Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers

Artificial intelligence (AI) has made great changes to prostate cancer screening and early detection across biomarkers, imaging, and pathology. On micro-ultrasound (micro-US), AI improves discrimination and raises specificity at comparable sensitivity versus clinical models, while multimodal magnetic resonance imaging-transrectal US (MRI-TRUS) AI achieves higher specificity at matched sensitivity. Liquid-biopsy programs combine fragmentomics with ctDNA and cell-free mRNA interpreted by AI, enabling noninvasive risk stratification and clinical feasibility. In imaging, AI for MRI matches or exceeds expert radiologists in large reader studies and MRI benchmarks; commercial tools show robust patient- and lesion-level performance. Quantitative pipelines (e.g., automated tissue-composition metrics) aid equivocal Prostate Imaging-Reporting and Data System (PI-RADS 3) lesions with PSA density, and AI-derived intraprostatic tumor volume offers independent prognostic value. Multimodal fusion of MRI with TRUS boosts detection, and automated prostate-specific membrane antigen (PSMA) PET/CT algorithms quantify tumor burden and support longitudinal response tracking. In pathology, clinical-grade AI automates cancer detection and Gleason grading, cutting reading time, ancillary tests, and second-opinion requests, while supporting integrative prognostic models. Downstream, AI accelerates radiotherapy planning, guides focal therapies and surgical margins, personalizes systemic therapy, and enables early post-treatment monitoring. Translation still requires rigorous, prospective, multi-site validation.