The adoption, efficacy, and limitations of large language models in urological education: A cross-sectional survey.
- 1Department of Urology, University of Tabuk, Tabuk, Saudi Arabia.
Abstract
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 collected from medical students and interns. The survey assessed adoption patterns, perceived efficacy on a 7-point Likert-scale, and limitations. The analysis revealed near-universal adoption (98.4%), with 65% of participants using AI tools daily, predominantly ChatGPT. Key applications included generating exam-style questions and summarizing complex guidelines. Participants reported significant perceived improvements in core knowledge areas, with the highest gains in disease knowledge and diagnostics (Median = 7, interquartile range: 6-7), which were strongly correlated with increased clinical confidence. Multivariate regression identified usage frequency and comfort with technology as significant predictors of knowledge improvement (β = 0.29, P = .001) and confidence gains (β = 0.30, P < .001). The primary limitation identified was the potential for AI "hallucinations" or incorrect information (54.1%). Large language models are widely adopted and perceived as highly effective for enhancing knowledge and clinical reasoning in urological education. However, their transformative potential is tempered by critical concerns regarding reliability. A balanced approach that combines these powerful AI tools with vigilant oversight is essential.
Related
- An integrated framework of ChatGPT adoption in higher education using TAM and UTAUT models
- Preclinical-Clinical Discrepancies in Immune Checkpoint Inhibitor Efficacy for Triple-Negative Breast Cancer: Disease Stage, Antibody Properties, Biomarker Alignment, and Model Limitations.
- Physician-Reported Practices and Innovation Adoption in Acute Kidney Injury Care in Saudi Arabia: A Nationwide Cross-Sectional Survey : Short title: Acute Kidney Injury Care and Innovation Adoption in Saudi Arabia.
- General self-efficacy, self-confidence, and satisfaction in simulation-based nursing education: a structural equation modeling and mediation analysis
- Use of quality-of-care and patient-safety policies in the WHO European region: an ecological cross-sectional exploratory analysis of policy adoption and health outcomes
BibTeX
@article{khan2026adoption,
title = {{The adoption, efficacy, and limitations of large language models in urological education: A cross-sectional survey.}},
author = {Wesam Khan},
journal = {Medicine},
year = {2026},
month = oct,
doi = {10.1097/md.0000000000051124},
url = {https://europepmc.org/article/MED/42888594},
note = {CC BY},
}
RIS
TY - JOUR
TI - The adoption, efficacy, and limitations of large language models in urological education: A cross-sectional survey.
AU - Wesam Khan
PY - 2026
DA - 2026/10/01
JO - Medicine
DO - 10.1097/md.0000000000051124
UR - https://europepmc.org/article/MED/42888594
AB - 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 collected from medical students and interns. The survey assessed adoption patterns, perceived efficacy on a 7-point Likert-scale, and limitations. The analysis revealed near-universal adoption (98.4%), with 65% of participants using AI tools daily, predominantly ChatGPT. Key applications included generating exam-style questions and summarizing complex guidelines. Participants reported significant perceived improvements in core knowledge areas, with the highest gains in disease knowledge and diagnostics (Median = 7, interquartile range: 6-7), which were strongly correlated with increased clinical confidence. Multivariate regression identified usage frequency and comfort with technology as significant predictors of knowledge improvement (β = 0.29, P = .001) and confidence gains (β = 0.30, P < .001). The primary limitation identified was the potential for AI "hallucinations" or incorrect information (54.1%). Large language models are widely adopted and perceived as highly effective for enhancing knowledge and clinical reasoning in urological education. However, their transformative potential is tempered by critical concerns regarding reliability. A balanced approach that combines these powerful AI tools with vigilant oversight is essential.
SN - 0304-5412
SN - 1536-5964
ER -