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

Cheng-Cheng Wu; Bin Gong; Xiaochen Zhang; Jin Yang

doi:10.1097/JS9.0000000000004716

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

Int J Surg. 2026 Jan 7;112(4):10359-10373. doi: 10.1097/JS9.0000000000004716. Online ahead of print.

Authors

Cheng-Cheng Wu¹, Bin Gong¹, Xiaochen Zhang¹, Jin Yang^{2

3}

Affiliations

¹ Education Office, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
² Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medical College, Zhejiang University, Hangzhou, China.
³ Key Laboratory of Laparoscopic Technology of Zhejiang Province, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

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.

Keywords: artificial intelligence; bibliometric analysis; learning curve; robotic surgical training; simulation training.