Validation, implementation, and impact of an AI model in routine practice for pathologic diagnosis of prostate cancer in an academic medical center

Background: Pathological evaluation of prostate needle biopsies is labor-intensive and often requires ancillary immunohistochemistry (IHC), increasing cost and diagnostic turnaround time (TAT). Artificial intelligence (AI)-based decision-support tools may improve efficiency, but clinical deployment requires institutional validation and assessment of real-world impact. Methods: Within a fully digital pathology practice, we performed institutional validation of an AI-assisted prostate biopsy decision-support tool using routine clinical cases, with pathologist-rendered diagnoses as ground truth. Following validation, the tool was implemented into the routine sign-out. A retrospective pre-post analysis compared prostate biopsy cases signed out during 3-month periods before and after implementation, excluding a transition month. Diagnostic TAT was defined as the interval from whole-slide image scan completion to final sign-out. IHC utilization was recorded. Weighted median TATs and IHC use were compared using standard statistical methods. Results: The validation cohort met all predefined acceptance criteria, demonstrating high AI performance (sensitivity 91-100%, specificity 99%, positive-predictive value 98%, negative-predictive value 96%, area under the curve 0.97). Following clinical implementation, diagnostic TAT decreased by 30% and IHC utilization decreased by 38%. Conclusions: Institutional validation and clinical implementation of an AI-assisted prostate biopsy decision-support tool were associated with significant reductions in diagnostic TAT and IHC utilization. When deployed as an adjunct within a digital workflow, AI assistance may enhance efficiency while preserving pathologist responsibility for final diagnosis.