评估和缓解临床决策中大型语言模型的局限性
Paul Hager1,2, Friederike Jungmann3,4, Robbie Holland5
1Institute for AI and Informatics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. paul.hager@tum.de.
Nature medicine
|July 4, 2024
概括
大型语言模型 (LLM) 是有前途的,但尚未为自主临床决策做好准备. 他们难以准确诊断,遵守指南和整合工作流程,这对患者的安全构成风险.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 医生的职责包括临床关键决策.
- 人工智能 (AI) 和大型语言模型 (LLM) 提供了潜在的好处.
- 目前的LLM评估 (例如,医疗执照考试) 不反映现实世界的临床复杂性.
研究的目的:
- 评估LLMs对自主临床决策的准备程度.
- 在超越标准化测试的现实临床场景中评估LLM绩效.
- 为未来的医疗保健LLM研究开发数据集和框架.
主要方法:
- 来自重症监护医疗信息中心 (MIMIC) 数据库 (2400例,4种腹部病理) 的精选数据集.
- 模拟一个现实的临床环境LLM评估框架.
- 评估诊断准确性,遵守指导方针,实验室解释和工作流集成.
主要成果:
- 在病理学方面,LLM的诊断准确性明显低于医生.
- 临床医生未能遵守诊断和治疗指南.
- 由于跟随指令的问题和对信息顺序/数量敏感性,LLM对实验室结果的解释很差,并且不容易被纳入临床工作流程.
结论:
- 目前的LLM不适合自主临床决策,因为其具有显著的性能限制.
- 如果LLM自主部署,就会对患者健康构成严重风险.
- 开发的数据集和模拟框架可以指导未来在临床环境中对AI的研究.
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