大型语言模型的临床决策在何时进行脏活检:比较研究
Michael Toal1, Christopher Hill2, Michael Quinn1
1Centre for Public Health, Royal Victoria Hospital, Queen's University Belfast, Grosvenor Road, Belfast, BT12 6BA, United Kingdom, 44 28 9097 6350.
Journal of medical Internet research
|September 18, 2025
概括
大型语言模型 (LLM) 在复制专家科医生关于活检指示的共识方面表现出不同的能力. 虽然一些LLM与人类决策保持一致,但其他LLM有很大的不同,影响了潜在的临床应用.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 医疗决策支持 医疗决策支持
背景情况:
- 人工智能 (AI) 和大型语言模型 (LLM) 越来越复杂,并融入各种学科.
- 应用LLM来增强临床决策是一个活跃的研究领域.
研究的目的:
- 为了比较1000多名脏病学家的反应与常用LLM关于脏活检指示的输出.
- 与人类专家共识相比,评估不同LLM的调整和风险承受能力.
主要方法:
- 一个大型的国际在线问卷被分配给1000多名科医生,以确定脏活检的指示.
- 同样的问题被问及八个LLM (ChatGPT-3.5,Mistral,困惑,副驾驶,拉玛2,GPT-4,MedLM,克劳德3).
- 答案得分 (0-44) 反映了活检倾向,较高的得分表明更高的风险容忍度.
主要成果:
- 在复制人类专家共识方面,LLM的表现差异很大.
- 开放AI模型 (ChatGPT-3.5,GPT-4) 与人类共识 (6/11个问题) 的一致性最高.
- 在LLM中,风险承受能力不同,MedLM是最不愿承受风险的 (得分11) 和Claude 3是最不愿承受风险的 (得分34).
结论:
- 在脏活检指标中,LLMs表现出模仿人类临床决策的适度能力.
- 在LLM之间性能差异表明现实世界临床实践的局限性.
- 对于具有统一的人类答案的问题,对准度更高,对于具有较少共识的问题,对准度更低.
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