Pathologic 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. 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 routine sign-out. A retrospective pre–post analysis compared prostate biopsy cases signed out during three-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. 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 turnaround time decreased by 30% and immunohistochemistry utilization decreased by 38%. Institutional validation and clinical implementation of an AI-assisted prostate biopsy decision-support tool were associated with significant reductions in diagnostic turnaround time and IHC utilization. When deployed as an adjunct within a digital workflow, AI assistance may enhance efficiency while preserving pathologist responsibility for final diagnosis. • Institutional validation demonstrated high AI accuracy for prostate biopsy diagnosis • Validation performance met acceptance criteria (sensitivity, specificity, AUC 0.97) • AI decision-support was integrated into routine digital pathology sign-out • Diagnostic turnaround time decreased by 30% following AI implementation • Immunohistochemistry utilization decreased by 38% after implementation
Udoh et al. (2026) studied this question.