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Robot-generated data and objective performance assessment in urologic robotic surgery: global research trends and
1Department of Urology, The Second Clinical Medical College of Gansu, Lanzhou First People's Hospital, University of Chinese Medicine, Lanzhou, Gansu, 730050, Gansu, China.
Journal of Robotic Surgery
|August 11, 2026
Summary
Robot-generated surgical data in urology is rapidly advancing, moving from engineering metrics to clinical performance assessment. Further validation is needed for objective metrics to support clinical decisions and credentialing.
Area of Science:
- Urology
- Robotic Surgery
- Medical Data Analytics
Background:
- The development of robot-generated surgical data in urology has evolved significantly.
- Early research focused on engineering measurements, with a recent shift towards clinically interpretable performance assessment.
Purpose of the Study:
- To map the progression of robot-generated surgical data in urology.
- To assess the literature's transition from engineering-focused measurements to clinically relevant performance evaluations.
Main Methods:
- A comprehensive search of the Web of Science Core Collection was conducted using keywords related to robotic urologic procedures and performance metrics.
- Bibliometric analysis using R/Bibliometrix-Biblioshiny and CiteSpace was performed on 86 retrieved publications from 1995-2026.
- Keyword and co-cited-reference clustering identified key themes and research communities.
Main Results:
- The literature has grown substantially since 2021, with the USA as the leading producer.
- Key themes include surgical training, AI, prostate interventions, and kinematic analysis.
- While progress has been made in construct validity and associations with continence recovery, transportability and causal benefits require further investigation.
Conclusions:
- The field is transitioning from device control to performance measurement and surgical analytics.
- Objective metrics require careful definition and validation, considering confounding factors.
- Multicenter validation and standardized data are crucial for clinical application and credentialing.