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Although artificial intelligence (AI) has shown strong standalone performance in prostate pathology, its real-world impact on TURP diagnostic workflows remains insufficiently characterized. We developed an AI tool for slide-level detection of prostatic adenocarcinoma using 6,535\u00a0H&amp;E whole-slide images from biopsies and TURP specimens, with feature extraction by UNI, classification through a CLAM framework, and presentation on a custom OpenSeadragon-based viewer. Five readers with different experience levels evaluated 102 TURP slides in two sessions, first unaided and then AI-assisted after an 8-week washout period. Primary outcome was per-slide review time; secondary outcome was diagnostic accuracy. AI assistance significantly reduced review time for all readers, with mean reductions ranging from 29.4% to 53.8%. In mixed-effects modeling, AI was associated with a mean reduction of 34.5\u00a0s per slide (95% CI, 30.0\u201339.0;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Time savings were greatest among less experienced readers. Overall diagnostic accuracy increased from 95.3% in the unaided setting to 98.2% with AI assistance. Non-inferiority was confirmed for all readers, and accuracy even improved significantly in the least experienced readers. Across all readings, classification errors decreased from 24 unaided to 9 aided. AI assistance improved TURP slide review efficiency while maintaining, and in some readers improving, diagnostic accuracy. These findings support AI as a workflow-oriented decision-support tool in TURP pathology, particularly for low-prevalence, high-volume diagnostic settings.\n                  <\/jats:p>","DOI":"10.1007\/s00428-026-04645-5","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T07:21:22Z","timestamp":1784618482000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-assisted TURP slide review: a multi-reader study of efficiency, diagnostic performance, and human\u2013AI error patterns"],"prefix":"10.1007","author":[{"given":"C. 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