Development Trends and Knowledge Framework of Artificial Intelligence (AI) Applications in Oncology by years: A Bibliometric Analysis from 1992 to 2022

Development Trends and Knowledge Framework of Artificial Intelligence (AI) Applications in Oncology by years: A Bibliometric Analysis from 1992 to 2022 | 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 Development Trends and Knowledge Framework of Artificial Intelligence (AI) Applications in Oncology by years: A Bibliometric Analysis from 1992 to 2022 Murat Koçak, Zafer Akçalı This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4260599/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 Purpose: Oncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022. Methods: The research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022. Results: A total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are " machine learning ", "deep learning," "radiomics", "breast cancer", “melanoma” and "artificial intelligence," which are the research hotspots in this field. A total of 10866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated Conclusion: The investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field. oncology cancer artificial intelligence deep learning neural network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Artificial intelligence is based on creating machines that think like humans and is seen as the apotheosis of science (McCorduck, 1982). Artificial intelligence (AI), which has emerged in different fields in the twenty-first century, has become a trend in many fields, such as medicine, science, and business (Jarek & Mazurek, 2019) and has manifested itself in medicine. It is possible to categorize medical data that can be assessed with deep learning/machine learning/artificial intelligence into genetic, imaging, and clinical data. The images may be real-size photographs of visible parts of the human body and internal parts of the human body obtained by devices such as computed tomography, x-ray, magnetic resonance, and endoscopic devices. The field that deals with images of the inside of the human body obtained with such devices is called radiomics. In 2009, "Radiogenomics: Creating a link between Molecular Diagnostics and Diagnostic Imaging" (Rutman et al., 2009) and "Radiomics: Images Are More than Pictures, They Are Data" (Gillies et al., 2016) in, the term radiomics started to appear in the literature. Competitions using The Pascal Visual Object Classes Challenge and Imagenet datasets have been held worldwide to recognize highly accurate images by electronic systems. In 2012, AlexNet software, which participated in the competition under the name Supervision, won this competition by a large margin, and the paper "ImageNet classification with deep convolutional neural networks," published by the authors (Krizhevskyet al., 2012), was an important milestone. Please note that, Ilya Sutskever is a major contributor to ChatGPT, a large language model based AI software. The article "Radiomics: images are more than pictures, they are data” (Gillies et al., 2016), drew attention to the fact that meaningful results can be obtained primarily in cancer patients by converting radiologic images into numerical data. Publications on the ability of Alexnet-type software to successfully evaluate patient-related images have started to appear since 2017 and have increased steadily. Magnified images of cells in the human body tissues can be obtained in real-time with devices such as endoscopy, or cells extracted from the body by biopsy can be examined by various processes and staining. The field that deals with microscopic images of tissues are called pathomics (Hou L, 2016). There are very few publications using the term pathomics. Genetic data can be related to DNA gene sequences or protein structures. The field dealing with gene sequences is called genomics and the field dealing with protein structures is called proteomics. The highly cited publications on gene sequencing and cancer started even before radiomics and received many citations (Khan et al., 2001). Genetic data can be about DNA gene sequences or protein structures. The field that deals with gene sequences is called genomics, and the field that deals with protein structures is called proteomics. Highly cited publications on gene sequencing and cancer started even before radiomics and were highly cited (Khan et al., 2001; Shipp et al.,2002 ). Although not cited as genomics, publications on proteomics started appearing before radiomics (Chen et al., 2004). While the name clinicomics seems appropriate for evaluating clinical data with machine learning (Zhang et al., 2023; Rennard & Stoner, 2005; Maojo et al., 2005), the term has only recently been used. In the USA., clinical data obtained from hospitals' electronic health records (EHR) are anonymized / de-identified in a CancerLinq database and re-recorded so that meaningful conclusions can be drawn and used by users who want to benefit from it. (Schilsky et al., 2014; Rubinstein & Warner, 2018 ). Unlike the SEER database (https://seer.cancer.gov, an extensive anonymized cancer patient database), where fewer parameters about a patient are stored, it should be considered to allow for the evaluation of individualized diagnoses and treatments. Clinicomics may attempt to extract meaningful data from text based clinical records and is expected to utilize large language model AI frameworks. Since it is necessary to use one of the research methods appropriate for evaluation and prediction, bibliometric analysis is one of the analyses that serves this purpose. Bibliometrics is used as a type of analysis that systematically analyzes datasets (Donthu et al., 2021). This type of analysis requires a systematic literature review and meaningful structuring of a large dataset (Donthu et al., 202). Bibliometric analysis is a method used to analyze the studies conducted in a specific field, and its use has increased in recent years. While bibliometric analysis in this concept is seen as inevitable for many fields (Synder, 2019), it is necessary to examine academic studies on the impact of artificial intelligence applications in medicine through bibliometric analysis. This is because AI's increasing popularity and widespread use cause the subject to become a need in terms of health. This study analyzed 7923 articles published between 1992 and 20222 with the R program, biblioshiny package program, and VOSviewer software(Van Eck & Waltman, 2010). The article contributed to determining the techniques used in the cumulative information in the relevant research, thematic analysis, and central trends. It sheds light on the research to be conducted. In this context, the study will first include the literature on artificial intelligence and cancer, then the data used and the research method will be mentioned. Karger (2023) provides a bibliometric analysis of research on AI for cancer detection, emphasizing its growth and potential for early detection. Khanam and Kumar (2022) explore the recent applications of AI algorithms, including machine learning and deep learning, in the early detection of various cancers. Pacurari et al. (2023) focus on using AI techniques, such as support vector machines and neural networks, to detect and classify lung, breast, and brain cancers using medical imaging. Overall, these papers highlight the importance of AI in improving Oncology and emphasize the need for further research in this field. The objective of this research is to address specific inquiries regarding the utilization of Artificial Intelligence (AI) in Oncology. a) Sources: Which scientific journals are most influential in the field? b) Researhers: Which Authors are the most influential in the field of Artificial Intelligence (AI) applications in Oncology? c) Papers: Which papers about Artificial Intelligence (AI) applications in Oncology? d) Keywords: What are the most popular authors keywords in Artificial Intelligence (AI) applications in Oncology research? e) How have the themes of Artificial Intelligence (AI) applications in Oncology evolved? f) Funding: Which fundings are the most influential in the field of Artificial Intelligence (AI) applications in Oncology? 2. Methods The relevant subject is examined statistically and mathematically in Bibliometrics, and a framework is drawn for the course. In addition, bibliometric analysis can also reveal the effectiveness of studies conducted through statistical data (Broadus, 1987). The data for this study were retrieved from the online database Web of Science on 21.11.2023. The reasons for using the Web of Science database instead of Scopus Google Scholar for bibliometric analysis are that it is the most extensive database in abstracts and literature, it can produce information with better actions, decisions, and results (Web of Science, 2022), it is a valuable resource for bibliometric studies, and it offers a comprehensive perspective on publications in the fields of science, technology, art, medicine, and social sciences. The Web of Science database used in bibliometric analysis is preferred (Martín et al., 2018). The search strategy of this study is as follows: While obtaining the data of the study, the first step was to search the Web of Science database (TI=("deep-Learn*" OR "machine learn*" OR "deep learn*"OR "artificial intelligence" OR "artificial neural network*" OR "deep neural network*" OR radiomics OR pathomics )) AND (AB=(melanoma OR cancer OR malignancy OR leukemia OR lymphoma OR neoplasia OR SEER* OR "Surveillance, Epidemiology, and End Results" OR CancerLinQ*)) AND (PY=1992-2022) AND (DT=Article). Data from databases were directly accessed to retrieve metadata from chosen documents, encompassing details like active authors, journal sources, countries, institutions, and funding sources. The analysis involved employing tools such as Bibliometrix package (version 3.1.4, http://www.bibliometrix.org) in R software (version 3.6.3), VOSviewer, and Litmaps to generate comprehensive bibliometric insights. Initially, raw data in plain text format was loaded and processed, involving calculations and visual representations of metadata comprising sources, authors, and citations. This was followed by intricate analyses focusing on clustering and the conceptual framework encompassing intelligence and society. Additionally, a comprehensive bibliometric evaluation was carried out, encompassing co-authorship and keyword co-occurrence using the VOSviewer software (Van Eck & Waltman, 2010). 3. Results When 7923 articles were reviewed, although the importance of genetic data in cancer diagnosis is significant, there are relatively few publications in machine learning and genomics fields, comprising only 2.2% (174 articles) of the studies in this work. There are only 12 articles related with pathomics, and deep learning in WOS database. The term 'Clinicomics' has not yet been established and has not found a place within the article set of this study. When searching for the term 'Clinical informatics,' only four articles were found; 'Clinical data extraction' yielded 1.6% (124 articles), 'SEER' resulted in 0.90% (71 articles), and 'NLP' led to 28 articles. Only 1 article containing the term 'CancerLing'(Heilbroner et al., 2021) was found. Only three articles can be found when the keywords 'CancerLinq' and 'machine learning' are searched across all fields in the WOS database. As 1716 articles were found with the term 'Radiomics' and 2215 articles with the term 'image,' we can conclude that the most commonly used datasets related to the topic of this article are primarily in the form of images. 3.1 General information The findings of the bibliometric analysis conducted within the scope of the study will be presented in this section. Table 1 provides fundamental details about the dataset, encompassing its size, descriptive statistics, content overview, and statistics regarding authorship and collaborative efforts. It has 7923 documents over the timespan of 1992–2022. 7923 journal articles were extracted from the Web of Science database. This downloaded database was then thoroughly analyzed and examined with the help of the Bibliometrix and R software (2023) application. At the early stage of this study, a descriptive analysis was provided to examine the details of the work published in this area(Table 1). Table 1: General information about the publications analyzed in the Artificial Intelligence (AI) and Oncology Dataset Description Results Main Information About Data Timespan 1992:2022 Sources (Journals, Books, etc) 1592 Documents 7923 Annual Growth Rate % 19.34 Average citations per doc 24 Document Contents Keywords Plus (ID) 7443 Author's Keywords (DE) 10966 Authors Authors 35198 Authors of single-authored docs 120 Co-Authors per Doc 7.95 International co-authorships % 29.48 (Bibliometrix & R software, 2023) Table 1 shows that the analyzed studies consist of 7923 publications published between 1992 and 2022 and are based on the statistics of publications on oncology using artificial intelligence methods. Figure 1 shows the quantity of AI-related articles within the oncology domain. Overall, there's been a consistent upward trend in publications from 1992 to 2022. Particularly in the last decade, there has been a surge in global interest within the research field. Between 1994 and 2014, publications remained relatively low and steady. However, since AI gained prominence around 2016, there has been a notable and substantial increase in the number of publications. The trend of the journal impact factor quartile of articles is shown in Figure 2. From 2016 to 2021, the number of documents in Q1 articles showed a fast growth trend; after 2021, the number of documents in Q1 has gone steadily. For the first time in 2021, the number of articles in the Q2 category exceeded that in the Q1 category. This means that higher-quality journals are starting to lower acceptance rates in this field. The lowest rate in this area is for articles in the Q4 category. This indicator shows the popularity of this field. The Web of Science was used to meticulously define and classify citation topics. All findings from the search query were included in the review without further filtration. The citation topics were narrowly focused and categorized according to the recently published classifications by the Web of Science, encompassing over 2500 detailed citation topics. This classification operates hierarchically below the Web of Science subject categories and citation topics at a broader level, enabling a precise and unbiased evaluation of the technologies utilized in the search query. Our datasets were determined and ranked based on the citation topic micro criteria in the WoS Citation index. The most represented citation topics micro based on our datasets were "glioblastoma", "breast cancer," "prostate cancer," "lung cancer," “melanoma” "rectal cancer," and "gastric cancer." Various cancer types are distributed among the article publications related to cancer research. The analysis of cancer types investigated provides insight into the present landscape of cancer research employing artificial intelligence techniques. As depicted in Figure 3, within this decade, the most prevalent studies have focused on glioblastoma (539 articles) and breast cancer (488 articles), with prostate cancer (345 articles) following closely in frequency. 3.2 Authors In Table 2 and Figure 4, Lotka's Law reveals the quantitative distribution of the publications of authors who contribute to the literature on a particular subject in the literature of that field. With this, the scientific productivity of the authors was tried to be revealed. Lotka's Law predicts that 70% of the authors who publish on a subject contribute to the subject with one publication, 15% with two publications, and 7% with three publications (Rowland, 2005). It can be seen that the data obtained as a result of the analysis also complies with Lotka's Law. According to Table 2, 78% of the authors made one publication, 13% made two, and 4% made three. Table 2: Lotka's Law and Author Productivity Ratio Documents written N. of Authors Proportion of Authors 1 25587 0.729 2 4999 0.142 3 1799 0.051 4 921 0.026 5 553 0.016 6 313 0.009 7 222 0.006 8 136 0.004 9 102 0.003 10 99 0.003 (Bibliometrix & R software, 2023) Figure 5a shows the main statistical characteristics of the Top 20 authors ranked by number of articles. When the graph is analyzed, Tian, Jie, and Liu Zaiyi are the leading authors working on artificial intelligence and Oncology. The author's publications cover 17.5 percent of the total publications. Figure 5b shows the main statistical characteristics of the Top 20 authors ranked by number of citations. When the graph is analyzed, Aerts Hugo and Gillies Robyn are the leading authors working on artificial intelligence and oncology. The author's number of citations covers 22.7 percent of the total citations. Figure 6 shows the bibliometric historiography using the science mapping tool bibliometrix (Aria and Cuccurullo, 2017). Initially, this mapping process establishes the historical direct citation network starting from the most-cited work, subsequently visualizing the network in chronological order (Garfield, 2004). The subsequent subsections will detail these networks, proceeding from the earliest to the most recent. As seen in Figure 6, the radomics article by Gillies (2016), Robyn is one of the first articles to show the importance of using artificial intelligence in cancer detection. Therefore, it is at the center of the Historiography graph. 3.3 Papers The top 25 most widely cited articles are presented in Table 3. The article was published by Esteva A in Nature in 2017 (Total citations = 5900), followed by Gillies (2016) in Radiology in 2016 (total citations = 4362), and Lambin (2012) published in the European Journal of Cancer in 2012 with 3018 citations. Hence, an annual average citation has been included in Table 3 to aid researchers in promptly identifying recently published highly cited papers. These high-impact publications, in essence, offer a swift overview of the field and expand researchers' perspectives. Table 3: Distribution of Most Cited AI Articles in Oncology from 1992 to 2022 Paper Article Title #citation DOI Journal WoS Categories Esteva et al., 2017 Dermatologist-level classification of skin cancer with deep neural networks 5900 10.1038/nature21056 Nature Multidisciplinary Sciences Gillies et al., 2016 Radiomics: Images Are More than Pictures, They Are Data 4362 10.1148/radiol.2015151169 Radiology Radiology Lambin et al., 2012 Radiomics: Extracting more information from medical images using advanced feature analysis 3018 10.1016/j.ejca.2011.11.036 Eur. J. Cancer Oncology Aerts et al., 2014 Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach 2958 10.1038/ncomms5006 Nat. Commun. Multidisciplinary Sciences Van Griethuysen et al., 2017 Computational Radiomics System to Decode the Radiographic Phenotype 2725 10.1158/0008-5472.CAN-17-0339 Cancer Res. Oncology Khan et al., 2001 Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks 1897 10.1038/89044 Nat. Med. Biochemistry & Molecular Biology Shipp et al., 2002 Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning 1796 10.1038/nm0102-68 Nat. Med. Biochemistry & Molecular Biology Bejnordi et al., 2017 Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer 1466 10.1001/jama.2017.14585 JAMA-J. Am. Med. Assoc. Medicine, General & Internal Kumar et al.,2012 Radiomics: the process and the challenges 1374 10.1016/j.mri.2012.06.010 Magn. Reson. Imaging Radiology Zwanenburg et al., 2020 The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping 1367 10.1148/radiol.2020191145 Radiology Radiology Coudray et al., 2018 Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning 1243 10.1038/s41591-018-0177-5 Nat. Med. Biochemistry & Molecular Biology Huang et al., 2016 Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer 1151 10.1200/JCO.2015.65.9128 J. Clin. Oncol. Oncology Malta et al., 2018 Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation 1009 10.1016/j.cell.2018.03.034 Cell Biochemistry & Molecular Biology Campanella et al., 2019 Clinical-grade computational pathology using weakly supervised deep learning on whole slide images 899 10.1038/s41591-019-0508-1 Nat. Med. Biochemistry & Molecular Biology Johnson et al., 2019 Survey on deep learning with class imbalance 892 10.1186/s40537-019-0192-5 J. Big Data Computer Science Ardila et al., 2019 End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography 828 10.1038/s41591-019-0447-x Nat. Med. Biochemistry & Molecular Biology Sirinukunwattana et al., 2016 Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images 706 10.1109/TMI.2016.2525803 IEEE Trans. Med. Imaging Engineering, Biomedical Ye et al., 2013 Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning 699 10.1038/nm843 Nat. Med. Biochemistry & Molecular Biology Bi et al., 2019 Artificial intelligence in cancer imaging: Clinical challenges and applications 698 10.3322/caac.21552 CA-Cancer J. Clin. Oncology Sun et al., 2018 A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study 638 10.1016/S1470-2045(18)30413-3 Lancet Oncol. Oncology Parmar et al., 2015 Machine Learning methods for Quantitative Radiomic Biomarkers 610 10.1038/srep13087 Sci Rep Multidisciplinary Sciences Yao & Liu, 1997 A new evolutionary system for evolving artificial neural networks 591 10.1109/72.572107 IEEE Trans. Neural Netw. Artificial Intelligence Litjens et al., 216 Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis 588 10.1038/srep26286 Sci Rep Multidisciplinary Sciences Rajpurka et al., 2018 Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists 545 10.1371/journal.pmed.1002686 PLos Med. Medicine, General & Internal Bera et al., 2019 Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology 525 10.1038/s41571-019-0252-y Nat. Rev. Clin. Oncol. Oncology (Bibliometrix & R software, 2023) For this study, Litmaps, an advanced science discovery platform known for its visual citation navigation, has been utilized. This platform offers an interface facilitating the exploration of scientific literature, enabling researchers to delve into the research terrain and uncover articles intricately linked within maps. Litmaps also presents convenient options for swiftly importing articles through various means such as reference manager, keyword search, ORCID ID, DOI, or by utilizing a seed article (Kaur et al., 2022). Litmaps helps researchers do the literature review very briefly and systematically. It helps find related or relevant studies through the seed paper. This will include some of your Seed Article's direct references and citations and some of their citations and references. Litmaps provides functionalities for visualizing literature maps that encompass pivotal articles relevant to specific research fields through diverse visualization modes. Notably, papers with higher citation counts are represented with larger circles, where the size of the node correlates proportionally to the logarithm of the citation count. In Figures 7a and 7b, Seed Maps show the top 20 citations and references related to a single article. 3.4 Sources (Journals) In Figure 7, Bradford's Law divides journals into three main classes and helps to find the core journals. First formulated in 1934, Bradford's Scatter Law "describes the scatter or distribution of literature on a particular topic across journals" (Garfield, 1980). According to this law, there should be an inverse relationship between the number of studies published on a topic and the number of journals in which they are published. Journals are divided into regions by ranking them according to the number of studies they publish. Although the number of journals in each region is not equal, the total number of publications in the regions will be equal. Because the productivity of journals is different from each other (Andres, 2009). As a result of the analysis, it was determined that the journals were divided into three regions. Figure 7 shows the sources in the first region. Figure 8a and Figure 8b show the trend of AI articles in oncology journals and reveal their decreasing or increasing trend over time in oncology journals. The results in Figure 8a and Figure 8b show the distribution of the journals with the most articles by year. Three leading journals publish the most articles on artificial intelligence and Oncology: Frontiers in Oncology (428 articles) and Scientific Reports and Cancer (284 and 247 articles, respectively). Although these three prominent journals contributed to 12% of the total articles, the remaining publications are widely distributed. This suggests that apart from the top three journals that publish the majority of insights in this research domain, a diverse array of other sources also significantly contributes to the literature When we look at the change in scientific journals over the years, it was first published in Scientific Reports in 2015. After 2020, a significant increase in publications in Artificial Intelligence (AI) applications in Oncology was observed. Frontiers in Oncology journal is leading this increase. As shown in Figure 8b, Scientific Journals began publishing artificial intelligence studies in the field of Oncology in 2015. It began to rise after 2018. In the early part of 2020, Scientigfic Reports was leading in publishing articles in this area, but after 2020, Frontiers in Oncology took the lead. Figure 15 shows that Frontiers in Oncology published these studies at significantly higher rates compared to other Journals. 3.5 Web of Science Categories According to the categorization within the Web of Science database, the articles were distributed across 147 scientific categories. However, nearly 70% of these articles predominantly fall within the scope of 10 major categories: Oncology (22%), Radiology, Nuclear Medicine Medical Imaging (13.4%), Engineering Biomedical (5.1%), Mathematical Computational Biology (5%), Engineering Electrical Electronic (4.8%), Computer Science Artificial Intelligence (4.7%), Computer Science Interdisciplinary Applications (4,7%) and Computer Science Information Systems (4.6%). Detailed category distribution is provided below (Table 4) Table 4: Distribution of Articles by Top 20 Web of Science Categories Name Web of Science Documents Times Cited Citation Impact Documents in Q1 Journals Documents in Q2 Journals Documents in Q3 Journals Documents in Q4 Journals ONCOLOGY 2021 49877 0.04 538 1006 195 114 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 1774 53085 0.03 763 550 239 51 ENGINEERING, BIOMEDICAL 493 14545 0.03 227 151 59 21 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 490 19532 0.03 159 136 58 33 MATHEMATICAL & COMPUTATIONAL BIOLOGY 477 8409 0.06 242 82 22 32 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 459 14204 0.03 276 83 39 10 ENGINEERING, ELECTRICAL & ELECTRONIC 440 10731 0.04 98 232 77 15 COMPUTER SCIENCE, INFORMATION SYSTEMS 418 6664 0.06 91 137 105 20 MEDICAL INFORMATICS 322 8318 0.04 147 67 59 11 BIOCHEMICAL RESEARCH METHODS 307 6115 0.05 125 111 40 18 BIOTECHNOLOGY & APPLIED MICROBIOLOGY 268 5080 0.05 100 59 61 7 MEDICINE, RESEARCH & EXPERIMENTAL 246 12342 0.02 88 38 44 28 BIOCHEMISTRY & MOLECULAR BIOLOGY 241 12785 0.02 119 75 31 9 COMPUTER SCIENCE, THEORY & METHODS 236 5488 0.04 65 90 20 3 HEALTH CARE SCIENCES & SERVICES 225 3944 0.06 76 75 31 11 GASTROENTEROLOGY & HEPATOLOGY 217 5324 0.04 84 60 31 21 CHEMISTRY, MULTIDISCIPLINARY 201 2710 0.07 27 82 86 2 SURGERY 193 2553 0.08 90 61 20 9 GENETICS & HEREDITY 165 2500 0.07 54 78 20 4 BIOLOGY 163 3630 0.04 101 38 16 3 (InCites, 2023) 3.6 Keywords The most frequent author keyword analysis was conducted to identify research hotspots and future research directions in the academic field. In this study, authors’ keyword co-occurrence visualization graph was created by the Bibliometrix & R software program (Figure 9). The top 20 author keywords were shown. The most frequent author keywords in the dataset are machine learning, deep learning, radiomics, artificial intelligence, breast cancer, magnetic resonance imaging, and prostate cancer (Fig. 9). Keyword co-occurrence analysis serves as a method to comprehend the primary themes or topics within a research field. Co-occurrence signifies the joint appearance of two pieces of information within a dataset. Each keyword within the dataset is represented as a node, while the co-occurrence of a pair of keywords is depicted as a link. The strength of this link is determined by the frequency of appearance of the paired keywords together (Radhakrishnan et al., 2017). This study employs Author Keywords for conducting keyword co-occurrence analysis. Author Keywords are automatically generated using a proprietary algorithm unique to Clarivate Analytics databases. The keywords associated with Artificial Intelligence (AI) applications in Oncology research are categorized into four clusters, denoted by four distinct colors (Figure 10). Each circle within the figure represents a keyword, and the lines connecting these circles signify the connections between the keywords. Keywords sharing the same color belong to the same cluster. The size of each circle in the figure corresponds to the frequency of the keyword: larger circles indicate higher frequency, while smaller circles denote lower frequency. Figure 10 shows the visualization network map of author keywords co-occurrence. Four Clusters are formed weights based on occurrences. The red color indicates Cluster 1 (radiomics, magnetic resonance imaging, computer tomograph, etc.); the green color indicates Cluster 2 (machine learning, prediction, artificial neural network, etc.); the blue color indicates Cluster 3 (deep learning, breast cancer, artificial intelligence, etc.); the yellow color indicates Cluster 4 (cancer, feature extraction, image classification, etc.). 3.7 Countries & Universities Figure 11 shows the institution collaboration network based on authors. five main clusters of institutions were identified: mostly universities from China (see blue) and universities from the USA (see red). Chinese institutes constitute a majority of the entities involved, with prominent institutions like the Chinese Academy of Science and Shanghai Jiao Tong University standing out. These institutions are noted for their high activity and collaboration in the realm of AI articles related to oncology. The USA is the second-largest cluster developed in the collaboration network. The institutes in this cluster, including Harward University and the University of Texas System, are the main ones. Figure 12 shows the top 20 institutions in terms of publications. The co-authorship analysis showed that 51 institutions published more than five papers. Figure 13 shows that only five clusters are formed; purple color indicates Cluster 1 (China, France, Taiwan, etc.); yellow color indicates Cluster 2 (USA, Germany, Italy, etc.); blue color indicates Cluster 3 (Netherlands, England, Spain, etc.); red color indicates Cluster 3 (South Korea, India, Egypt, Saudi Arabia, etc.); green color indicates cluster 5 (Japan, Australia, Norway, etc.) From the country/region perspective, 106 countries/regions have participated in the article publications. China involves 1001 articles, accounting for 28.34% of the total publications, followed by the USA (25.62%), India (6.63%), South Korea (6.40%), and the United Kingdom (5.63%) (figure 14) Figure 14 presents the top 20 countries based on the number of articles published, with a categorization by Multiple Country Publication (MCP) and Single Country Publication (SCP). Multiple Country Publication refers to collaborative works involving authors from different countries, indicating international collaboration. Conversely, Single Country Publication denotes works where all authors belong to the same country, signifying intra-country collaboration. Chinese authors lead in productivity with 2508 articles, comprising 2071 single-country publications and 437 multiple-country publications, resulting in an MCP ratio of 17.4%. On the other hand, authors from Turkey exhibit the lowest ratio of multiple-country publications, contributing a total of 96 articles, with only 11 being single-country publications. For more detailed information, refer to Figure 14 and Table 5. Figure 14 further illustrates that the majority of publications are authored by individuals from the same countries. This trend might arise from authors' preferences to collaborate within their research groups or with academics sharing the same national background. Table 5: Corresponding author's country and between-country collaboration Country Articles SCP MCP Freq MCP_Ratio CHINA 2508 2071 437 0.323 0.174 USA 1428 994 434 0.184 0.304 INDIA 461 383 78 0.059 0.169 KOREA 422 343 79 0.054 0.187 JAPAN 286 251 35 0.037 0.122 GERMANY 263 145 118 0.034 0.449 ITALY 253 187 66 0.033 0.261 UNITED KINGDOM 222 108 114 0.029 0.514 CANADA 180 117 63 0.023 0.35 NETHERLANDS 155 70 85 0.02 0.548 FRANCE 130 81 49 0.017 0.377 IRAN 104 69 35 0.013 0.337 TURKEY 96 85 11 0.012 0.115 AUSTRALIA 95 40 55 0.012 0.579 SAUDI ARABIA 85 41 44 0.011 0.518 EGYPT 79 42 37 0.01 0.468 SPAIN 77 52 25 0.01 0.325 PAKISTAN 61 13 48 0.008 0.787 SWEDEN 56 31 25 0.007 0.446 SWITZERLAND 48 20 28 0.006 0.583 (Bibliometrix & R software, 2023) 3.8 Funding According to the results presented in Table 6, the National Natural Science Foundation, with 1045 studies, and the Department of Health & Human Services, with 656 studies, had the highest support for the publication of scientific research on Artificial Intelligence for cancer detection. The National Natural Science Foundation, Department of Health & Human Services, NIH National Cancer Institute, US Department of Health and Human Services significantly improve artificial intelligence for cancer detection. Table 6: Distribution of Articles according to the international organizations that funded them Funding Web of Science Documents Times Cited International Collaborations Domestic Collaborations Documents in Q1 Journals Documents in Q2 Journals Documents in Q3 Journals Documents in Q4 Journals National Natural Science Foundation of China 1045 18886 229 626 472 417 109 22 Department of Health & Human Services-USA 656 22221 251 264 353 196 34 6 National Institutes of Health (NIH)-USA 649 21919 248 263 350 195 33 6 NIH National Cancer Institute (NCI)-USA 270 9855 97 118 141 81 15 2 National Research Foundation of Korea 205 2843 35 130 96 83 8 12 National Science Foundation-USA 102 2549 43 35 56 24 6 1 Ministry of Education Culture Sports Science and Technology-Japan 89 1155 29 45 31 39 9 2 Japan Society for the Promotion of Science 87 1121 29 43 31 38 9 2 Ministry of Science ICT & Future Planning-Republic of Korea 86 1155 15 56 40 37 3 3 European Union-EU 83 1964 56 18 43 29 3 1 Fundamental Research Funds for the Central Universities-China 81 1789 25 41 36 34 10 0 National Natural Science Foundation of Guangdong Province-China 77 1665 11 56 36 30 6 2 Grants-in-Aid for Scientific Research-Japan 72 895 20 38 24 33 8 1 UK Research & Innovation-UK 71 3290 41 24 45 18 0 1 Beijing Natural Science Foundation-China 68 1818 16 46 32 31 5 0 Spanish Government 66 3096 29 25 42 18 2 0 Ministry of Science and Technology-Taiwan 64 960 12 49 34 23 6 0 German Research Foundation (DFG)-Germany 61 2074 35 23 40 15 3 1 Ministry of Science & ICT-Republic of Korea 61 733 11 35 26 27 4 1 China Postdoctoral Science Foundation 60 674 16 38 34 24 1 0 Natural Sciences and Engineering Research Council of Canada 56 1602 20 25 26 21 4 0 European Research Council 45 3609 31 8 30 13 0 0 Medical Research Council UK 44 2902 25 17 29 11 0 1 Natural Science Foundation of Zhejiang Province-China 42 713 18 21 20 19 3 0 Canadian Institutes of Health Research 40 1379 11 24 21 13 0 0 (InCites, 2023) As shown in Figure 15, countries began supporting artificial intelligence studies in the field of Oncology in 2014. It began to rise after 2016. In the early part of 2016, the National Institutes of Health (NIH) in the USA was leading in supporting studies in this area, but after 2019, the National Natural Science Foundation of China (NSFC) took the lead. Figure 15 shows that NSFC supported these studies at significantly higher rates compared to other funding organizations. It's noticeable that the expected support from NIH's National Cancer Institute lagged behind. As seen in previous graphs and tables, it demonstrates how extensively China has invested in this field. 4. Discussion In recent years, artificial intelligence (AI) has swiftly become an integral part of the medical field, particularly in cancer detection. This study utilized the bibliometrix package of R software and Litmaps visualization software to conduct a thorough bibliometric analysis of AI applications in oncology research over the past 30 years. The objective was to provide a comprehensive understanding of the field. Our analysis objectively and systematically outlined the current status of AI applications, identified developmental trends, and highlighted potential research focal points in cancer detection. This endeavor facilitates scholars in rapidly comprehending the research landscape and offers valuable insights for selecting research topics. The initial phase of the study examined publication trends, covering aspects such as countries, institutions, authors, and journals. Subsequently, cluster analysis was applied to keywords to identify research hotspots within the field. Based on the analysis of publication trends, there has been a significant surge in the number of publications on AI in Oncology over the past four years. China and the United States emerged as the leading nations regarding the volume of publications in this field. Citation counts, widely recognized as an indicator of professional acknowledgment in scientific work, were extensively used to assess research quality. The United States stood out in terms of both citation counts and international collaborations, with a considerable lead over other countries. Additionally, the university contributing the most publications and citations was based in China, underscoring China's pivotal role and global leadership in this domain. Despite China's considerable volume of publications, the relatively low citation counts suggest a need to enhance the quality and impact of its research. This could be attributed, in part, to the later initiation of AI in Oncology research in China, resulting in comparatively lower international academic influence. Notably, Jie Tian and Zaiyi Liu from China emerged as the most published authors, contributing to 10.3% of the publications. Regarding citations, Hugo Aerts from Stanford University and Robyn Gillies from the University of Washington in the United States received the highest recognition. These findings underscore the importance of quantity, quality, and global impact in advancing AI research in cancer detection. This is the newest bibliometric study that provides detailed information about published literature on the AI in Oncology. The most active institutions were the Chinese Academy of Science and Harward University, and the most productive countries were China and the USA. The most frequently co-occurrence author keywords were radiomics, machine learning, artificial intelligence, and breast cancer. The outcomes of this study hold value for researchers, policymakers, and educational purposes. Additionally, they offer assistance to funding agencies in evaluating current research trajectories and anticipating future trends in AI within Oncology. Effective AI development and treatment therapy is still a hot zone for future research directions. Three leading journals publish the most articles on artificial intelligence and Oncology: Frontiers in Oncology (428 articles) and Scientific Reports and Cancer (284 and 247 articles, respectively). While these three leading journals accounted for 12 % of total articles, the remaining list is well-distributed. The majority, almost 70%, of the articles fit into ten significant categories: Oncology (22%), Radiology Nuclear Medicine Medical Imaging (13.4%), Engineering Biomedical (5.1%), Mathematical Computational Biology (5%), 5. Conclusion Articles about extracting meaning from radiological/microscopic/real patient images in cancer patients using artificial intelligence have been produced for approximately six years and have reached a certain maturity. It is understood that some obstacles are related to deriving meaning from genomic/genomic/proteomic data and doctor notes written in text. As new findings emerge, the association of diseases and treatments using existing classification systems with genomic/proteomic data should be expected to increase geometrically/exponentially. In order to derive meaning from clinical data, it is necessary to create new databases of the NoSQL type for transferring values from biochemistry, tumor markers, drug doses, as well as names of drugs/materials/devices, and text notes written manually with pen or keyboard into artificial intelligence software. Therefore, it is expected that big data derived from electronic health records should be reprocessed and re-archived by developing new standards. It is understood that the CancerLinq database has not contributed to machine learning-related publications so far and may not be able to do so in its current state. It is observed that despite fewer available parameters for individuals in the previously established SEER database, more publications related to machine learning have been made. This situation may stem from a structural difference between the CancerLinq and SEER databases. The exploration of artificial intelligence in Oncology is in its early phases but is anticipated to progress rapidly. Researchers are currently investigating AI applications in various aspects of Oncology within the medical field, including medicine, diagnosis, therapy, and risk assessment. Implementing artificial intelligence proves effective in mitigating human errors and enhancing work efficiency. This bibliometric study offers a comprehensive overview of AI in Oncology research, focusing on the discipline's current state. This perspective assists researchers in identifying critical areas of interest, cutting-edge developments, and emerging research directions within the field. Declarations ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. CONFLICT OF INTEREST STATEMENT The authors declare that they have no conflict of interest. DATA AVAILABILITY STATEMENT The data used in the paper is publicly available. Author Contributions (According to ICMJE) Compliance with Ethical Standards (According to COPE) Disclosure of potential conflicts of interest: None Research involving Human Participants and/or Animals: None Informed consent: Not Applicable Institutional Review Board Statement: Not Applicable Funding Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). Data Availability No datasets were generated or analysed during the current study. Declarations Ethical Approval No ethical approval was needed because this is not a human study, but only online information was used. References Andres, A. (2009). Measuring Academic Research: How to Undertake a Bibliometric Study [Book]. 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Scientometrics , 84 (2), 523-538. https://doi.org/10.1007/s11192-009-0146-3 Web of Science (2022). Retrieved November 21, 2023, from https://www.webofscience.com/wos/woscc/summary/d395368a-6372-4770-ba65-01c2a114dea3-b5fc6c6e/times-cited-descending/1 Zhang, C., Qi, L. S., Cai, J., Wu, H. X., Xu, Y., Lin, Y. L., . . . Ma, W. J. (2023). Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence [Article]. Bmc Cancer , 23 (1), 16, Article 239. https://doi.org/10.1186/s12885-023-10704-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4260599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292130589,"identity":"5967085e-2212-4e5e-b8b1-73c6af963f8a","order_by":0,"name":"Murat 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Oncology\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/8edf5ac15423aaeb075504b0.png"},{"id":55080054,"identity":"77b807ec-42c3-4086-9269-a3a1094d5d42","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32878,"visible":true,"origin":"","legend":"\u003cp\u003eLotka's Law and Author Productivity Rate\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/2b96424dba65845f3ddff9e2.png"},{"id":55080059,"identity":"69c3737e-89c2-472d-9164-a58b377c380c","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167129,"visible":true,"origin":"","legend":"\u003cp\u003ea: Top 20 Authors published the most AI articles in oncology from 1992 to 2022.\u003c/p\u003e\n\u003cp\u003eb: Top 20 Authors published the most AI citations in oncology from 1992 to 2022.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/fd7cc999d27325d40ae87976.png"},{"id":55080058,"identity":"1eb1a422-6b10-4524-b558-876c0f71e852","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":236846,"visible":true,"origin":"","legend":"\u003cp\u003eHistoriography of AI articles in oncology from 1992 to 2022.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/79a9fa04af890b3daa69fa89.png"},{"id":55080062,"identity":"ca375e20-71a2-4709-8f80-ca1636ba3665","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":142285,"visible":true,"origin":"","legend":"\u003cp\u003ea: Seed Maps of Esteva (2017)\u003c/p\u003e\n\u003cp\u003eb: Seed Maps of \u0026nbsp;Gillies (2016)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/c883a0b7cc693aab9a0d0c62.png"},{"id":55080415,"identity":"53da5bd1-9077-43f1-b450-6ea3a76b2a44","added_by":"auto","created_at":"2024-04-22 09:39:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":109097,"visible":true,"origin":"","legend":"\u003cp\u003ea: Distribution of the scientific journals publishing the most articles in artificial intelligence and Oncology according to Bradford's law\u003c/p\u003e\n\u003cp\u003eb: Distribution of the journals with the most articles by year.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/87d1d07c96030f24fdc0074a.png"},{"id":55080061,"identity":"a742c1c5-5128-48f7-aa7b-36d9eaf8a2c3","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":118127,"visible":true,"origin":"","legend":"\u003cp\u003eMost Frequent Author Keywords in the AI and Oncology literature, 1992–2022\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/c160f826aeccd7520ca397bd.png"},{"id":55080417,"identity":"5cc4d999-be0e-4616-a188-30e4bca63767","added_by":"auto","created_at":"2024-04-22 09:39:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1275182,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork map of author keywords co-occurrence that appeared 12+ times in the artificial intelligence and Oncology literature, 1992–2022\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/63629248634268dbe74f5132.png"},{"id":55080067,"identity":"43237206-188b-4c2c-83ad-e4f1fdd966f0","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":333702,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of institutions among articles with authors affiliated with institutions\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/9f05db1a86741173cafd6920.png"},{"id":55080066,"identity":"da197d27-df08-4e95-a81f-ce36ba292eff","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":161361,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of author affiliations to organizations based on the selected dataset (top 20 organizations)\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/29b7a932c95c97d7fc9e0999.png"},{"id":55080064,"identity":"887a789c-3dcc-4b2d-8ea1-03d7c56920ac","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1105163,"visible":true,"origin":"","legend":"\u003cp\u003eCo-authorship country visualization network map\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/f4f028262ee7d36a70a14905.png"},{"id":55080805,"identity":"fdecf68d-343e-421a-94cf-73b6f2e31318","added_by":"auto","created_at":"2024-04-22 09:47:59","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":73064,"visible":true,"origin":"","legend":"\u003cp\u003eMost productive countries: Single Country Publications (SCP), Multiple Country Publications (MCP).\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/3116490dae8f46fb51a586d7.png"},{"id":55080065,"identity":"e5f634af-71e8-4214-8864-c8fdaa86820c","added_by":"auto","created_at":"2024-04-22 09:31:59","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":140197,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the Distribution of the Articles According to the Institutions Supporting the Articles by Years.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/ba3a7268521215c89b2cdbf8.png"},{"id":55691687,"identity":"e7f39f2f-9a55-4613-b891-c8afbcd6f5a5","added_by":"auto","created_at":"2024-05-01 23:31:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4712354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4260599/v1/349ca144-34c4-4ffd-a507-acbf9e994b52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development Trends and Knowledge Framework of Artificial Intelligence (AI) Applications in Oncology by years: A Bibliometric Analysis from 1992 to 2022","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial intelligence is based on creating machines that think like humans and is seen as the apotheosis of science (McCorduck, 1982). Artificial intelligence (AI), which has emerged in different fields in the twenty-first century, has become a trend in many fields, such as medicine, science, and business (Jarek \u0026amp; Mazurek, 2019) and has manifested itself in medicine.\u003c/p\u003e\n\u003cp\u003eIt is possible to categorize medical data that can be assessed with deep learning/machine learning/artificial intelligence into genetic, imaging, and clinical data. The images may be real-size photographs of visible parts of the human body and internal parts of the human body obtained by devices such as computed tomography, x-ray, magnetic resonance, and endoscopic devices. The field that deals with images of the inside of the human body obtained with such devices is called radiomics. In 2009, \u0026quot;Radiogenomics: Creating a link between Molecular Diagnostics and Diagnostic Imaging\u0026quot; (Rutman et al., 2009) and \u0026quot;Radiomics: Images Are More than Pictures, They Are Data\u0026quot; (Gillies \u0026nbsp;et al., 2016) in, the term radiomics started to appear in the literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompetitions using The Pascal Visual Object Classes Challenge and Imagenet datasets have been held worldwide to recognize highly accurate images by electronic systems. In 2012, AlexNet software, which participated in the competition under the name Supervision, won this competition by a large margin, and the paper \u0026quot;ImageNet classification with deep convolutional neural networks,\u0026quot; published by the authors (Krizhevskyet al., 2012), was an important milestone. Please note that, Ilya Sutskever is a major contributor to ChatGPT, a large language model based AI software. The article \u0026quot;Radiomics: images are more than pictures, they are data\u0026rdquo; (Gillies \u0026nbsp;et al., 2016), drew attention to the fact that meaningful results can be obtained primarily in cancer patients by converting radiologic images into numerical data. Publications on the ability of Alexnet-type software to successfully evaluate patient-related images have started to appear since 2017 and have increased steadily.\u003c/p\u003e\n\u003cp\u003eMagnified images of cells in the human body tissues can be obtained \u003cs\u003ein real-time\u003c/s\u003e with devices such as endoscopy, or cells extracted from the body by biopsy can be examined by various processes and staining. The field that deals with microscopic images of tissues are called pathomics (Hou L, 2016). There are very few publications using the term pathomics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic data can be related to DNA gene sequences or protein structures. The field dealing with gene sequences is called genomics and the field dealing with protein structures is called proteomics. The highly cited publications on gene sequencing and cancer started even before radiomics and received many citations (Khan et al., 2001). Genetic data can be about DNA gene sequences or protein structures. The field that deals with gene sequences is called genomics, and the field that deals with protein structures is called proteomics. Highly cited publications on gene sequencing and cancer started even before radiomics and were highly cited (Khan et al., 2001; Shipp et al.,2002 ). Although not cited as genomics, publications on proteomics started appearing before radiomics (Chen et al., 2004).\u003c/p\u003e\n\u003cp\u003eWhile the name clinicomics seems appropriate for evaluating clinical data with machine learning (Zhang et al., 2023; Rennard \u0026amp; Stoner, 2005; Maojo et al., 2005), the term has only recently been used. In the USA., clinical data obtained from hospitals\u0026apos; electronic health records (EHR) are anonymized / de-identified in a CancerLinq database and re-recorded so that meaningful conclusions can be drawn and used by users who want to benefit from it. (Schilsky et al., 2014; Rubinstein \u0026amp; Warner, 2018 ). Unlike the SEER database (https://seer.cancer.gov, an extensive anonymized cancer patient database), where fewer parameters about a patient are stored, it should be considered to allow for the evaluation of individualized diagnoses and treatments. Clinicomics may attempt to extract meaningful data from text based clinical records and is expected to utilize large language model AI frameworks.\u003c/p\u003e\n\u003cp\u003eSince it is necessary to use one of the research methods appropriate for evaluation and prediction, bibliometric analysis is one of the analyses that serves this purpose. Bibliometrics is used as a type of analysis that systematically analyzes datasets (Donthu et al., 2021). This type of analysis requires a systematic literature review and meaningful structuring of a large dataset (Donthu et al., 202). Bibliometric analysis is a method used to analyze the studies conducted in a specific field, and its use has increased in recent years.\u003c/p\u003e\n\u003cp\u003eWhile bibliometric analysis in this concept is seen as inevitable for many fields (Synder, 2019), it is necessary to examine academic studies on the impact of artificial intelligence applications in medicine through bibliometric analysis. This is because AI\u0026apos;s increasing popularity and widespread use cause the subject to become a need in terms of health. This study analyzed 7923 articles published between 1992 and 20222 with the R program, biblioshiny package program, and VOSviewer software(Van Eck \u0026amp; Waltman, 2010). The article contributed to determining the techniques used in the cumulative information in the relevant research, thematic analysis, and central trends. It sheds light on the research to be conducted. In this context, the study will first include the literature on artificial intelligence and cancer, then the data used and the research method will be mentioned.\u003c/p\u003e\n\u003cp\u003eKarger (2023) provides a bibliometric analysis of research on AI for cancer detection, emphasizing its growth and potential for early detection. Khanam and Kumar (2022) explore the recent applications of AI algorithms, including machine learning and deep learning, in the early detection of various cancers. Pacurari et al. (2023) focus on using AI techniques, such as support vector machines and neural networks, to detect and classify lung, breast, and brain cancers using medical imaging. Overall, these papers highlight the importance of AI in improving Oncology and emphasize the need for further research in this field.\u003c/p\u003e\n\u003cp\u003eThe objective of this research is to address specific inquiries regarding the utilization of Artificial Intelligence (AI) in Oncology.\u003c/p\u003e\n\u003cp\u003ea) Sources: Which scientific journals are most influential in the field?\u003c/p\u003e\n\u003cp\u003eb) Researhers: Which Authors are the most influential in the field of Artificial Intelligence (AI) applications in Oncology?\u003c/p\u003e\n\u003cp\u003ec) Papers: Which papers about Artificial Intelligence (AI) applications in Oncology?\u003c/p\u003e\n\u003cp\u003ed) Keywords: What are the most popular authors keywords in Artificial Intelligence (AI) applications in Oncology research?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ee) How have the themes of Artificial Intelligence (AI) applications in Oncology evolved?\u003c/p\u003e\n\u003cp\u003ef) Funding: Which fundings are the most influential in the field of Artificial Intelligence (AI) applications in Oncology?\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe relevant subject is examined statistically and mathematically in Bibliometrics, and a framework is drawn for the course. In addition, bibliometric analysis can also reveal the effectiveness of studies conducted through statistical data (Broadus, 1987). The data for this study were retrieved from the online database Web of Science on 21.11.2023. The reasons for using the Web of Science database instead of Scopus Google Scholar for bibliometric analysis are that it is the most extensive database in abstracts and literature, it can produce information with better actions, decisions, and results (Web of Science, 2022), it is a valuable resource for bibliometric studies, and it offers a comprehensive perspective on publications in the fields of science, technology, art, medicine, and social sciences. The Web of Science database used in bibliometric analysis is preferred (Mart\u0026iacute;n et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe search strategy of this study is as follows: While obtaining the data of the study, the first step was to search the Web of Science database (TI=(\u0026quot;deep-Learn*\u0026quot; OR \u0026quot;machine learn*\u0026quot; OR \u0026quot;deep learn*\u0026quot;OR \u0026quot;artificial intelligence\u0026quot; OR \u0026quot;artificial neural network*\u0026quot; OR \u0026quot;deep neural network*\u0026quot; OR radiomics OR pathomics )) AND (AB=(melanoma OR cancer OR malignancy OR leukemia OR lymphoma OR neoplasia OR SEER* OR \u0026quot;Surveillance, Epidemiology, and End Results\u0026quot; OR \u0026nbsp;CancerLinQ*)) AND (PY=1992-2022) AND (DT=Article).\u003c/p\u003e\n\u003cp\u003eData from databases were directly accessed to retrieve metadata from chosen documents, encompassing details like active authors, journal sources, countries, institutions, and funding sources. The analysis involved employing tools such as Bibliometrix package (version 3.1.4, http://www.bibliometrix.org) in R software (version 3.6.3), VOSviewer, and Litmaps to generate comprehensive bibliometric insights. Initially, raw data in plain text format was loaded and processed, involving calculations and visual representations of metadata comprising sources, authors, and citations. This was followed by intricate analyses focusing on clustering and the conceptual framework encompassing intelligence and society. Additionally, a comprehensive bibliometric evaluation was carried out, encompassing co-authorship and keyword co-occurrence using the VOSviewer software (Van Eck \u0026amp; Waltman, 2010).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eWhen 7923 articles were reviewed, although the importance of genetic data in cancer diagnosis is significant, there are relatively few publications in machine learning and genomics fields, comprising only 2.2% (174 articles) of the studies in this work. There are only 12 articles related with pathomics, and deep learning in WOS database. The term \u0026apos;Clinicomics\u0026apos; has not yet been established and has not found a place within the article set of this study. When searching for the term \u0026apos;Clinical informatics,\u0026apos; only four articles were found; \u0026apos;Clinical data extraction\u0026apos; yielded 1.6% (124 articles), \u0026apos;SEER\u0026apos; resulted in 0.90% (71 articles), and \u0026apos;NLP\u0026apos; led to 28 articles. Only 1 article containing the term \u0026apos;CancerLing\u0026apos;(Heilbroner et al., 2021) was found. Only three articles can be found when the keywords \u0026apos;CancerLinq\u0026apos; and \u0026apos;machine learning\u0026apos; are searched across all fields in the WOS database. As 1716 articles were found with the term \u0026apos;Radiomics\u0026apos; and 2215 articles with the term \u0026apos;image,\u0026apos; we can conclude that the most commonly used datasets related to the topic of this article are primarily in the form of images.\u003c/p\u003e\n\u003ch2\u003e3.1 General information\u003c/h2\u003e\n\u003cp\u003eThe findings of the bibliometric analysis conducted within the scope of the study will be presented in this section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 provides fundamental details about the dataset, encompassing its size, descriptive statistics, content overview, and statistics regarding authorship and collaborative efforts. It has 7923 documents over the timespan of 1992\u0026ndash;2022. 7923 journal articles were extracted from the Web of Science database. This downloaded database was then thoroughly analyzed and examined with the help of the Bibliometrix and R software (2023) application. At the early stage of this study, a descriptive analysis was provided to examine the details of the work published in this area(Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: General information about the publications analyzed in the Artificial Intelligence (AI) and Oncology Dataset\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"505\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003eResults\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMain Information About Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eTimespan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e1992:2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eSources (Journals, Books, etc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e1592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e7923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eAnnual Growth Rate %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eAverage citations per doc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDocument Contents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eKeywords Plus (ID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e7443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eAuthor\u0026apos;s Keywords (DE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e10966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e35198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eAuthors of single-authored docs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eCo-Authors per Doc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.42574257425743%\" valign=\"bottom\"\u003e\n \u003cp\u003eInternational co-authorships %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.574257425742573%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Bibliometrix \u0026amp; R software, 2023)\u003c/p\u003e\n\u003cp\u003eTable 1 shows that the analyzed studies consist of 7923 publications published between 1992 and 2022 and are based on the statistics of publications on oncology using artificial intelligence methods. Figure 1 shows the quantity of AI-related articles within the oncology domain. Overall, there\u0026apos;s been a consistent upward trend in publications from 1992 to 2022. Particularly in the last decade, there has been a surge in global interest within the research field. Between 1994 and 2014, publications remained relatively low and steady. However, since AI gained prominence around 2016, there has been a notable and substantial increase in the number of publications.\u003c/p\u003e\n\u003cp\u003eThe trend of the journal impact factor quartile of articles is shown in Figure 2. From 2016 to 2021, the number of documents in Q1 articles showed a fast growth trend; after 2021, the number of documents in Q1 has gone steadily. For the first time in 2021, the number of articles in the Q2 category exceeded that in the Q1 category. This means that higher-quality journals are starting to lower acceptance rates in this field. The lowest rate in this area is for articles in the Q4 category. This indicator shows the popularity of this field.\u003c/p\u003e\n\u003cp\u003eThe Web of Science was used to meticulously define and classify citation topics. All findings from the search query were included in the review without further filtration. The citation topics were narrowly focused and categorized according to the recently published classifications by the Web of Science, encompassing over 2500 detailed citation topics. This classification operates hierarchically below the Web of Science subject categories and citation topics at a broader level, enabling a precise and unbiased evaluation of the technologies utilized in the search query.\u003c/p\u003e\n\u003cp\u003eOur datasets were determined and ranked based on the citation topic micro criteria in the WoS Citation index. The most represented citation topics micro based on our datasets were \u0026quot;glioblastoma\u0026quot;, \u0026quot;breast cancer,\u0026quot; \u0026quot;prostate cancer,\u0026quot; \u0026quot;lung cancer,\u0026quot; \u0026ldquo;melanoma\u0026rdquo; \u0026quot;rectal cancer,\u0026quot; and \u0026quot;gastric cancer.\u0026quot; Various cancer types are distributed among the article publications related to cancer research. The analysis of cancer types investigated provides insight into the present landscape of cancer research employing artificial intelligence techniques. As depicted in Figure 3, within this decade, the most prevalent studies have focused on glioblastoma (539 articles) and breast cancer (488 articles), with prostate cancer (345 articles) following closely in frequency.\u003c/p\u003e\n\u003ch2\u003e3.2 Authors\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn Table 2 and Figure 4, Lotka\u0026apos;s Law reveals the quantitative distribution of the publications of authors who contribute to the literature on a particular subject in the literature of that field. With this, the scientific productivity of the authors was tried to be revealed. Lotka\u0026apos;s Law predicts that 70% of the authors who publish on a subject contribute to the subject with one publication, 15% with two publications, and 7% with three publications (Rowland, 2005). It can be seen that the data obtained as a result of the analysis also complies with Lotka\u0026apos;s Law. According to Table 2, 78% of the authors made one publication, 13% made two, and 4% made three.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Lotka\u0026apos;s Law and Author Productivity Ratio\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"517\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003eDocuments written\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003eN. of Authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003eProportion of Authors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e25587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e4999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e1799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.46228239845261%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.19922630560929%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Bibliometrix \u0026amp; R software, 2023)\u003c/p\u003e\n\u003cp\u003eFigure 5a shows the main statistical characteristics of the Top 20 authors ranked by number of articles. When the graph is analyzed, Tian, Jie, and Liu Zaiyi are the leading authors working on artificial intelligence and Oncology. The author\u0026apos;s publications cover 17.5 percent of the total publications.\u003c/p\u003e\n\u003cp\u003eFigure 5b shows the main statistical characteristics of the Top 20 authors ranked by number of citations. When the graph is analyzed, Aerts Hugo and Gillies Robyn are the leading authors working on artificial intelligence and oncology. The author\u0026apos;s number of citations covers 22.7 percent of the total citations.\u003c/p\u003e\n\u003cp\u003eFigure 6 shows the bibliometric historiography using the science mapping tool bibliometrix (Aria and Cuccurullo, 2017). Initially, this mapping process establishes the historical direct citation network starting from the most-cited work, subsequently visualizing the network in chronological order (Garfield, 2004). The subsequent subsections will detail these networks, proceeding from the earliest to the most recent. As seen in Figure 6, the radomics article by Gillies (2016), Robyn is one of the first articles to show the importance of using artificial intelligence in cancer detection. Therefore, it is at the center of the Historiography graph.\u003c/p\u003e\n\u003ch2\u003e3.3 Papers\u003c/h2\u003e\n\u003cp\u003eThe top 25 most widely cited articles are presented in Table 3. The article was published by Esteva A in Nature in 2017 (Total citations = 5900), followed by Gillies (2016) in Radiology in 2016 (total citations = 4362), and Lambin (2012) published in the European Journal of Cancer in 2012 with 3018 citations. Hence, an annual average citation has been included in Table 3 to aid researchers in promptly identifying recently published highly cited papers. These high-impact publications, in essence, offer a swift overview of the field and expand researchers\u0026apos; perspectives.\u003c/p\u003e\n\u003cp\u003eTable 3: Distribution of Most Cited AI Articles in Oncology from 1992 to 2022\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArticle Title\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e#citation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eJournal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWoS Categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eEsteva et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eDermatologist-level classification of skin cancer with deep neural networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1038/nature21056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eNature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMultidisciplinary Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eGillies et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eRadiomics: Images Are More than Pictures, They Are Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e4362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1148/radiol.2015151169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eLambin et al., 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eRadiomics: Extracting more information from medical images using advanced feature analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e3018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1016/j.ejca.2011.11.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eEur. J. Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eOncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eAerts et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1038/ncomms5006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eNat. Commun.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMultidisciplinary Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eVan Griethuysen et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eComputational Radiomics System to Decode the Radiographic Phenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1158/0008-5472.CAN-17-0339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eCancer Res.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eOncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eKhan et al., 2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eClassification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1038/89044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eNat. Med.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eBiochemistry \u0026amp; Molecular Biology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eShipp et al., 2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eDiffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1038/nm0102-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eNat. Med.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eBiochemistry \u0026amp; Molecular Biology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eBejnordi et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eDiagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1001/jama.2017.14585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eJAMA-J. Am. Med. Assoc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMedicine, General \u0026amp; Internal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eKumar et al.,2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eRadiomics: the process and the challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1016/j.mri.2012.06.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eMagn. Reson. Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eZwanenburg et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eThe Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1148/radiol.2020191145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eCoudray et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eClassification and mutation prediction from non-small cell lung cancer histopathology images using deep learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10.1038/s41591-018-0177-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eNat. 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Rev. Clin. Oncol.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eOncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Bibliometrix \u0026amp; R software, 2023)\u003c/p\u003e\n\u003cp\u003eFor this study, Litmaps, an advanced science discovery platform known for its visual citation navigation, has been utilized. This platform offers an interface facilitating the exploration of scientific literature, enabling researchers to delve into the research terrain and uncover articles intricately linked within maps. Litmaps also presents convenient options for swiftly importing articles through various means such as reference manager, keyword search, ORCID ID, DOI, or by utilizing a seed article (Kaur et al., 2022).\u003c/p\u003e\n\u003cp\u003eLitmaps helps researchers do the literature review very briefly and systematically. It helps find related or relevant studies through the seed paper. This will include some of your Seed Article\u0026apos;s direct references and citations and some of their citations and references. Litmaps provides functionalities for visualizing literature maps that encompass pivotal articles relevant to specific research fields through diverse visualization modes. Notably, papers with higher citation counts are represented with larger circles, where the size of the node correlates proportionally to the logarithm of the citation count. In Figures 7a and 7b, Seed Maps show the top 20 citations and references related to a single article.\u003c/p\u003e\n\u003ch2\u003e3.4 Sources (Journals)\u003c/h2\u003e\n\u003cp\u003eIn Figure 7, Bradford\u0026apos;s Law divides journals into three main classes and helps to find the core journals. First formulated in 1934, Bradford\u0026apos;s Scatter Law \u0026quot;describes the scatter or distribution of literature on a particular topic across journals\u0026quot; (Garfield, 1980). According to this law, there should be an inverse relationship between the number of studies published on a topic and the number of journals in which they are published. Journals are divided into regions by ranking them according to the number of studies they publish. Although the number of journals in each region is not equal, the total number of publications in the regions will be equal. Because the productivity of journals is different from each other (Andres, 2009). As a result of the analysis, it was determined that the journals were divided into three regions. Figure 7 shows the sources in the first region.\u003c/p\u003e\n\u003cp\u003eFigure 8a and Figure 8b show the trend of AI articles in oncology journals and reveal their decreasing or increasing trend over time in oncology journals. The results in Figure 8a and Figure 8b show the distribution of the journals with the most articles by year. Three leading journals publish the most articles on artificial intelligence and Oncology: Frontiers in Oncology (428 articles) and Scientific Reports and Cancer (284 and 247 articles, respectively). Although these three prominent journals contributed to 12% of the total articles, the remaining publications are widely distributed. This suggests that apart from the top three journals that publish the majority of insights in this research domain, a diverse array of other sources also significantly contributes to the literature\u003c/p\u003e\n\u003cp\u003eWhen we look at the change in scientific journals over the years, it was first published in Scientific Reports in 2015. After 2020, a significant increase in publications in Artificial Intelligence (AI) applications in Oncology was observed. Frontiers in Oncology journal is leading this increase.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 8b, Scientific Journals began publishing artificial intelligence studies in the field of Oncology in 2015. It began to rise after 2018. In the early part of 2020, Scientigfic Reports was leading in publishing articles in this area, but after 2020, Frontiers in Oncology took the lead. Figure 15 shows that Frontiers in Oncology published these studies at significantly higher rates compared to other Journals.\u003c/p\u003e\n\u003ch2\u003e3.5 Web of Science Categories\u003c/h2\u003e\n\u003cp\u003eAccording to the categorization within the Web of Science database, the articles were distributed across 147 scientific categories. However, nearly 70% of these articles predominantly fall within the scope of 10 major categories: Oncology (22%), Radiology, Nuclear Medicine Medical Imaging (13.4%), Engineering Biomedical (5.1%), Mathematical Computational Biology (5%), Engineering Electrical Electronic (4.8%), Computer Science Artificial Intelligence (4.7%), Computer Science Interdisciplinary Applications (4,7%) and Computer Science Information Systems (4.6%). Detailed category distribution is provided below (Table 4)\u003c/p\u003e\n\u003ch4\u003eTable 4: Distribution of Articles by Top 20 Web of Science Categories\u003c/h4\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeb of Science Documents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTimes Cited\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCitation Impact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocuments in Q1 Journals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocuments in Q2 Journals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocuments in Q3 Journals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocuments in Q4 Journals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eONCOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e49877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eRADIOLOGY, NUCLEAR MEDICINE \u0026amp; MEDICAL IMAGING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e1774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e53085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eENGINEERING, BIOMEDICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e14545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e19532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATHEMATICAL \u0026amp; COMPUTATIONAL BIOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e8409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e14204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eENGINEERING, ELECTRICAL \u0026amp; ELECTRONIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e10731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eCOMPUTER SCIENCE, INFORMATION SYSTEMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e6664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eMEDICAL INFORMATICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e8318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eBIOCHEMICAL RESEARCH METHODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e6115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eBIOTECHNOLOGY \u0026amp; APPLIED MICROBIOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e5080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eMEDICINE, RESEARCH \u0026amp; EXPERIMENTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e12342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eBIOCHEMISTRY \u0026amp; MOLECULAR BIOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e12785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eCOMPUTER SCIENCE, THEORY \u0026amp; METHODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e5488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eHEALTH CARE SCIENCES \u0026amp; SERVICES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e3944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eGASTROENTEROLOGY \u0026amp; HEPATOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e5324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eCHEMISTRY, MULTIDISCIPLINARY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e2710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eSURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e2553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eGENETICS \u0026amp; HEREDITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.03883495145631%\" valign=\"bottom\"\u003e\n \u003cp\u003eBIOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.89967637540453%\"\u003e\n \u003cp\u003e3630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.385113268608414%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(InCites, 2023)\u003c/p\u003e\n\u003ch2\u003e3.6 Keywords\u003c/h2\u003e\n\u003cp\u003eThe most frequent author keyword analysis was conducted to identify research hotspots and future research directions in the academic field. In this study, authors\u0026rsquo; keyword co-occurrence visualization graph was created by the Bibliometrix \u0026amp; R software program (Figure 9). The top 20 author keywords were shown. The most frequent author keywords in the dataset are machine learning, deep learning, radiomics, artificial intelligence, breast cancer, magnetic resonance imaging, and prostate cancer (Fig. 9).\u003c/p\u003e\n\u003cp\u003eKeyword co-occurrence analysis serves as a method to comprehend the primary themes or topics within a research field. Co-occurrence signifies the joint appearance of two pieces of information within a dataset. Each keyword within the dataset is represented as a node, while the co-occurrence of a pair of keywords is depicted as a link. The strength of this link is determined by the frequency of appearance of the paired keywords together (Radhakrishnan et al., 2017). This study employs Author Keywords for conducting keyword co-occurrence analysis. Author Keywords are automatically generated using a proprietary algorithm unique to Clarivate Analytics databases. The keywords associated with Artificial Intelligence (AI) applications in Oncology research are categorized into four clusters, denoted by four distinct colors (Figure 10). Each circle within the figure represents a keyword, and the lines connecting these circles signify the connections between the keywords. Keywords sharing the same color belong to the same cluster. The size of each circle in the figure corresponds to the frequency of the keyword: larger circles indicate higher frequency, while smaller circles denote lower frequency.\u003c/p\u003e\n\u003cp\u003eFigure 10 shows the visualization network map of author keywords co-occurrence. Four Clusters are formed weights based on occurrences. The red color indicates Cluster 1 (radiomics, magnetic resonance imaging, computer tomograph, etc.); the green color indicates Cluster 2 (machine learning, prediction, artificial neural network, etc.); the blue color indicates Cluster 3 (deep learning, breast cancer, artificial intelligence, etc.); the yellow color indicates Cluster 4 (cancer, feature extraction, image classification, etc.).\u003c/p\u003e\n\u003ch2\u003e3.7 Countries \u0026amp; Universities\u003c/h2\u003e\n\u003cp\u003eFigure 11 shows the institution collaboration network based on authors. five main clusters of institutions were identified: mostly universities from China (see blue) and universities from the USA (see red). Chinese institutes constitute a majority of the entities involved, with prominent institutions like the Chinese Academy of Science and Shanghai Jiao Tong University standing out. These institutions are noted for their high activity and collaboration in the realm of AI articles related to oncology. The USA is the second-largest cluster developed in the collaboration network. The institutes in this cluster, including Harward University and the University of Texas System, are the main ones.\u003c/p\u003e\n\u003cp\u003eFigure 12 shows the top 20 institutions in terms of publications. The co-authorship analysis showed that 51 institutions published more than five papers.\u003c/p\u003e\n\u003cp\u003eFigure 13 shows that only five clusters are formed; purple color indicates Cluster 1 (China, France, Taiwan, etc.); yellow color indicates Cluster 2 (USA, Germany, Italy, etc.); blue color indicates Cluster 3 (Netherlands, England, Spain, etc.); red color indicates Cluster 3 (South Korea, India, Egypt, Saudi Arabia, etc.); green color indicates cluster 5 (Japan, Australia, Norway, etc.)\u003c/p\u003e\n\u003cp\u003eFrom the country/region perspective, 106 countries/regions have participated in the article publications. China involves 1001 articles, accounting for 28.34% of the total publications, followed by the USA (25.62%), India (6.63%), South Korea (6.40%), and the United Kingdom (5.63%) (figure 14)\u003c/p\u003e\n\u003cp\u003eFigure 14 presents the top 20 countries based on the number of articles published, with a categorization by Multiple Country Publication (MCP) and Single Country Publication (SCP). Multiple Country Publication refers to collaborative works involving authors from different countries, indicating international collaboration. Conversely, Single Country Publication denotes works where all authors belong to the same country, signifying intra-country collaboration. Chinese authors lead in productivity with 2508 articles, comprising 2071 single-country publications and 437 multiple-country publications, resulting in an MCP ratio of 17.4%. On the other hand, authors from Turkey exhibit the lowest ratio of multiple-country publications, contributing a total of 96 articles, with only 11 being single-country publications. For more detailed information, refer to Figure 14 and Table 5.\u003c/p\u003e\n\u003cp\u003eFigure 14 further illustrates that the majority of publications are authored by individuals from the same countries. This trend might arise from authors\u0026apos; preferences to collaborate within their research groups or with academics sharing the same national background.\u003c/p\u003e\n\u003cp\u003eTable 5: Corresponding author\u0026apos;s country and between-country collaboration\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"475\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eArticles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCP_Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e2508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e2071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e1428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eINDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eKOREA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eJAPAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eGERMANY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNITED KINGDOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eCANADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eNETHERLANDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eFRANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eIRAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eTURKEY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eAUSTRALIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eSAUDI ARABIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eEGYPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eSPAIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003ePAKISTAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eSWEDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.88259958071279%\" valign=\"bottom\"\u003e\n \u003cp\u003eSWITZERLAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.658280922431867%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.482180293501049%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.964360587002098%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Bibliometrix \u0026amp; R software, 2023)\u003c/p\u003e\n\u003ch2\u003e3.8 Funding\u003c/h2\u003e\n\u003cp\u003eAccording to the results presented in Table 6, the National Natural Science Foundation, with 1045 studies, and the Department of Health \u0026amp; Human Services, with 656 studies, had the highest support for the publication of scientific research on Artificial Intelligence for cancer detection. The National Natural Science Foundation, Department of Health \u0026amp; Human Services, NIH National Cancer Institute, US Department of Health and Human Services significantly improve artificial intelligence for cancer detection.\u003c/p\u003e\n\u003cp\u003eTable 6: Distribution of Articles according to the international organizations that funded them\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\"\u003e\n \u003cp\u003eFunding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003eWeb of Science Documents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003eTimes Cited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003eInternational Collaborations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003eDomestic Collaborations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003eDocuments in Q1 Journals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003eDocuments in Q2 Journals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003eDocuments in Q3 Journals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003eDocuments in Q4 Journals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNational Natural Science Foundation of China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e1045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e18886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eDepartment of Health \u0026amp; Human Services-USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e22221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNational Institutes of Health (NIH)-USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e21919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNIH National Cancer Institute (NCI)-USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e9855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNational Research Foundation of Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e2843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNational Science Foundation-USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e2549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinistry of Education \u0026nbsp;Culture Sports \u0026nbsp;Science and Technology-Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eJapan Society for the Promotion of Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinistry of Science \u0026nbsp;ICT \u0026amp; Future Planning-Republic of Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eEuropean Union-EU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eFundamental Research Funds for the Central Universities-China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNational Natural Science Foundation of Guangdong Province-China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrants-in-Aid for Scientific Research-Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eUK Research \u0026amp; Innovation-UK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e3290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eBeijing Natural Science Foundation-China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eSpanish Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e3096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinistry of Science and Technology-Taiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eGerman Research Foundation (DFG)-Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e2074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinistry of Science \u0026amp; ICT-Republic of Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eChina Postdoctoral Science Foundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNatural Sciences and Engineering Research Council of Canada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eEuropean Research Council\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e3609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedical Research Council UK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e2902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eNatural Science Foundation of Zhejiang Province-China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.34975369458128%\" valign=\"bottom\"\u003e\n \u003cp\u003eCanadian Institutes of Health Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.060755336617405%\"\u003e\n \u003cp\u003e1379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717569786535304%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.239737274220033%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(InCites, 2023)\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 15, countries began supporting artificial intelligence studies in the field of Oncology in 2014. It began to rise after 2016. In the early part of 2016, the National Institutes of Health (NIH) in the USA was leading in supporting studies in this area, but after 2019, the National Natural Science Foundation of China (NSFC) took the lead. Figure 15 shows that NSFC supported these studies at significantly higher rates compared to other funding organizations. It\u0026apos;s noticeable that the expected support from NIH\u0026apos;s National Cancer Institute lagged behind. As seen in previous graphs and tables, it demonstrates how extensively China has invested in this field.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn recent years, artificial intelligence (AI) has swiftly become an integral part of the medical field, particularly in cancer detection. This study utilized the bibliometrix package of R software and Litmaps visualization software to conduct a thorough bibliometric analysis of AI applications in oncology research over the past 30 years. The objective was to provide a comprehensive understanding of the field. Our analysis objectively and systematically outlined the current status of AI applications, identified developmental trends, and highlighted potential research focal points in cancer detection. This endeavor facilitates scholars in rapidly comprehending the research landscape and offers valuable insights for selecting research topics. The initial phase of the study examined publication trends, covering aspects such as countries, institutions, authors, and journals. Subsequently, cluster analysis was applied to keywords to identify research hotspots within the field.\u003c/p\u003e\n\u003cp\u003eBased on the analysis of publication trends, there has been a significant surge in the number of publications on AI in Oncology over the past four years. China and the United States emerged as the leading nations regarding the volume of publications in this field. Citation counts, widely recognized as an indicator of professional acknowledgment in scientific work, were extensively used to assess research quality. The United States stood out in terms of both citation counts and international collaborations, with a considerable lead over other countries. Additionally, the university contributing the most publications and citations was based in China, underscoring China\u0026apos;s pivotal role and global leadership in this domain. Despite China\u0026apos;s considerable volume of publications, the relatively low citation counts suggest a need to enhance the quality and impact of its research. This could be attributed, in part, to the later initiation of AI in Oncology research in China, resulting in comparatively lower international academic influence. Notably, Jie Tian and Zaiyi Liu from China emerged as the most published authors, contributing to 10.3% of the publications. Regarding citations, Hugo Aerts from Stanford University and Robyn Gillies from the University of Washington in the United States received the highest recognition. These findings underscore the importance of quantity, quality, and global impact in advancing AI research in cancer detection.\u003c/p\u003e\n\u003cp\u003eThis is the newest bibliometric study that provides detailed information about published literature on the AI in Oncology. The most active institutions were the Chinese Academy of Science and Harward University, and the most productive countries were China and the USA. The most frequently co-occurrence author keywords were radiomics, machine learning, artificial intelligence, and breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe outcomes of this study hold value for researchers, policymakers, and educational purposes. Additionally, they offer assistance to funding agencies in evaluating current research trajectories and anticipating future trends in AI within Oncology. Effective AI development and treatment therapy is still a hot zone for future research directions. Three leading journals publish the most articles on artificial intelligence and Oncology: Frontiers in Oncology (428 articles) and Scientific Reports and Cancer (284 and 247 articles, respectively). While these three leading journals accounted for 12 % of total articles, the remaining list is well-distributed. The majority, almost 70%, of the articles fit into ten significant categories: Oncology (22%), Radiology Nuclear Medicine Medical Imaging (13.4%), Engineering Biomedical (5.1%), Mathematical Computational Biology (5%),\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eArticles about extracting meaning from radiological/microscopic/real patient images in cancer patients using artificial intelligence have been produced for approximately six years and have reached a certain maturity. It is understood that some obstacles are related to deriving meaning from genomic/genomic/proteomic data and doctor notes written in text. As new findings emerge, the association of diseases and treatments using existing classification systems with genomic/proteomic data should be expected to increase geometrically/exponentially. In order to derive meaning from clinical data, it is necessary to create new databases of the NoSQL type for transferring values from biochemistry, tumor markers, drug doses, as well as names of drugs/materials/devices, and text notes written manually with pen or keyboard into artificial intelligence software. Therefore, it is expected that big data derived from electronic health records should be reprocessed and re-archived by developing new standards. It is understood that the CancerLinq database has not contributed to machine learning-related publications so far and may not be able to do so in its current state. It is observed that despite fewer available parameters for individuals in the previously established SEER database, more publications related to machine learning have been made. This situation may stem from a structural difference between the CancerLinq and SEER databases.\u003c/p\u003e\n\u003cp\u003eThe exploration of artificial intelligence in Oncology is in its early phases but is anticipated to progress rapidly. Researchers are currently investigating AI applications in various aspects of Oncology within the medical field, including medicine, diagnosis, therapy, and risk assessment. Implementing artificial intelligence proves effective in mitigating human errors and enhancing work efficiency. This bibliometric study offers a comprehensive overview of AI in Oncology research, focusing on the discipline\u0026apos;s current state. This perspective assists researchers in identifying critical areas of interest, cutting-edge developments, and emerging research directions within the field.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the anonymous reviewers for their helpful comments and suggestions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the paper is publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions (According to ICMJE)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompliance with Ethical Standards (According to COPE)\u003c/p\u003e\n\u003cp\u003eDisclosure of potential conflicts of interest: \u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch involving Human Participants and/or Animals: \u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent: \u003cstrong\u003eNot Applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement: \u003cstrong\u003eNot Applicable\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOpen access funding provided by the Scientific and Technological Research Council of T\u0026uuml;rkiye (T\u0026Uuml;BİTAK).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study. \u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo ethical approval was needed because this is not a human study, but only online information was used.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndres, A. 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Software survey: VOSviewer, a computer program for bibliometric mapping [Article]. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 84\u003c/em\u003e(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3\u003c/li\u003e\n\u003cli\u003eWeb of Science (2022). Retrieved November 21, 2023, from https://www.webofscience.com/wos/woscc/summary/d395368a-6372-4770-ba65-01c2a114dea3-b5fc6c6e/times-cited-descending/1\u003c/li\u003e\n\u003cli\u003eZhang, C., Qi, L. S., Cai, J., Wu, H. X., Xu, Y., Lin, Y. L., . . . Ma, W. J. (2023). Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence [Article]. \u003cem\u003eBmc Cancer\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(1), 16, Article 239. https://doi.org/10.1186/s12885-023-10704-w\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":"oncology, cancer, artificial intelligence, deep learning, neural network","lastPublishedDoi":"10.21203/rs.3.rs-4260599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4260599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Oncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are \" machine learning \", \"deep learning,\" \"radiomics\", \"breast cancer\", “melanoma” \u0026nbsp;and \"artificial intelligence,\" which are the research hotspots in this field. A total of 10866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field.\u003c/p\u003e","manuscriptTitle":"Development Trends and Knowledge Framework of Artificial Intelligence (AI) Applications in Oncology by years: A Bibliometric Analysis from 1992 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 09:31:54","doi":"10.21203/rs.3.rs-4260599/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":"63766326-7e45-4307-9ead-31ac2d5868c7","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-03T23:09:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 09:31:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4260599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4260599","identity":"rs-4260599","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}