大型语言模型对管理推理的影响:一个随机对照试验
Ethan Goh1,2, Robert Gallo3, Eric Strong4
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
medRxiv : the preprint server for health sciences
|August 16, 2024
概括
大型语言模型 (LLM) 人工智能 (AI) 与传统资源相比,显著改善了医生管理推理. 这种人工智能协助增强了临床决策,特别是在复杂的患者病例中.
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
- 医学教育和培训 医学教育和培训
背景情况:
- 大型语言模型 (LLM) 显示出诊断推理的潜力,但它们对复杂的管理决策的影响尚不清楚.
- 医生在开放式的临床管理任务中的表现通常依赖于从各种来源合成信息.
研究的目的:
- 评估LLM辅助是否提高了医生在开放式临床管理推理任务中的表现.
- 为了比较LLM增强资源与单独使用传统资源的有效性.
主要方法:
- 一项前性随机对照试验,涉及多个机构92名医生 (临床医生和住院医生).
- 参与者使用GPT-4 (通过ChatGPT Plus) 与传统资源或仅使用传统资源来管理专家开发的五个临床病例小组.
- 通过Delphi过程创建分数标签,以评估管理和诊断决策.
主要成果:
- 使用LLM辅助的医生获得了明显更高的整体得分 (增加6.5%,p<0.001).
- 在管理 (6.1%),诊断 (12.1%) 和具体案例 (6.2%) 决策领域,有所改善.
- 每个案例的LLM用户花费的时间更长 (119.3秒),GPT-4增强组和GPT-4单独组之间没有显著差异.
结论:
- 通过LLM辅助,可以明显改善医生管理的推理,特别是在情境和患者特定的决策方面.
- 这些发现表明,LLM可以作为增强复杂医疗场景中的临床管理的宝贵工具.
- 像LLM这样的AI工具的整合有望提高医生的表现和患者护理.
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