AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials

IntroductionArtificial intelligence (AI) is increasingly applied in prostate cancer screening and diagnostic evaluation; however, the structure, methodological characteristics, and clinical positioning of AI-focused trials remain incompletely characterized. This study aimed to map the clinical trial landscape of AI applications in prostate cancer diagnosis using registry-based evidence mapping.MethodsA registry-based evidence-mapping analysis was conducted using ClinicalTrials.gov. Trials registered up to 15 November 2025 were systematically identified using search terms related to prostate cancer and AI-based methodologies. Eligible studies included interventional and observational trials evaluating AI applications for diagnostic purposes. Data were extracted on study design, diagnostic modality, functional role of AI, comparator framework, and validation strategy. Descriptive statistics and cross-tabulation analyses were used to characterize patterns across studies. The study selection process was presented using a PRISMA-style flow diagram.ResultsA total of 84 trials met the inclusion criteria. Imaging-based AI applications predominated, accounting for 52.4% of studies, with magnetic resonance imaging (MRI) representing the most frequently investigated modality (34.5%). Biomarker-based (16.7%), multimodal (15.5%), and computational pathology (7.1%) approaches were less frequently reported. The most common functional applications were classification and risk prediction (48.8%) and lesion detection and segmentation (29.8%). Most studies employed prospective observational designs (84.5%) and frequently relied on stand-alone AI evaluation frameworks (39.2%). Histopathology or biopsy confirmation was the most commonly reported reference standard (56.0%). Only a limited number of trials incorporated workflow integration or clinical decision-support evaluation.ConclusionAI research in prostate cancer diagnostics appears to be primarily centered on imaging-based, early-phase, and performance-oriented studies. Current evidence suggests that AI systems are predominantly positioned as decision-support tools rather than fully integrated clinical solutions. Greater emphasis on multicenter validation, standardized reporting, and clinically relevant outcome evaluation may be required to support broader clinical implementation.