一种类型的医生输入方法来使用人工智能聊天机器人进行临床决策
Rachel Siden1, Hannah Kerman2,3, Robert J Gallo4,5
1Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA. rsiden@stanford.edu.
NPJ digital medicine
|December 5, 2025
概括
在临床推理任务中使用大型语言模型 (LLM) 聊天机器人的医生没有表现改善. 医生如何将临床病例数据输入到LLM聊天机器人并没有影响测试成绩.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 聊天机器人越来越多地被整合到临床工作流程中.
- 以前的研究表明,医生在临床推理测试中使用LLM并不总是比单独的聊天机器人表现更好.
- 了解医生与LLM的交互模式对于优化其使用至关重要.
研究的目的:
- 探索医生如何在临床推理任务中使用LLM聊天机器人.
- 为了确定是否提供给LLM的临床病例信息的数量会影响医生的表现.
- 在使用LLM聊天机器人时确定常见的医生输入策略.
主要方法:
- 与美国医生进行了半结构化采访,询问他们对LLM聊天机器人的经验.
- 根据采访数据开发了医生输入模式的类型学.
- 分析了来自两个随机对照试验的医生聊天日志,根据输入方法对病例进行分类.
- 采用线性混合效应模型,在不同的输入方法中比较临床病例得分.
主要成果:
- 确定了四种医生输入方法类型:复制粘贴器 (整个案例),选择性复制粘贴器 (案例摘录),总结器 (用户生成的摘要) 和搜索器 (简要查询).
- 复制粘贴和搜索是最常用的方法.
- 没有特定的输入方法与更高的临床病例得分有显著的关联.
结论:
- 医生在使用LLM聊天机器人的临床推理任务上的表现不仅仅取决于输入方法.
- 诸如提示策略,认知参与和输出解释等因素可能发挥更重要的作用.
- 未来的研究应该调查这些细微的因素,以加强在临床实践中有效使用LLMs.
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