Review and Commentary on Digital Pathology and Artificial Intelligence in Pathology.

Purpose: This Special Article provides a comprehensive review and expert commentary on the prospective clinical implementation of artificial intelligence (AI) in the detection of prostate cancer from digital prostate biopsies, as presented in the original research by Flach et al. It contextualizes the study within broader developments in digital pathology and AI, addressing barriers to adoption and the implications for diagnostic workflows and pathology practice.

Design: Drawing on insights from the CONFIDENT-P trial and the author's own experience with digital pathology and AI-assisted workflows, this article critically examines the clinical, regulatory, economic, and operational dimensions of implementing AI in diagnostic pathology. The focus centers on real-world deployment, particularly the integration of Paige Prostate Detect AI (PPD-AI) and its influence on immunohistochemistry (IHC) utilization.

Results: The commentary highlights the trial's prospective design as a significant advancement in AI validation. Key findings include a reduction in IHC use, high diagnostic performance of PPD-AI, and improved diagnostic confidence among AI-assisted pathologists. However, variability in IHC practices across institutions, limitations in AI generalizability, and the need for system integration remain major challenges. The article also addresses practical issues such as automation bias, model drift, and lack of interoperability between viewers and laboratory information systems.

Conclusion: The adoption of AI in digital pathology is accelerating but requires thoughtful integration into clinical workflows. Although prostate biopsies represent an ideal entry point, broader success will depend on regulatory alignment, workforce training, infrastructure readiness, and data governance. This commentary underscores the importance of clinician-AI synergy and provides practical guidance for laboratories navigating the transition from pilot implementations to scalable clinical use.