Artificial Intelligence in MRI for Urologic Oncology: A Systematic Review of Diagnostic Accuracy and Clinical Utility

Artificial intelligence (AI) integrated with MRI is rapidly transforming urologic oncology by enhancing lesion detection, risk stratification, and workflow efficiency. However, existing evidence remains fragmented across tumor sites and model types. This systematic review aimed to synthesize the diagnostic accuracy and clinical utility of AI applied to MRI in prostate, kidney, and bladder cancers, while evaluating study quality using the Quality Assessment of Diagnostic Accuracy Studies-Artificial Intelligence (QUADAS-AI) tool. Following a registered protocol and PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, comprehensive searches of PubMed, Cochrane, Scopus, and Web of Science identified 4,442 records; after removing duplicates and screening, 14 studies met the inclusion criteria. Two reviewers independently screened studies, extracted data on model type, diagnostic tasks, cohorts, comparators, and reference standards, and assessed risk of bias. In prostate MRI, AI systems - including deep learning, radiomics, and zone-specific computer-aided detection models - demonstrated diagnostic accuracy comparable to expert Prostate Imaging-Reporting and Data System (PI-RADS) assessment, with an area under the receiver operating characteristic curve (AUC) of 0.82-0.89 and sensitivities of 91-95%, enabling biopsy-reduction strategies without compromising detection of clinically significant cancers. In kidney MRI, ensemble residual neural network (ResNet) and convolutional neural network (CNN)-based models differentiated benign from malignant lesions with sensitivities near 0.92 and AUCs around 0.90, often matching or surpassing radiologist performance, particularly in identifying oncocytomas. For bladder MRI, radiomics, and federated deep-learning frameworks accurately distinguished muscle-invasive from non-muscle-invasive disease, achieving AUCs up to 0.93 and demonstrating strong cross-center generalizability. Segmentation networks achieved high geometric accuracy (Dice similarity coefficient >0.90), reducing contouring time, while cost analyses indicated AI-assisted MRI as a dominant, economically favorable alternative to standard imaging pathways. Overall, AI-assisted MRI achieves diagnostic performance comparable to expert interpretation across prostate, kidney, and bladder cancers, offering added value in biopsy triage, staging, segmentation, and potential cost savings. Future efforts should focus on standardized reporting, external validation, prospective impact evaluation, and assessment of pricing and reimbursement models to ensure safe and equitable clinical adoption.