{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T11:08:23Z","timestamp":1789384103702,"version":"build-2803163510"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gastric cancer (GC) is the fifth most common cancer worldwide, ranking fifth in both incidence and mortality rates; it severely impacts patients\u2019 quality of life, and the identification and detection of GC are crucial for its prevention. In recent years, there has been a growing trend in the application of artificial intelligence (AI) for the diagnosis, treatment and prognosis of GC; however, systematic analyses using bibliometric tools remain scarce. We have conducted a comprehensive bibliometric analysis to assess the current state and future trends of research on the AI in GC, thereby providing valuable insights to inform further in-depth research in this field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To utilize bibliometric and network analysis methods to examine research progress and trends of AI applications in GC. The findings of this study aim to provide a foundation and guidance for further in-depth research into GC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dual-database search and analysis were conducted. Firstly, English-language academic journals in the Web of Science Core Collection (WoSCC) database on the application of AI in GC were retrieved. Subsequently, VOSviewer was utilized to conduct a network co-occurrence analysis of the output data, including institutional affiliations, authors, references and keywords. CiteSpace software was employed to perform statistical analyses of annual publication numbers, keyword clustering, citation counts and keyword bursts. Scimago Graphica was used to map collaboration networks between countries and regions. Finally, we searched PubMed for clinical trial literature to conduct a complementary analysis, which improved the scientific rigor and comprehensiveness of our results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 2,357 eligible articles and 311 clinical trials were included. Since 1993, the number of published papers has increased steadily each year. Among the authors, several stable core groups have emerged, represented by Li, Wang, Tian, Dong, Ta Da and Yu, among others. Of the affiliated institutions, the Chinese Academy of Sciences showed the strongest association and published the most papers, totaling 102. High-frequency keywords include \u2018gastric cancer\u2019, \u2018machine learning\u2019, \u2018artificial intelligence\u2019, \u2018deep learning\u2019 and \u2018radiomics\u2019. Results derived from PubMed complement those from the WoSCC dataset, offering a more holistic overview of the research landscape in this field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The visualized map provides an intuitive overview of research landscape of AI applications in GC over the past 33 years. Over the last five years, research in this field has gradually intensified, and the trend is positive. Our findings indicate that AI-assisted endoscopic and pathological diagnosis of GC, immunotherapy, and AI-assisted clinical decision-making systems will be key areas of future research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1926967","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T11:00:12Z","timestamp":1789383612000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026"],"prefix":"10.3389","volume":"16","author":[{"given":"Xueqing","family":"Wang","sequence":"first","affiliation":[{"name":"The First School of Clinical Medicine, Heilongjiang University of Chinese Medicine","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department 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