泌尿生殖器病理学における人工知能

Artificial intelligence (AI) is now a practical, value-generating tool in genitourinary (GU) pathology. Real-world deployments report up to 65% time-savings and multi-million-dollar returns on investment within 3 years at high-volume centres. Across prostate, bladder, renal and testicular systems, contemporary algorithms equal or exceed expert accuracy for cancer detection, grading and prognostication. Foundation models trained on millions of whole-slide images now match specialized organ-specific tools without bespoke tuning. High AI-pathologist concordance is widely regarded as a surrogate marker of safety and clinical acceptability, yet no universally codified regulatory threshold for sensitivity, specificity or concordance has been issued. Because internationally recognized guidelines still omit detailed instructions for safe roll-out and sustained performance, we distilled insights from real-world deployments and pioneering pilot studies into two complementary roadmaps: the nine-step VALIDATED framework, which focuses on governance and safety oversight, and the 11-principle ORCHESTRATE blueprint, which guides day-to-day implementation. By 2030, we anticipate AI will automate ~80% of routine quantification, allowing pathologists to assume the role of diagnostic orchestrators who integrate multimodal data streams, helping offset a ~40% workforce shortfall and reducing inter-observer variability across practice settings. This review distils the evidence, economics and practical guidance required for successful AI adoption in GU pathology. Institutions following the VALIDATED-ORCHESTRATE pathway can harness efficiency gains while maintaining diagnostic excellence and achieving positive ROI within 5 years.