Artificial intelligence, surgical vision, and digital navigation in robotic urology: a bibliometric and knowledge-mapping analysis.

Digital technologies are increasingly used to extend the anatomical, functional, and procedural information available in robot-assisted urology, but their development has not been examined within a common field-level framework. The final corpus comprised 297 English-language articles and reviews from the Web of Science Core Collection after prespecified screening. We examined publication trends, collaboration patterns, citation relationships, author-keyword structures, and six mutually exclusive primary technology domains using R, VOSviewer, and CiteSpace. Annual output rose from 12 publications in 2018 to 52 in 2025. The primary domain encompassing three-dimensional modelling, augmented reality, mixed reality, and digital navigation was the largest, comprising 118 of 297 publications. Surgical vision, video, and workflow intelligence showed the clearest recent expansion, accounting for 24.0% of publications in 2022-2026, while artificial intelligence and machine learning (AI/ML) prediction and decision support increased from 5.7% in 2018-2021 to 10.4% in 2022-2026. Research was concentrated in the USA, Italy, and the Netherlands, with distinct country-level technology profiles. Co-citation and keyword analyses situated newer work in surgical-phase recognition and artificial intelligence alongside established research on navigation, fluorescence and molecular guidance, image-guided surgery, and performance analytics. These patterns suggest that the field is diversifying by adding data-driven interpretation of intraoperative information to established systems of planning, guidance, and performance assessment. They describe research activity and knowledge structure rather than clinical effectiveness, technological maturity, or readiness for routine use.