The evolving landscape of artificial intelligence in cholecystectomy: a bibliometric and science-mapping analysis.

Authors

Hai-Bo Zou, Qian Ding, Ying Xiao, Yi-Zhun Zhu

Published in

Updates in surgery. Nov 11, 2026. Epub Nov 11, 2026.

Abstract

To map the global research landscape and thematic evolution of artificial intelligence (AI)-related research in cholecystectomy and to identify major contributors, collaboration patterns, and intellectual foundations. English-language articles and reviews were retrieved from the Web of Science Core Collection and Scopus. After deduplication and eligibility screening, 559 publications were included. Publication trends, scientific collaboration, keyword co-occurrence, conceptual structure, thematic evolution, and co-citation and citation-burst patterns were analyzed using bibliometrix, VOSviewer, and Python. Research activity increased markedly in recent years, with 424 publications (75.8%) appearing between 2022 and 2025. China contributed the largest publication output, whereas the United States received the highest total citations. Keyword and conceptual-structure analyses identified research domains involving AI and computational methods, surgical workflow and intraoperative visual analysis, and computer-vision-based applications. Thematic evolution suggested a progression from early workflow- and convolutional-neural-network-related research toward machine learning, deep learning, radiomics, surgical phase recognition, and more recent transformer-related approaches. Twinanda et al. [1] showed the strongest citation burst and occupied a prominent position in the reference co-citation network. AI-related research in cholecystectomy has expanded rapidly and become increasingly diverse in its methodological and application-oriented focus. Current bibliometric patterns suggest growing attention to workflow analysis, computer vision, surgical phase recognition, and newer model architectures. Further multicenter validation and prospective clinical evaluation are needed to determine whether these developments translate into generalizable and clinically meaningful applications.

PMID:
43013215
Bibliographic data and abstract were imported from PubMed on 12 Nov 2026.

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