AI in Primary Diagnosis for Prostate cancer in Real-World Settings

Juan Carlos Santa-RosarioUnited States Speaker AI for Precision Pathology in Prostate Cancer Diagnosis in a Real-World SettingBackground: Accurate and timely prostate cancer diagnosis is crucial yet challenged by rising case volumes, inter-observer variability, and diagnostic delays. Artificial intelligence (AI) offers a compelling solution when integrated effectively into real-world pathology workflows. Objectives: To assess the real-world performance, diagnostic impact, and workflow integration of an AI-based second-read tool over a three-year validation period in routine prostate cancer diagnostics. Methods: A clinically deployed AI algorithm was implemented as a second reader for prostate biopsy specimens in a high-volume pathology setting. Key metrics included diagnostic accuracy, concordance rates, turnaround time (TAT), and clinical utility of AI-assisted reads versus standard practice. Results: Over three years of real-world use, the AI system consistently enhanced diagnostic performance. It reduced diagnostic discordances, improved overall concordance, and contributed to more efficient workflows with shorter TATs. These outcomes demonstrate AI's viability as a supportive second reader in routine pathology practice. Conclusions: This study validates that AI, when embedded as a second-read system, can bolster diagnostic consistency, efficiency, and quality in prostate cancer pathology. Its success in real-world clinical settings underscores AI’s readiness to augment pathologists’ expertise and drive precision diagnostics.