ReviewCanadian Urological Association journal = Journal de l'Association des urologues du Canada2026
Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.
Review in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence-based expert trajectory guidance in anFrontiers in surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe integration of artificial intelligence (AI) into surgical training is rapidly evolving, driven by advancements in machine learning. This review aimed to map the current landscape of AI's educational applications in urology.
methodsA 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
resultsOur search yielded 2774 studies, of which 59 relevant ones 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.
conclusionsThe 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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.