Abstract
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 15th, 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.
Keywords
Get full access to this article
View all access options for this article.