JMIR Publications

ABSTRACT

Background:

Multimodal artificial intelligence (AI) may improve clinically significant prostate cancer (csPCa) detection and risk stratification by integrating imaging, histopathology, molecular, radiomic, and structured clinical data.

Objective:

This scoping review mapped recent evidence on multimodal AI for prostate cancer diagnosis, staging, prognosis, and treatment personalisation, with emphasis on data modalities, fusion strategies, validation, reproducibility, and clinical translation.

Methods:

A PRISMA-ScR-guided search of IEEE Xplore, Scopus, Web of Science, PubMed, and SpringerLink identified English-language peer-reviewed studies published from January 2021 to December 2025. Eligible studies applied AI to at least two data sources for prostate cancer detection, grading, staging, prognosis, recurrence prediction, or treatment selection. Two reviewers screened records and charted clinical task, modalities, fusion strategy, validation design, performance, and reproducibility indicators.

Results:

Twenty-six studies were included. Clinical variables were incorporated in 24 studies, multiparametric magnetic resonance imaging in 18, and whole-slide histopathology in six. Multimodal models usually outperformed unimodal baselines in paired comparisons, including PI-CAI performance above the median radiologist AUROC (0.91 vs 0.86) and trimodal PET/MRI/clinical models reporting AUC values up to 0.955. Evidence was limited by retrospective designs, small cohorts, geographic concentration, scarce public code, inconsistent calibration reporting, and no prospective workflow validation.

Conclusions:

Multimodal AI is promising for biopsy triage, grading, staging, prognostication, and treatment selection, but current evidence supports research prioritisation rather than routine deployment. Prospective, diverse, transparent, and clinically embedded validation is required before multimodal AI can guide routine prostate cancer decisions.