Elucidating the transformative role of large language models in advancing anesthesiology education.

Authors

Yu Zhu, Daoqing Xie, Renrui Liang, Jian-Jun Yang, Xueke Du, Cheng-Mao Zhou

Published in

The Journal of international medical research. Volume 54. Issue 9. Pages 3000605261483195. Epub Sep 07, 2026.

Abstract

This narrative review synthesizes evidence published from 2020 to 2026 on the implementation and educational impact of large language models in anesthesiology training, following the Scale for the Assessment of Narrative Review Articles guidelines. We outline core technical attributes of large language models and identify four validated anesthesiology-specific use cases: standardized learning resource generation, clinical scenario simulation, personalized remediation of knowledge gaps, and automated assessment tool development. Current evidence suggests that these applications improve trainee knowledge scores and reduce faculty workload. Key deployment barriers include the risk of hallucination in high-stakes anesthesia content and potential overreliance on large language models, which may impair independent clinical reasoning. We propose targeted mitigation strategies and a forward-looking research agenda for structured large language model integration. Our analysis confirms that large language models are high-value enabling tools for anesthesiology education and require intentional, guideline-aligned integration to maximize synergies.

PMID:
42704109
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.

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