Abstract
The emergence of generative artificial intelligence (AI), particularly large language models and image-generation tools, is poised to transform the field of urology. These technologies enable innovative applications in medical education, clinical decision support, patient communication, and surgical planning, extending beyond traditional analytical AI by creating new content, from synthetic clinical notes to simulated surgical environments. In urology, these capabilities translate into automated summarization of complex patient histories, generation of patient-specific three-dimensional anatomical reconstructions, and support for differential diagnosis in conditions such as prostate cancer or renal masses. However, the rapid adoption of generative AI also introduces significant ethical, legal, and clinical challenges. Risks are amplified in urological practice, where sensitive imaging data, biomarker profiles, and diagnostic decision pathways may be vulnerable to privacy breaches, algorithmic bias, or erroneous AI-generated recommendations. Hallucinated outputs, such as incorrect treatment summaries or misinterpreted radiologic features, can directly compromise patient safety if not rigorously validated. This review synthesizes the current landscape of generative AI in urology, critically examines these discipline-specific risks, and proposes a structured framework for responsible integration into clinical workflows. We highlight the need for transparent governance, bias mitigation, prospective validation, and interdisciplinary collaboration to ensure that generative AI enhances, rather than undermines, the quality, equity, and safety of urologic care.