Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology
Abstract
Prostate biopsy remains the cornerstone for the diagnosis, risk stratification, and management of prostate cancer. However, biopsy interpretation is challenged by sampling limitations, tumor heterogeneity, and interobserver variability in Gleason grading. Recent advances in digital pathology and artificial intelligence (AI) have created new opportunities to improve diagnostic accuracy, reproducibility, and clinical decision-making. This narrative mini-review summarizes current evidence regarding AI applications in prostate biopsy evaluation. Relevant studies published between 2015 and 2026 were reviewed, focusing on AI-assisted cancer detection, automated Gleason grading, quantitative pathology, prognostic assessment, and multimodal approaches integrating histopathological, molecular, and clinical data. AI-based systems have demonstrated high accuracy in prostate cancer detection, Gleason pattern classification, and tumor burden assessment, with several studies showing strong concordance with expert genitourinary pathologists. These technologies have the potential to improve diagnostic consistency, enhance workflow efficiency, refine risk stratification, and support treatment planning. Emerging multimodal AI models integrating histopathological, genomic, imaging, and clinical information may further improve prognostic assessment and facilitate precision oncology approaches. However, challenges remain, including limited prospective validation, data heterogeneity, regulatory considerations, model interpretability, and integration into routine clinical workflows. AI is emerging as a valuable adjunct in prostate biopsy evaluation, with the potential to enhance diagnostic precision, grading reproducibility, and personalized patient management. Continued multicenter validation, development of explainable AI frameworks, and effective integration into multidisciplinary prostate cancer care pathways will be essential for successful clinical adoption and improved patient outcomes.