Artificial intelligence in urological education: a systematic review
Abstract
Background: Artificial intelligence (AI) is currently widely applied across various fields, especially in the field of education where it holds significant implications. This systematic review aims to explore the specific role of AI in urological education.
Methods: Following the PRISMA 2020 guidelines, we searched the PubMed, Scopus, and Web of Science databases for articles related to AI in urological education published up to February 10, 2026. The quality of included studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI), and educational outcomes were classified based on the modified Kirkpatrick framework.
Results: A total of 15 eligible study records were finally included. The use of AI in urology education can be categorized into two groups: interventions that are used directly in the educational process, and enabling technologies that provide support for education. It was showed that combining AI in urology education had a positive impact on both learners and teachers. For learners such as medical students, urology residents, or interns, the AI-based hybrid teaching model increased learning efficiency and improved students' theoretical exam scores. Meanwhile, the combination of high-fidelity virtual reality simulation training and intelligent AI feedback showed potential in facilitating the acquisition of clinical skills, in which simulation scores were positively correlated with surgical potential. On the other hand, as the…
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