Artificial intelligence in urology training

Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery

Authors

  • Jackie Han Michael G. DeGroote School of Medicine McMaster University, Hamilton, ON https://orcid.org/0009-0008-6757-4179
  • Le Yi He Faculty of Health Sciences Queen’s University, Kingston, ON
  • Ankit Parmar Michael G. DeGroote School of Medicine, McMaster University
  • Kofi Brako Michael G. DeGroote School of Medicine McMaster University, Hamilton, ON
  • Edward D. Matsumoto Division of Urology McMaster University, St. Joseph’s Hospital, Hamilton, ON https://orcid.org/0009-0004-3120-3406

DOI:

https://doi.org/10.5489/cuaj.9477

Keywords:

articial intelligence, Education, Laparoscopy, Robotics, Surgical Innovation, Training & Evaluation, Robotic Surgery, Training, Education, AI

Abstract

Introduction: The integration of artificial intelligence (AI) into surgical training is rapidly evolving, driven by advancements in machine learning. This review aims to map the current landscape of AI’s educational applications in urology.

Methods: A systematic search of MEDLINE, PubMed, Embase, Cochrane, Scopus, and Engineering Village identified studies exploring AI applications in video-based surgical education and assessment. Search terms included AI, urologic procedures, and training/assessment components, and results were screened in Covidence®. AI applications involving urological procedures were included. For every study, two reviewers independently conducted screening. Data were synthesized thematically to evaluate AI’s application in urology training.

Results: Our search yielded 2767, of which 59 relevant studies were identified. AI was most frequently applied with robotic-assisted radical prostatectomy (RARP), followed by robotic-assisted partial nephrectomy (RAPN). AI applications were broadly categorized into three domains: 1) annotation, where key anatomy and instruments from procedural videos are labelled; 2) feedback, such as recognizing surgical phases or monitoring surgical events; and 3) evaluation, where the surgical gestures are recognized or evaluated to stratify skill level and predict patient outcomes.

Conclusions: The emergence of AI use in urologic procedures underscores its transformative potential in procedural education and training. AI has wide applications in annotation, feedback, and assessment across different procedures. While prostatectomy dominates in the literature, the adaptability of AI frameworks exists across other urologic procedures. New commercially available tools demonstrate promising results, making them potentially beneficial additions to urology training programs. Future efforts should focus on multicentric collaboration and longitudinal skill assessments.

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Published

2026-02-13

How to Cite

Han, J., He, L. Y., Parmar, A., Brako, K., & Matsumoto, E. D. (2026). Artificial intelligence in urology training: Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery. Canadian Urological Association Journal, 20(6). https://doi.org/10.5489/cuaj.9477

Issue

Vol. 20 No. 6 (2026): CUAJ June

Section

Review

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