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Global research landscape of robot-assisted surgical training: a 35-year bibliometric and visualization analysis

Year: 2026

Type: article

Abstract: BACKGROUND: Over 17 million robot-assisted surgeries (RAS) have been performed globally, driving demand for standardized and effective surgical training. This study maps the 35-year bibliometric landscape of RAS training research to identify trends, gaps, and future directions. MATERIALS AND METHODS: We analyzed 592 publications from the Web of Science Core Collection (1990-2025) using CiteSpace, VOSviewer, and Excel. Metrics included publication trends, citations, author/institutional collaborations, journal impact, keyword clusters, and burst detection. RESULTS: Research output grew exponentially, with three evolutionary phases: (1) Technical validation (1998-2010), focusing on prostatectomy and lymphadenectomy; (2) Methodological innovation (2011-2020), emphasizing face and simulator (e.g., Fundamentals of Robotic Surgery); and (3) Standardization/AI integration (2020-2025), prioritizing patient safety and training curriculum. The USA dominated contributions (47.13% of publications), followed by the UK (highest citations/article: 33.43) and Germany. Surgical Endoscopy published the most studies (70), while European Urology had the highest impact (IF: 25.2), The Journal of Robotic Surgery is promising. The analysis identifies three critical challenges currently facing the field: (1) skill transfer from training to clinical practice, (2) integration of artificial intelligence technology, and (3) establishment of a standardized governance framework empowered by AI for robotic surgery training. CONCLUSION: RAS training research still focuses on European and North American countries, with differences in global cooperation and standardized training governance. Future efforts require cross-border partnerships, open access policies, and governance frameworks to coordinate training standards and accelerate the integration of artificial intelligence technology and enhance clinical translation of simulated training results.

Source: International Journal of Surgery

DOI: 10.1097/js9.0000000000004716

Authors: Cheng-Cheng Wu, Bin Gong, Xiaochen Zhang, Jin Yang

Institutions: Sir Run Run Shaw Hospital, Zhejiang University

Language: English

Topic: Surgical Simulation and Training

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDGs: Partnerships for the goals, Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,450