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

Abstract

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

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