PURPOSE OF REVIEW: The purpose of this review is to characterize contemporary models and metrics for medical education in Urology. RECENT FINDINGS: Innovative models involve using augmented reality and 3D-printing to enhance stimulation-based training; these are cost-effective and improve surgical skill metrics. Virtual didactics are preferred since they offer flexible self-paced learning. Structured frameworks (e.g., PREDICT, GAPS) increase graduated autonomy without negative impact on patient care. For medical students, Urology interest groups and mandatory skills sessions significantly boost interest and confidence in basic procedures, with targeted outreach helping to address disparities. Effective urology education requires standardization, flexibility, and autonomy for residents, and early, equitable exposure for medical students. Integrating evidence-based novel models, supported by technology and Artificial Intelligence for content and feedback, is critical for preparing trainees in the evolving field of Urology. Future research should focus on comparative studies to validate these novel educational models against traditional approaches.
Keywords: Artificial intelligence; Medical education; Surgical simulation training; Urology education; Virtual learning.