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Panorama mundial de la investigación en formación en cirugía asistida por robot: un análisis bibliométrico y de
Cheng-Cheng Wu1, Bin Gong1, Xiaochen Zhang1
1Education Office, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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.
