人们在大型语言模型中转移临床行动值.
Eyal Klang1,2, Alon Gorenstein1,2, Mahmud Omar1,2
1The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, NY, USA.
medRxiv : the preprint server for health sciences
|January 8, 2026
概括
系统提示在大型语言模型 (LLM) 中显著改变临床决策,即使患者数据相同. 这凸显了监管LLM提示作为部署配置的关键需求.
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) 的临床部署正在从评估可行性转向解决责任.
- 当前的指导往往将LLM行为作为一个控制问题.
- 系统提示对临床判断的影响需要进一步调查.
研究的目的:
- 确定决策式系统提示是否可以改变LLMs中的临床行动值.
- 评估这些转变在不同临床环境和LLM架构中的一致性.
- 评估医生人物对LLM决策的影响.
主要方法:
- 根据伦理取向和认知风格定义了九个医生人格.
- 通过使用2500个模拟ED vignettes和2500个MIMIC-IV-Note排放摘要,评估了20个开放权重的LLM.
- 模型在基线和人格提示下对每个文本的五个二元决策项目做出了回应,产生了超过500万个决策.
主要成果:
- 个性提示将肯定决策率从36.9%转移到46.4%,在固定的临床证据下,这一变化为9.5个百分点.
- 促成效应在自主性和治疗决策方面最为明显.
- 快速诱导的转移在不同的文本体中是一致的,但易感性因LLM而异,没有医学微调或更大的模型大小的明显好处.
结论:
- 决策类型的系统在与一致的临床事实呈现时,促使在LLM中明显改变临床行动值.
- 促成功能作为制定政策的机制,而不仅仅是一个沟通工具.
- 系统提示应被视为临床LLM的主要部署配置.
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