From Data Fusion to Decision Support in Prostate Cancer: Evidence Mapping of Multimodal Artificial Intelligence for Diagnosis, Prognosis, and Treatment Selection

Artificial intelligence systems for prostate cancer are increasingly moving beyond single-source image analysis toward models that combine radiology, clinical variables, pathology, molecular measurements, and radiomics. We conducted a PRISMA-ScR-guided evidence map of peer-reviewed English-language studies published from January 2021 to December 2025 and indexed in IEEE Xplore, Scopus, Web of Science, PubMed, or SpringerLink. Eligible studies used AI or machine learning to integrate at least two information sources for prostate cancer detection, grading, staging, prognosis, recurrence prediction, or treatment selection. Two reviewers screened records and extracted study design, clinical task, input modalities, fusion approach, validation strategy, performance, reproducibility, and implementation-relevant reporting. Twenty-six studies met eligibility criteria. Clinical variables were the most frequent non-imaging input (24/26), mpMRI was the dominant imaging source (18/26), and six studies incorporated whole-slide histopathology. Reported paired comparisons usually favoured multimodal approaches, with examples including PI-CAI performance above the median radiologist AUROC (0.91 vs. 0.86) and PET/MRI/clinical models reporting AUC values up to 0.955. However, the evidence remains mostly retrospective, geographically concentrated, weakly reproducible, and rarely calibrated; none of the included studies tested a multimodal AI system prospectively inside a clinical pathway. Current findings support continued development and rigorous evaluation rather than routine deployment. The next phase of research should prioritise prospective pathway studies, diverse external validation, transparent reporting, missing-modality robustness, and patient-centred outcomes.