Artificial Intelligence in Digital Pathology for Prostate Cancer Detection: FDA Clearance and Real-World Implementation

Abstract

Artificial intelligence (AI) has emerged as a promising adjunct in surgical pathology, particularly in the diagnosis of prostate cancer, where variability in interpretation or missed cancer foci can significantly affect patient management. This review provides a concise, practice-oriented overview of the two Food and Drug Administration (FDA)-cleared AI tools for prostate biopsy interpretation: Paige Prostate Detect and Ibex Prostate Detect (formerly Galen Second Read). We examine their regulatory indications, diagnostic performance and integration requirements within digital pathology workflows. Emphasis is placed on real-world implementation considerations, including variation in technical inputs and the level at which data are analysed. We highlight less obvious risks, such as domain shift and the potential for inequitable performance in under-represented patient populations. Trade-offs between sensitivity and specificity, particularly in the context of AI-assisted pathologist assessments, are discussed using data from clinical validation studies. We also consider the variable impact of AI tools depending on the user’s expertise, noting enhanced diagnostic consistency for general pathologists. By highlighting both the opportunities and limitations of integrating AI into routine practice, we aim to provide pathologists with a pragmatic understanding of how these systems may influence diagnostic workflows and to emphasise that FDA clearance must be complemented by local validation as well as ongoing performance monitoring to ensure safe and equitable deployment.