一种类型的医生输入方法来使用AI聊天机器人进行临床决策:一种混合方法研究
Rachel Siden1, Hannah Kerman2,3, Robert J Gallo4,5
1Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
medRxiv : the preprint server for health sciences
|August 8, 2025
概括
医生在输入信息到大型语言模型 (LLM) 聊天机器人时使用各种方法进行临床推理. 没有单一的输入方法,如复制粘贴或总结,通过这些AI工具提高了医生的表现.
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:
- 大型语言模型 (LLM) 聊天机器人显示高准确性,但医生使用并不总是提高临床推理得分.
- 医生与临床任务的LLM聊天机器人的互动模式尚不清楚.
研究的目的:
- 在使用LLM聊天机器人进行临床推理时,识别和分类医生输入方法.
- 确定特定的输入方法是否与增强的临床推理性能相关.
主要方法:
- 一项混合方法研究,涉及半结构面试和分析医生-聊天机器人交互日志.
- 开发一个类型的医生输入方法 (复制粘贴,选择性复制粘贴,总结器,搜索器).
- 使用线性混合效应模型进行统计分析,以在输入类型之间比较性能.
主要成果:
- 确定了四种不同的医生输入方法:复制粘贴器,选择性复制粘贴器,总结器和搜索器.
- 复制粘贴和搜索是最常见的输入方法.
- 在任何特定的输入方法类型和改善临床病例表现之间没有发现显著的关联.
结论:
- 医生与LLM聊天机器人的互动是直观的,但又多样化.
- 在医疗决策中有效使用人工智能工具需要针对医生提供有针对性的培训和支持.
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