Artificial intelligence in genitourinary pathology.

Area of Science:

• Digital pathology
• Computational pathology
• Genitourinary (GU) pathology

Background:

• Artificial intelligence (AI) is a proven tool in genitourinary (GU) pathology, demonstrating significant time savings and financial returns.
• Current AI algorithms achieve expert-level accuracy in detecting, grading, and prognosing cancers across prostate, bladder, renal, and testicular systems.
• Foundation models trained on extensive whole-slide image datasets now perform comparably to specialized organ-specific AI tools.

Purpose of the Study:

• To provide practical guidance for the safe and effective adoption of AI in GU pathology.
• To present two roadmaps, VALIDATED and ORCHESTRATE, derived from real-world deployments and pilot studies.
• To address the lack of universally codified regulatory thresholds and detailed implementation guidelines for AI in pathology.

Main Methods:

• Distillation of insights from real-world AI deployments and pilot studies in GU pathology.
• Development of the nine-step VALIDATED framework for AI governance and safety oversight.
• Creation of the 11-principle ORCHESTRATE blueprint for day-to-day AI implementation.

Main Results:

• AI adoption can lead to up to 65% time savings and multi-million-dollar returns on investment.
• AI algorithms demonstrate equal or superior accuracy to expert pathologists in cancer detection, grading, and prognostication.
• By 2030, AI is projected to automate approximately 80% of routine quantification tasks, aiding in workforce shortages and reducing variability.

Conclusions:

• Successful AI adoption in GU pathology requires robust governance and practical implementation strategies.
• The VALIDATED-ORCHESTRATE pathway enables institutions to achieve efficiency gains, diagnostic excellence, and positive ROI within five years.
• AI integration is poised to transform GU pathology by automating tasks and empowering pathologists as diagnostic orchestrators.