Trends and Themes in Artificial Intelligence Applications for Obesity: A Bibliometric Mapping Study

Background: Obesity is a major global health challenge with significant metabolic and cardiovascular consequences. Artificial intelligence (AI) offers novel opportunities for prediction, risk stratification, and management; however, the structural landscape of this research has not been comprehensively assessed. Methods: We conducted a bibliometric analysis of 5893 unique articles from 2015 to July 15 th , 2025, indexed in Web of Science and Scopus. We used R (Bibliometrix and Biblioshiny) and VOSviewer to evaluate publication trends, citations, collaborations, and thematic clusters. Data integrity was verified by dual review of 5% of studies. Results: Scientific output rose steadily, with a marked acceleration after 2019. Citation activity peaked in 2020, reflecting increased focus on digital health during the Coronavirus Disease 2019 (COVID-19) pandemic. Thematic mapping identified four main clusters: (1) AI in surgical outcomes, including bariatric surgery and risk prediction; (2) digital health and remote care; (3) conversational technologies like natural language processing and chatbots; and (4) precision health, focusing on personalized medicine and predictive analytics. Collaboration networks were sparse, with few prolific authors. Conclusions: AI research in obesity is expanding rapidly across diverse themes but remains fragmented. Strengthening interdisciplinary collaboration will be critical to maximize impact on obesity care and outcomes.