使用人工智能聊天机器人作为区域麻醉的临床决策支持:新时代的"电子咨询"准备好了吗?
David Corpman1, David B Auyong2, Drew Walters3
1Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, USA dcorpman@uw.edu.
Regional anesthesia and pain medicine
|July 3, 2025
概括
大型语言模型 (LLM) 显示出潜力,但尚未准备好在区域麻醉学中临床使用. 目前基于LLM的聊天机器人在麻醉场景的判断和决策方面表现出弱点.
科学领域:
- 麻醉学 麻醉学
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能 (AI) 融入临床实践的兴趣日益增长.
- 在区域麻醉中进行外科手术期间的决策存在复杂的挑战.
- 对人工智能作为区域麻醉学临床决策支持工具的有限调查.
研究的目的:
- 评估区域麻醉学中大语言模型 (LLM) 的性能.
- 在临床情景和管理问题提示时评估LLM的能力.
- 确定基于LLM的聊天机器人准备好协助区域麻醉.
主要方法:
- 一项试点研究涉及四个流行的基于LLM的聊天机器人.
- 提示聊天机器人基本和复杂的区域麻醉学临床场景.
- 对特定于区域麻醉学的聊天机器人反应的定性审查.
主要成果:
- 所有四个LLM平台都在临床判断和决策逻辑方面表现出重大缺陷.
- 在复杂的临床场景中,表现缺陷更为明显.
- 在LLM的回复中观察到共的证据 (产生虚假信息).
结论:
- 目前基于LLM的聊天机器人对于协助区域麻醉学是不够可靠的.
- 在基本或复杂的麻醉场景中,LLM还没有准备好支持临床决策.
- 在人工智能工具可以安全地在这个子专业中实施之前,需要进一步开发.
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