作为大众的医疗助理的LLM的可靠性:一项随机预先注册的随机研究
Andrew M Bean1, Rebecca Elizabeth Payne2,3,4, Guy Parsons1,5
1Oxford Internet Institute, University of Oxford, Oxford, UK.
Nature medicine
|February 9, 2026
概括
大型语言模型 (LLM) 在医疗保健中表现有希望,但在公众使用医疗建议时失败. 用户互动挑战意味着LLM在现实医学场景中表现不如对照组.
科学领域:
- 人工智能在医学中的应用
- 人与计算机的交互
- 临床决策支持 临床决策支持
背景情况:
- 医疗保健提供者正在调查公共医疗咨询的大型语言模型 (LLM).
- 法学士在医学考试中表现出色,但现实世界的表现尚未得到证实.
- 用户互动动态可能会影响医疗保健环境中的LLM有效性.
研究的目的:
- 评估LLM在协助公众进行医学诊断和处置方面的有效性.
- 在模拟的医疗场景中,将LLM协助与对照组进行比较.
- 确定在公共医疗咨询中部署LLM的挑战.
主要方法:
- 一项受控研究涉及1298名参与者,评估了10个医疗场景.
- 参与者得到了LLM (GPT-4o,Llama 3,Command R+) 或他们自己的资源 (控制) 的帮助.
- 性能通过条件识别和处置选择的准确性来衡量.
主要成果:
- 只有LLM能够准确地识别疾病 (94.9%) 和情绪 (56.3%).
- 使用LLM的参与者在识别疾病 (<34.5%) 或情绪 (<44.2%) 方面没有比对照组有任何改善.
- 与独立的LLM功能相比,用户交互显著降低了LLM性能.
结论:
- 在模拟医疗场景中LLM的表现并不能转化为有效的公共援助,因为用户交互挑战.
- 目前的基准没有预测与人类用户的LLMs的现实世界失败.
- 在部署LLM用于公共卫生保健咨询之前,系统的人类用户测试至关重要.
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