Abstract
BACKGROUND: Obesity is the leading preventable cause of death worldwide. In recent years, artificial intelligence (AI) has shown the potential in the prevention, diagnosis, and management of obesity. OBJECTIVE: This study employ bibliometric analysis methods to systematically review applications within this field, identify key research hotspots, and provide novel insights for future research. METHODS: The Web of Science Core Collection database was employed as the source. All relevant publications were searched from the database's inception to June 2025. VOSviewer was used to analyze co-occurrence networks among countries, institutions, authors, and keywords. CiteSpace was utilized for keyword burst analysis and to detect emerging research fields, while the Bibliometrix R package was applied to identify influential papers, institutions, and authors. RESULTS: A total of 420 publications were included, which were affiliated with 806 institutions in 42 countries. These publications were authored by 2589 individuals and published in 122 journals. The United States had the highest number of publications and citations. Harvard University produced the greatest volume of papers. El Chaar, M. was the most influential author. Obesity Surgery published the most articles, while Journal of Medical Internet Research was cited most frequently. Based on keyword cluster analysis, this study identified three major research themes within the field of obesity management: telemedicine, machine learning applications, and perioperative management. CONCLUSION: This study provides a comprehensive bibliometric analysis of AI in obesity management. Future research should focus on conducting multi-center, long-term, randomized controlled trials, obtaining high-quality longitudinal data, and formulating reasonable reimbursement policies and data security regulations to enhance the generalizability, effectiveness, and safety of AI applications. These findings are crucial for addressing the global burden of obesity.