Urodynamic investigations, including cystometry, pressure-flow studies, and electromyography, are considered the gold standard for the objective diagnosis of lower urinary tract dysfunction according to current international guidelines. However, accurate interpretation requires simultaneous analysis of multiple pressure signals, identification of artifacts, and application of complex nomograms, making urodynamics one of the most challenging diagnostic skills to master during urology residency training. Traditional training largely depends on apprenticeship-based exposure, which is highly variable across training centers. The primary aim of this prospective educational study is to evaluate the effectiveness of a large language model (LLM), as an interactive tutor in improving urology residents' urodynamic interpretation skills and learning curve. By providing structured theoretical instruction, case-based guidance, and real-time feedback through a standardized case pool, this study investigates whether AI-assisted mentorship can accelerate skill acquisition, enhance diagnostic accuracy, and offer a standardized, accessible educational model for urodynamic training.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Improvement in Urodynamic Interpretation Accuracy
Timeframe: From baseline (pre-test) to post-test (approximately 4 weeks)