Rapid advancements in artificial intelligence (AI) have significantly impacted health care, notably within the field of urology, where generative AI presents opportunities to enhance patient education, streamline clinical workflows, and support research activities. This prospective, cross-sectional study systematically investigated the adoption of generative AI among urology professionals, exploring usage patterns, perceived benefits, and barriers to broader implementation. The survey was developed by an expert panel from the Endourology Section of the European Association of Urology and collaborators from the Progressive Endourological Association for Research and Leading Solutions. Utilizing a structured survey disseminated internationally, the study collected responses from 243 urology professionals, predominantly specialists and mid-career practitioners from Europe, Latin America, and Asia. Findings indicate substantial engagement with generative AI, with approximately 53% of respondents reporting prior experience using AI tools in clinical practice. Patient education emerged as the most favored application, highlighted by 55% of respondents within the patient interaction category. Clinical decision-making, particularly treatment recommendations (44%), and research support (18%) were other key areas of usage. However, 82% of participants expressed concerns regarding the technical reliability of generative AI, and 76% worried about diagnostic errors. Notably, about 25% of respondents felt no health care functions should be entirely replaced by AI, emphasizing its role as complementary to human expertise rather than a substitute. Primary barriers identified included inadequate training (20%), technical limitations (14%), and insufficient empirical evidence validating effectiveness (12%). Addressing these barriers through targeted training, rigorous validation, and clear regulatory frameworks is essential for fully realizing AI's potential in urological practice.
Keywords: ChatGPT; artificial intelligence; digital health; ethics; machine learning; urology.