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Updated: May 27, 2026
Robotic Left Hepatectomy using Indocyanine Green Fluorescence Imaging for an Intrahepatic Complex Biliary Cyst
Published on: June 24, 2022
Global evolution of robot-assisted cholecystectomy research in the era of artificial intelligence: a bibliometric and
Chenglou Zhu1, Lanfang Liu2, Naiqing Liu1
1Central Laboratory, Clinical Medical Research Center, Linyi People's Hospital, Shandong Second Medical University, Shandong Province, 276000, Linyi, China.
Objective:
To systematically map the global evolution, collaborative networks, knowledge structure, and emerging research hotspots in robot-assisted cholecystectomy (RAC) using bibliometric and visualization techniques.
Methods:
A comprehensive bibliometric analysis was conducted using the Web of Science Core Collection (2005-2025). Publications were retrieved using predefined search strategies and screened according to strict inclusion criteria. CiteSpace, VOSviewer, and R software were employed to analyze publication trends, co-authorship networks, institutional and national collaborations, co-citation patterns, keyword co-occurrence, and research bursts. Knowledge mapping techniques were used to visualize thematic evolution and intellectual structure.
Results:
A total of 926 eligible publications were included. Global output demonstrated a continuous and marked increase, particularly after 2016, with citations following a similar upward trajectory, reflecting growing academic impact. The United States dominated both publication output and citation influence, while collaboration networks remained largely regionally clustered with limited cross-national integration. Research was primarily concentrated in high-impact surgical journals, with foundational contributions emphasizing feasibility, safety, and comparative effectiveness. Keyword and co-citation analyses revealed a knowledge structure centered on laparoscopic surgery, bile duct injury, and robotic systems, with emerging clusters highlighting artificial intelligence (AI) and surgical education. Evolutionary trajectory analysis demonstrated a transition from technical feasibility (early stage), to safety and evidence-based evaluation (middle stage), and to training and technological integration (recent stage). Burst detection further identified recent hotspots in AI, cost-effectiveness, and surgical training systems. Emerging evidence also indicates increasing integration of AI-driven systems into RAC, enabling intraoperative decision support, workflow recognition, and early-stage semi-autonomous surgical execution, particularly in standardized procedures such as cholecystectomy.
Conclusion:
RAC research has evolved from early exploratory studies toward increasing technological integration and methodological refinement. Although RAC is not currently among the most dominant clinical indications for robotic surgery, it provides a valuable model for studying surgical standardization, training systems, and emerging intelligent surgical technologies. While current applications remain largely assistive, emerging advances in AI and robotic technologies suggest the potential for future development toward more intelligent and partially automated surgical systems. However, most evidence currently remains experimental and has not yet translated into widespread clinical practice. Future research should focus on strengthening global collaboration, improving high-quality evidence generation, and carefully evaluating the safety, feasibility, and clinical applicability of emerging intelligent surgical technologies.
