
This study presents a comprehensive review of Artificial Intelligence (AI) applications across the diagnostic and prognostic spectrum of urological malignancies, highlighting the paradigm shift toward multi-modal data integration in precision oncology. By synthesizing evidence on the convergence of medical imaging, digital pathology, and genomic data, it explores how AI algorithms can mitigate diagnostic variability and enhance predictive accuracy for prostate, bladder, and renal cell cancers. The review critically examines the current landscape of AI-driven tools, from single-modality models to advanced multi-modal systems, while addressing the technological, regulatory, and ethical challenges that must be overcome to translate these innovations into routine clinical practice. It underscores the potential of AI to augment clinical decision-making and pave the way for a new era of personalized, data-centric urological care.
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