Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis

BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical practice, with applications spanning preoperative planning, intraoperative guidance, postoperative management, and surgical education. Despite accelerating research activity, the structure, thematic evolution, and funding landscape of AI research in general surgery remain incompletely characterized. This study aimed to systematically evaluate scientific production on AI in general surgery in the United States over the past 5 years using a bibliometric approach. METHODS: A bibliometric analysis was conducted following Preliminary Guideline for Reporting Bibliometric Reviews of the Biomedical Literature and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines using Web of Science. English-language articles published between 2020 and 2025 with a U.S.-affiliated senior author and focused on AI use in general surgery were included. Publications were analyzed across five primary domains: authorship metrics, thematic endpoints, journal characteristics, country of origin, and funding patterns. Bibliometric indicators included H-index, citation counts, Article Influence Score (AIS), and Bradford's Law classification. Funding distribution across endpoints was evaluated using chi-square or Fisher's exact tests, with effect sizes estimated using Cramér's V and odds ratios. Temporal trends in endpoints and keywords were assessed using Poisson and negative binomial regression models. RESULTS: = .029). CONCLUSION: AI research in U.S. general surgery is growing but remains concentrated in workflow and decision-support domains. Funding favors clinically actionable applications, highlighting the need for broader, equity-focused AI development.