Patel Ankush U, Parwani Anil V, Satturwar Swati
The Ohio State University, Wexner Medical Center and James Cancer Center, Columbus, OH, USA.
Histopathology. 2026 Jan;88(1):353-373. doi: 10.1111/his.70020.
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.
人工智能(AI)如今已成为泌尿生殖系统(GU)病理学中一种实用且能创造价值的工具。实际应用报告显示,在高业务量中心,人工智能可节省高达65%的时间,并在3年内带来数百万美元的投资回报。在前列腺、膀胱、肾脏和睾丸系统中,当代算法在癌症检测、分级和预后评估方面达到或超过了专家的准确性。基于数百万张全切片图像训练的基础模型,如今无需定制调整就能与专门的器官特异性工具相媲美。高人工智能-病理学家一致性被广泛视为安全性和临床可接受性的替代指标,但尚未发布关于敏感性、特异性或一致性的普遍编纂的监管阈值。由于国际认可的指南仍未包含安全推出和持续性能的详细说明,我们将实际应用和开创性试点研究的见解提炼为两个互补的路线图:侧重于治理和安全监督的九步VALIDATED框架,以及指导日常实施的11条原则的ORCHESTRATE蓝图。到2030年,我们预计人工智能将使约80%的常规定量分析自动化,使病理学家能够承担整合多模态数据流的诊断协调员角色,有助于弥补约40%的劳动力短缺,并减少不同实践环境下观察者之间的差异。本综述提炼了在泌尿生殖系统病理学中成功采用人工智能所需的证据、经济学和实用指南。遵循VALIDATED-ORCHESTRATE途径的机构可以在保持卓越诊断的同时提高效率,并在5年内实现正的投资回报率。