Beyond the Hype: Mapping the Evolution of Artificial Intelligence in General Surgery Through Two Decades of Bibliometrics

Abstract

BACKGROUND: Artificial intelligence (AI) has transformed many facets of general surgery. A quantitative bibliometric overview can map publication trends, research fronts, and collaborative patterns to guide future work. Our study provides a comprehensive analysis of the literature on AI in general surgery, identifying key trends and influential contributors. METHODS: We retrieved 536 "Article" and "Review" records from Scopus and Web of Science from January 2005 through June 2025. After a rigorous deduplication process, 536 unique publications remained. We analyzed annual scientific production, top journals, authors, keyword co-occurrence, and highly cited papers using descriptive and relational bibliometric analyses. RESULTS: Annual publications grew exponentially, accelerating significantly after 2019 and peaking at 160 publications in 2024. Annals of Surgery (n = 28), Surgical Endoscopy (n = 25), and Journal of Medical Internet Research (n = 20) were the most productive journals. Palenzuela DL (n = 7), Dayan D (n = 6), and Liu J (n = 6) were the most prolific authors. The most frequent keywords were "Artificial intelligence" (64), "General surgery" (43), and "Surgery" (31). Keyword co-occurrence analysis revealed five thematic clusters: AI language models, clinical outcomes/risk prediction, surgical education, socio-professional themes, and core surgical practice. The most cited articles focused on surgical phase recognition, medical education, and large-language models. CONCLUSIONS: AI in general surgery has seen a period of exponential growth, moving from exploratory discourse to applied research. While research is concentrated among a few authors and journals, its thematic diversity suggests a nascent, fragmented field without a dominant intellectual core. Future work should prioritize prospective validation, data-sharing infrastructures, and ethical frameworks to ensure responsible clinical translation. We propose an ethical-educational-technological (EET) framework to guide the responsible integration of AI into surgical practice and training.

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