Robot-generated data and objective performance assessment in urologic robotic surgery: global research trends and...

To map the development of robot-generated surgical data in urology and to assess how far the literature has progressed from engineering measurement to clinically interpretable performance assessment. The Web of Science Core Collection was searched on July 23, 2026 using a focused topic strategy that combined robotic urologic procedures with telemetry, kinematics, console or system events, instrument motion, trajectories, and automated or objective performance metrics (APMs). English-language articles and reviews were analyzed with R/Bibliometrix-Biblioshiny and CiteSpace. Of 125 retrieved records, 86 publications were included (84 articles, including 3 also indexed as proceedings papers, and 2 reviews). The corpus covered 1995-2026, 43 sources, and 461 authors; 51 publications (59.3%) appeared from 2021 onward. The USA had the highest affiliation-based production frequency (77) and citation count (1,499). The University of Southern California was the leading affiliation (24 publications), and Hung AJ was the most prolific author (15 publications; h-index 14; 72 local citations). Keyword clustering was coherent (Q = 0.7445; weighted mean silhouette = 0.9138) and identified 12 themes spanning surgical training, path planning, medical robotic systems, artificial intelligence, prostate interventions, kinematic analysis, human factors, and ergonomics. Co-cited-reference clustering was highly separated (Q = 0.9273; weighted mean silhouette = 0.9463), with major communities centered on urinary continence, training, and credentialing. The literature was nevertheless small, concentrated in a limited number of groups, and strongly weighted toward robot-assisted radical prostatectomy. The most clinically advanced evidence concerned construct or concurrent validity and a limited number of prospective associations with continence recovery or training; transportability and causal benefit remain uncertain. The field has moved from device control and kinematic engineering toward performance measurement and multimodal surgical analytics. Here, 'objective' describes how a signal is recorded, not freedom from confounding by anatomy, case complexity, surgical strategy, trainee involvement, center, or platform. Procedure-specific definitions, hierarchical risk adjustment, vendor-neutral data standards, and multicenter prospective validation are prerequisites before robot-derived metrics can support high-stakes credentialing or routine clinical decision-making.