诊断推理提示揭示了医学中大语言模型解释性的潜力
Thomas Savage1,2, Ashwin Nayak3,4, Robert Gallo5,6
1Department of Medicine, Stanford University, Stanford, CA, USA. tsavage@stanford.edu.
NPJ digital medicine
|January 24, 2024
概括
大型语言模型 (LLM) 现在可以模仿临床医生的推理,以准确诊断. 这一发展使得LLM在患者护理中更加可解释和值得信赖.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 被认为是临床决策中的"黑子",阻碍了它们在医疗保健中的采用.
- 法学士推理的不可解释性与人类的认知过程不同,造成了信任障碍.
- 医生需要透明度来评估和信任人工智能驱动的临床见解.
研究的目的:
- 调查大型语言模型 (LLM) 是否可以模仿临床诊断推理.
- 确定LLM是否可以在模仿临床医生的思维过程的同时保持诊断准确性.
- 探索使LLM临床决策更易于解释的方法.
主要方法:
- 为LLMs开发特定的诊断推理提示.
- 评估GPT-4复制常见临床推理模式的能力.
- 当LLM使用提示推理时,对诊断准确性的评估.
主要成果:
- 当被提示时,GPT-4成功模仿了临床医生的推理过程.
- 这些推理提示的使用并没有影响诊断的准确性.
- 该研究表明,LLM可以为诊断提供可解释的理由.
结论:
- 用诊断推理提示LLM可以减轻"黑子"限制.
- 可解释的LLM推理增强了信任,并促进了医生评估.
- 这种方法使LLM更接近安全有效的临床应用.
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