Applying the hallmarks of artificial intelligence to genitourinary medical oncology: an evidence-maturity framework for precision cancer care

Artificial intelligence (AI) is increasingly reshaping genitourinary (GU) medical oncology across the cancer-care continuum. In this structured narrative review, we apply the ten-hallmark precision-oncology taxonomy proposed by Chang et al. to GU malignancies. We further define GU-specific hallmark boundaries and introduce study-design and validation descriptors that span proof-of-concept, internal and external validation, prospective clinical evaluation, and prospective interventional evaluation, while distinguishing evidence derived from randomized trials. Across prostate, urothelial, and kidney cancers, AI has shown promising technical performance in imaging, pathology, molecular inference, and outcome modeling. General-oncology trial-matching benchmarks offer additional research opportunities, although evidence remains uneven. Among the examples reviewed, validation designs highly vary, and strong discrimination should not be interpreted as evidence of clinical benefit. We therefore emphasize cohort size, validation design, head-to-head comparator evidence, calibration when reported, and implementation considerations including regulation, model drift, fairness, clinician oversight, cybersecurity, workflow integration, and cost. The next phase of AI in GU oncology will depend on demonstrating incremental clinical utility and patient benefit, not only higher accuracy.