The Role of Artificial Intelligence in Optimizing Diagnosis in Prostate Cancer—A Narrative Review

Abstract

Artificial intelligence (AI) is increasingly being investigated in prostate cancer (PCa) diagnosis and characterization, offering novel approaches to improve detection and risk stratification. This narrative review summarizes current evidence regarding the application of AI across the major stages of PCa management, with particular emphasis on radiomics and pathomics. Radiomics enables the extraction of high-dimensional quantitative features from medical imaging modalities, including ultrasound, computed tomography, multiparametric magnetic resonance imaging (mpMRI), and prostate-specific membrane antigen positron emission tomography (PSMA PET), providing imaging biomarkers that extend beyond conventional visual interpretation. Numerous studies have demonstrated that AI-based radiomic models improve the detection of clinically significant PCa, characterize tumor aggressiveness, predict extracapsular extension, and support individualized treatment selection. Among available imaging modalities, mpMRI remains the cornerstone for radiomics owing to its superior soft-tissue characterization, whereas PSMA PET radiomics has shown particular promise for assessing biologically aggressive disease and metastatic spread. Pathomics has further expanded the role of AI by enabling automated tumor detection, grading, quantification, and identification of adverse pathological features, with promising performance reported in selected retrospective validation studies. Despite encouraging results, widespread clinical implementation remains limited by heterogeneous imaging protocols, variability in data acquisition and annotation, lack of standardized workflows, insufficient prospective multicenter validation, and ethical and regulatory challenges. The aim of this review was to summarize current evidence on AI-based imaging analysis, radiomics, and pathomics for PCa detection, characterization, risk stratification, and pathological assessment, while highlighting the methodological challenges that currently limit clinical implementation.