{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:15:22Z","timestamp":1785248122682,"version":"3.55.0"},"reference-count":217,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Precision oncology in urology increasingly depends on integrating heterogeneous data, including multiparametric imaging, histopathology, genomics, and clinical variables. Multimodal artificial intelligence (AI) offers a unified framework to manage this complexity, supporting refined risk stratification, personalized treatment decisions, and informed patient counseling. This narrative review examines applications of multimodal AI in prostate, bladder, and kidney cancers. Beyond listing individual tools, we emphasize how synergistic data fusion enhances the validation of diagnostic and prognostic performance. Clinical advances include more accurate tumor delineation on multiparametric MRI and predictive modeling of functional outcomes after surgery, underscoring the translational potential of these systems. However, major barriers hinder clinical adoption. Prospective validation remains scarce, data harmonization across institutions are limited, and the opaque nature of many algorithms fuels skepticism among clinicians. These factors collectively restrict the integration of multimodal AI into routine clinical practice. Closing this gap requires standardized data curation, development of interpretable and transparent models, and the design of collaborative human\u2013AI workflows. 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