\u003c/em\u003e=.004), and AASs (\u0026tau;b=.396, \u003cem\u003eP\u003c/em\u003e=.01, \u0026rho;=.542, \u003cem\u003eP\u003c/em\u003e=.009, \u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Correlation between Google Scholar citation count and alternative metrics.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKendall\u0026rsquo;s tau-b (\u0026tau;b) correlation coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGS citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS HICs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX captures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX mentions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX social media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman\u0026rsquo;s correlation coefficient (\u0026rho;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026tau;b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026tau;b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026tau;b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026tau;b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026tau;b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGS \u003csup\u003ea\u003c/sup\u003e citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.696**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.670**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.456**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.396*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS HICs \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.840**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.554**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX captures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.831**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.739**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.406*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX mentions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.609**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.547*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.745**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX social media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAS \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026rho;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.542**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.805**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.256410256410257%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e GS: Google Scholar; \u003csup\u003eb\u003c/sup\u003e SS HICs: Semantic Scholar highly influential citations; \u003csup\u003ec\u003c/sup\u003e AAS: Altmetric Attention Score; ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PlumX mentions and Altmetric Attention Scores were significantly associated with the region of the corresponding author affiliation, with the highest being in the United States or Canada (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Association of publication metrics with the region of the affiliation of the corresponding author.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnited States or Canada\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAustralia, Italy, Switzerland, U.K., or Ukraine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia, Jordan, Malaysia, or Pakistan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value, K-W \u003csup\u003ee\u003c/sup\u003e H (\u003cem\u003edf\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD \u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGS \u003csup\u003ea\u003c/sup\u003e citation count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e341.1\u0026plusmn;268.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e182\u0026plusmn;83.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e205.83\u0026plusmn;189.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e.214, 3.079 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS HICs \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e8.1\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e2.83\u0026plusmn;2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e6\u0026plusmn;6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e.456, 1.569 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX captures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e279.44\u0026plusmn;137.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e227.33\u0026plusmn;102.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e343.8\u0026plusmn;330.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e.884, .246 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX mentions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e100.89\u0026plusmn;182.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e4\u0026plusmn;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e4.8\u0026plusmn;8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e.007, 9.873 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlumX social media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e6.78\u0026plusmn;17.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e15\u0026plusmn;17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e7.4\u0026plusmn;10.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e.291, 2.470 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.566343042071196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAS \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e1026.7\u0026plusmn;1830.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e66.17\u0026plusmn;48.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e14.5\u0026plusmn;18.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.6084142394822%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001, 16.106 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e GS: Google Scholar; \u003csup\u003eb\u003c/sup\u003e SS HICs: Semantic Scholar highly influential citations; \u003csup\u003ec\u003c/sup\u003e AAS: Altmetric Attention Score; \u003csup\u003ed\u003c/sup\u003e SD: Standard deviation; \u003csup\u003ee\u003c/sup\u003e K-W: Kruskal Wallis H test.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the current study, bibliometric analysis was used to scrutinize the growing ChatGPT-related healthcare literature over a single year. The methodological approach involved a search across three prominent academic databases, with the primary criterion for ranking publications being the frequency of citations these records received [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The major finding was the elucidation of the rapid growth of literature and its swift impact in this emerging research field. Marking the first anniversary of ChatGPT public release and its recognition as the fastest growing application with active users, the current study pointed to the intricate interplay between AI and healthcare.\u003c/p\u003e \u003cp\u003eCentral to the findings of this study was the identification of the seminal study by Kung et al. that highlighted the impressive ChatGPT performance in the United States Medical Licensing Examination (USMLE) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In less than a year, the impact of Kung et al.\u0026rsquo;s study was highlighted by more than 1K citations in Google Scholar, underlining the potential of ChatGPT in medical education which is gaining a huge momentum [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Notably, the publication by Kung et al. was absent in Scopus and Web of Science which can be attributed to its publication in the newly established, yet-to-be-indexed scientific journal, PLOS Digital Health [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This highlights the importance of inclusion of Google Scholar in bibliometric analysis and systematic reviews considering the comprehensive coverage and immediate indexing of Google Scholar for various scholarly sources [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, a review I authored, which explored the applications of ChatGPT in healthcare education, research, and practice, has been found as one of the most frequently cited publications across the three databases, being the most commonly cited publication in Web of Science and Scopus [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite being published in a journal with relatively modest impact factor and Citescore, the aforementioned review achieved a significant level of citations within short period of time, which may suggest that influential research can transcend the traditional metrics of journal impact [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe geographical analysis of the source of the top publications identified in the current study revealed noticeable variability in spite of relative predominance of research originating from the U.S. [\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This result can be related to the forefront role and influence of U.S.-based research with advanced research infrastructure and funding opportunities [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Nevertheless, the presence of additional ten countries as origins of influential papers can point to the global interest in this emerging research field. Such a diversity appears of utmost value since the utility of ChatGPT in healthcare should be guided by the consideration of varied healthcare systems and patient demographics worldwide.\u003c/p\u003e \u003cp\u003eThe current study identified 22 unique records in the top publications list, a figure that surpassed the anticipated number of 10 across the three searched databases. Such a result demonstrates the notable variation in citation counts across different scientific databases [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Therefore, this result highlights the necessity for reliance on multiple databases in citation analysis to avoid biases in evaluation of publication impact [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. While a high citation count can be indicative of a high impact, the observed discrepancies in citation counts and absence of uniform presence of the top publications across the databases suggest that conventional publication metrics may not be an adequate measure of the publication influence, particularly in emerging research subjects such as ChatGPT in healthcare [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, a wide range of publication types were identified among the most influential publications. These types ranged from editorials, commentaries, and perspectives to original research articles and reviews, reflecting the dynamic nature of scholarly communication on ChatGPT role in healthcare. Importantly, the vast majority of top-ranked publications found in this study were published in open access journals, which may hint to the possible impact of open access policies on publication influence despite the need for more evidence to support such a tentative link [\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the strong correlation found between citation count and alternative metrics such as Semantic Scholar HICs, PlumX metrics, and AASs emphasizes the potential of these publication metrics in the assessment of scholarly and societal influence [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, these alternative publication metrics can complement the citation count to assess the outreach and influence of publications involving ChatGPT in healthcare similar to its use in other fields [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the influential publications identified in the current study pointed to three primary application areas of ChatGPT in healthcare. First, enhancing healthcare practice through improved workflows and patient engagement. Second, augmenting healthcare education with personalized learning and clinical simulations. Third, supporting medical research in areas like academic writing and data management. However, these applications should be done in light of challenges including the generation of inaccurate content, ethical concerns, and potential biases. Additionally, future research should prioritize establishing standard methodologies for design and reporting to ensure the reliability and credibility of assessing ChatGPT performance in various healthcare settings [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan additionalcitationids=\"CR84 CR85\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuture research should focus on multidisciplinary approaches involving AI developers, computer scientists, healthcare professionals, experts in healthcare education, and ethicists [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eI clearly and explicitly have to point out that the use of citation count or alternative metrics is by no means a direct measure of the quality of publications ranked in this study or a reflection of their direct impact. These metrics can only be viewed as a surrogate marker of the publication trends in this newly emerging research field.\u003c/p\u003e \u003cp\u003eOther several caveats in this study should be highlighted clearly and taken into consideration for any attempt to interpret the study results as follows: First, this study used Scopus, Web of Science, and Google Scholar as the databases for publication selection. Despite the extensive coverage of these databases, it is imperative to consider that this approach might overlooked publications in less prominent or regional journals due to differing indexing criteria and inherent coverage biases. The incorporation of Google Scholar as an additional source for publications characterized by comprehensive and immediate indexing was done to mitigate this limitation as much as possible.\u003c/p\u003e \u003cp\u003eSecond, the search strategy focused on the titles and abstracts of the records. This approach may have resulted in inadvertent exclusion of publications that addressed ChatGPT utility in healthcare in the main text but not explicitly in the title/abstract.\u003c/p\u003e \u003cp\u003eThird, the geographic allocation of publications based on the affiliation of the corresponding authors could be viewed as a source of selection bias since this approach might not be fully representative of the authorship and collaboration networks, potentially causing bias in the interpretation of publication sources.\u003c/p\u003e \u003cp\u003eFourth, it is important to reiterate that the use of citation counts and alternative metrics, such as Semantic Scholar HICs, PlumX, and Altmetric AAS for publication ranking is influenced by a variety of factors such as scientific journal perceived impact and visibility and the date of publication and indexing for the records. For example, more recently published articles might have lower citation counts due to a limited time frame for acknowledging their results. Thus, this approach of ranking should be carefully considered since it does not represent a direct reflection of the scientific quality or impact of the included publications.\u003c/p\u003e \u003cp\u003eFinally, based on the descriptive nature of the current study, the results were confined to descriptive and subjective identification of trends and correlations, without the ability to elucidate the underlying reasons for such observed attributes of the publications.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, the bibliometric analysis conducted in this study highlighted the dynamic nature of ChatGPT-related research in healthcare. The range of publication types and the variability in citation patterns across databases highlighted the complexity of the scholarly discourse in the newly emerging field namely ChatGPT in healthcare. The current study identified 22 influential publications addressing ChatGPT in healthcare across three scientific databases. The findings revealed robust correlations between GS citations and various alternative metrics, such as SS HICs, PlumX captures and mentions, and AAS, demonstrating the versatile impact of the identified publications and the usefulness of alternative metrics as an approach for gauging the publication impact. However, the regional affiliations of corresponding authors of the identified records, particularly in the U.S. and Canada, were correlated with higher PlumX mentions and AAS, suggesting the possible influence of research origin on its news coverage and public visibility.\u003c/p\u003e \u003cp\u003eAs AI-based conversational models continue to infiltrate various aspects of healthcare, the insights and recommendation of the identified publications can be invaluable in guiding future research, policy development, and practical applications in this rapidly evolving research field. This includes the need for a multidisciplinary collaboration among AI developers, healthcare researchers, education, ethics, and communication experts. Such a collaboration is essential for successful integration of ChatGPT as an example of AI models in healthcare. This approach can help to ensure maximizing ChatGPT benefits in various aspects of healthcare while successfully addressing any possible challenges.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAltmetric Attention Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGoogle Scholar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHighly influential citation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eK-W\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKruskal Wallis H test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSemantic Scholar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSMLE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe United States Medical Licensing Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the original records included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalik Sallam contributed to Conceptualization; Data curation; Formal analysis; Methodology; Visualization; Writing - original draft; and Writing - review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGruetzemacher R, Whittlestone J. The transformative potential of artificial intelligence. \u003cem\u003eFutures\u003c/em\u003e. 2022/01/01/ 2022;135:102884. doi:10.1016/j.futures.2021.102884\u003c/li\u003e\n\u003cli\u003eXu Y, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. \u003cem\u003eInnovation (Camb)\u003c/em\u003e. Nov 28 2021;2(4):100179. doi:10.1016/j.xinn.2021.100179\u003c/li\u003e\n\u003cli\u003eSallam M, Salim NA, Al-Tammemi AB, et al. ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine Conspiracy: A Descriptive Study at the Outset of a Paradigm Shift in Online Search for Information. \u003cem\u003eCureus\u003c/em\u003e. Feb 2023;15(2):e35029. doi:10.7759/cureus.35029\u003c/li\u003e\n\u003cli\u003eZhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 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Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models. \u003cem\u003eCureus\u003c/em\u003e. Nov 2023;15(11):e49373. doi:10.7759/cureus.49373\u003c/li\u003e\n\u003cli\u003eSallam M, Barakat M, Sallam M. METRICS: Establishing a Preliminary Checklist to Standardize Design and Reporting of Artificial Intelligence-Based Studies in Healthcare (Preprint). \u003cem\u003eJMIR Preprints\u003c/em\u003e. 2023;doi:10.2196/preprints.54704\u003c/li\u003e\n\u003cli\u003eSallam M, Al-Farajat A, Egger J. Envisioning the Future of ChatGPT in Healthcare: Insights and Recommendations from a Systematic Identification of Influential Research and a Call for Papers. \u003cem\u003eJordan Medical Journal\u003c/em\u003e. 02/19 2024;58(1)doi:10.35516/jmj.v58i1.2285\u003c/li\u003e\n\u003cli\u003eVeras M, Labb\u0026eacute; DR, Furlano J, et al. A framework for equitable virtual rehabilitation in the metaverse era: challenges and opportunities. \u003cem\u003eFront Rehabil Sci\u003c/em\u003e. 2023;4:1241020. doi:10.3389/fresc.2023.1241020\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":"ChatGPT in healthcare, bibliometric analysis, citation metrics, publication impact, AI in healthcare","lastPublishedDoi":"10.21203/rs.3.rs-4241528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4241528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBibliometric analysis is a useful tool to assess influential publications on ChatGPT utility in healthcare, an emerging research topic. The aim of this study was to identify the top ten cited healthcare-related ChatGPT publications. The study employed an advanced search on three databases: Scopus, Web of Science, and Google Scholar to identify ChatGPT-related records in healthcare education, research, and practice by 30 November 2023. Ranking was based on the retrieved citation count in each database. The alternative metrics evaluated included PlumX metrics and Altmetric Attention Scores (AASs). A total of 22 unique records were identified in the three databases. Only two publications were found in the top 10 list across the three databases. The range of citation count varied per database with the highest range identified in Google Scholar (1019\u0026ndash;121) followed by Scopus (242\u0026ndash;88), and Web of Science (171\u0026ndash;23). Google Scholar citations were correlated significantly with and the following metrics: Semantic Scholar highly influential citations (Spearman\u0026rsquo;s correlation coefficient (ρ)\u0026thinsp;=\u0026thinsp;.840, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), PlumX captures (ρ\u0026thinsp;=\u0026thinsp;.831, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), PlumX mentions (ρ\u0026thinsp;=\u0026thinsp;.609, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004), and AASs (ρ\u0026thinsp;=\u0026thinsp;.542, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009). Despite the several acknowledged limitations, bibliometric analysis in this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics highlighting its usefulness as a supplement to gauge publication impact even in a rapidly growing research field.\u003c/p\u003e","manuscriptTitle":"Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 15:49:58","doi":"10.21203/rs.3.rs-4241528/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":"4dae01a2-30a0-42b4-b707-cc9b8d9f9c57","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T11:51:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-23 15:49:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4241528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4241528","identity":"rs-4241528","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}