| Multimodal Artificial Intelligence in Urologic Precision Oncology; From Algorithm to Translational Medicine |
| Paper ID : 1324-IGA |
| Authors |
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Nazila Bahmaie *1, Farid Rajaee Rizi2, Maryam Sadat Jamadi3, Samin Rahimi4, Moein Bighamian5, Pouya Paidar6, Mohammad Javad Taki7 1Department of Medical Biology, Faculty of Medicine, Ankara Yildirim Beyazit University (AYBU), 06800 Ankara, Turkey. 2Research Assistant, Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Department of Urology, Al-Zahra University-Affiliated Hospital, Isfahan University of Medical Sciences, Isfahan, Iran. 3Department of Obstetrics and Gynecology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. 4Department of Genetics, Faculty of Natural Sciences, Tabriz University, Tabriz, Iran. 5Department of Urology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. 6Department of Computer Engineering, Faculty of Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Ankara, Turkey. 7Department of Medical Physiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. |
| Abstract |
| 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 accreditation of the 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 aforesaid systems. However, major barriers hinder clinical adoption. Prospective validation remains scarce, data harmonization across institutions is limited, and the opaque nature of many algorithms fuels the 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–AI workflows. Ultimately, successful translation will depend not only on technical progress but also on redefining trust and expertise in urologic oncology, ensuring that algorithmic insights are meaningfully aligned with bedside decision-making. |
| Keywords |
| Artificial Intelligence, Genitourinary Malignancies, Multimodal Algorithms, Molecular Medicine, Precision Uro-oncology, Risk Stratification, Translational Medicine. |
| Status: Accepted |
