aceThe adoption, efficacy, and limitations of large language models in urological education: A cross-sectional survey
The integration of large language models into medical education is rapidly transforming pedagogical approaches, particularly in specialized fields like urology. This study aims to evaluate the adoption patterns, perceived efficacy, and significant limitations of these artificial intelligence (AI) tools among urology trainees. This research employed a descriptive, cross-sectional study design through an online survey disseminated to urology trainees at a single institution. A total of 194 complete responses were…
More than half of surveyed trainees flagged AI hallucinations or incorrect information as the main limitation of LLMs in their urological study.
The data come from a single institution and rely on self-reported perceived efficacy rather than measured learning outcomes, so the benefits cannot be confirmed as actual improvements.
Evidence
- Peer-reviewedMedicine2026-10-16
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Truvace Impact Record TRV-2026-1677, v1: “The adoption, efficacy, and limitations of large language models in urological education: A cross-sectional survey.” Truvace, 2026-10-17. /record/TRV-2026-1677 (accessed at citation time). sha256 124a0af5ed6c6808…
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