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The focus of this study was to provide a quantitative overview of the field's development and trends.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A structured search was performed in the Web of Science Core Collection to identify original articles on AI use in gynecologic oncology. Two independent reviewers screened and selected studies based on predefined inclusion criteria. Extracted data\u2014including publication trends, author and institutional collaborations, keyword co-occurrence, and citation networks\u2014were analyzed using CiteSpace 6.2.R6 and VOSviewer software.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 2544 articles were included for analysis. Research activity showed a notable acceleration after 2019, reaching its highest output in 2024. China and the United States emerged as dominant contributors, with the Chinese Academy of Sciences and Fudan University leading among institutions. Influential authors such as Sala Evis, Tian Jie, and Scambia Giovanni were identified. Major research themes focused on \u201cRadiomics,\u201d \u201cDeep Learning,\u201d \u201cRadiotherapy,\u201d and cancers including cervical, ovarian, and endometrial. Recent emerging topics included \u201cDigital Pathology,\u201d \u201cPersonalized Medicine,\u201d and \u201cTumor Heterogeneity,\u201d signaling a shift toward precision oncology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This bibliometric study delineated the evolving field of AI in gynecologic oncology, highlighting dynamic research fronts and gaps.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261416708","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:30:11Z","timestamp":1769023811000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of artificial intelligence in gynecologic cancers: A bibliometric analysis"],"prefix":"10.1177","volume":"12","author":[{"given":"Nan","family":"Liu","sequence":"first","affiliation":[{"name":"Qilu Hospital of Shandong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-8752","authenticated-orcid":false,"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8617-9675","authenticated-orcid":false,"given":"Yaochen","family":"Lou","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases"},{"name":"Department of Gynecology, The International Peace Maternal and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Guan","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21456"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)00130-0"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(19)30482-6"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1039\/C9AY01454C"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15040406"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102164"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.70094"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/phar.1586"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.31181\/sems31202535k"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/publications9010012"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.5195\/jmla.2023.1513"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-021-03948-5"},{"key":"e_1_3_3_15_2","article-title":"Comparing bibliometric analysis using PubMed, Scopus, and Web of Science databases","volume":"152","author":"AlRyalat SAS","year":"2019","unstructured":"AlRyalat SAS, Malkawi LW, Momani SM. 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