Artificial intelligence (AI) is increasingly influencing the field of urologic oncology, offering novel tools to support for clinical decision-making, enhance diagnostic precision, and assist in surgical and pathological workflows. Machine learning (ML) and deep learning (DL) approaches-artificial neural networks, particularly convutional ones-have demonstrated potential across various urologic malignancies, with applications ranging from imaging interpretation and tumor grading to risk stratification and operative planning. While prostate cancer remains the most explored domain, growing interest surrounds AI's use in bladder and renal tumors, and more recently in testicular and penile cancers. Moreover, the integration of AI into robotic surgery and medical writing is opening new frontiers in performance evaluation and patient communication. Despite these advances, critical limitations persist. Issues such as data heterogeneity, lack of external validation, ethical and legal ambiguity, and algorithmic bias continue to hinder widespread adoption. This narrative review examines current developments in AI across major genitourinary cancers, highlighting both clinical opportunities and unresolved challenges in translating these technologies into practice.
Cicchetti et al. (Fri,) studied this question.