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<p>With the continuous advancement of medical technologies, perioperative anesthesia management decisions are confronted with challenges of complexity and dynamicity, rendering traditional machine learning models insufficient for clinical needs. As one of the breakthrough applications in artificial intelligence, large language models (LLMs) may offer better help for intelligent anesthesia management. LLMs are capable of processing multidimensional and multi-source data, enabling more comprehensive prediction and intervention suggestions, thereby optimizing anesthesia management processes. This review summarizes the current applications of LLMs in anesthesiology and proposes methods for building specialized LLMs to tackle challenges in application. This work albeit mainly in the proposed stage aims to provide references for future development in this field and to promote in-depth research of LLMs in anesthesiology.</p> Graphical Abstract <p></p>

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Applications and challenges of large language models in anesthesiology: narrative review and future perspectives

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Yiziting Zhu,
  • Xin Shu,
  • Xiang Liu,
  • Yujie Li,
  • Bin Yi,
  • Daqing Ma

摘要

With the continuous advancement of medical technologies, perioperative anesthesia management decisions are confronted with challenges of complexity and dynamicity, rendering traditional machine learning models insufficient for clinical needs. As one of the breakthrough applications in artificial intelligence, large language models (LLMs) may offer better help for intelligent anesthesia management. LLMs are capable of processing multidimensional and multi-source data, enabling more comprehensive prediction and intervention suggestions, thereby optimizing anesthesia management processes. This review summarizes the current applications of LLMs in anesthesiology and proposes methods for building specialized LLMs to tackle challenges in application. This work albeit mainly in the proposed stage aims to provide references for future development in this field and to promote in-depth research of LLMs in anesthesiology.

Graphical Abstract