改善医生在患者护理任务上的表现的GPT-4辅助:一个随机对照试验
Ethan Goh1,2, Robert J Gallo3,4, Eric Strong4
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Nature medicine
|February 5, 2025
概括
大型语言模型 (LLM) 在复杂病例中显著改善了医生管理推理. 这项研究表明,与传统方法相比,LLM辅助增强了临床决策.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持系统 临床决策支持系统
- 医生绩效评价 医生的绩效评价
背景情况:
- 大型语言模型 (LLM) 显示出诊断推理的潜力.
- 在医生管理推理上LLM的影响,包括治疗和测试策略,仍然在很大程度上未被探索.
- 有效的管理推理对于优化患者护理和资源配置至关重要.
研究的目的:
- 评估LLM辅助在改善医生在临床管理推理任务上的表现方面的有效性.
- 用LLM增强资源与传统资源在模拟环境中比较医生的表现.
- 评估LLM协助对每个案件所花费时间的影响.
主要方法:
- 这是一项前性随机对照试验,涉及92名执业医生.
- 随机分配医生使用GPT-4与常规资源或仅使用常规资源.
- 参与者回应了专家开发的五个临床图片,这些图片在模拟环境中顺序呈现.
主要成果:
- 使用LLM辅助的医生在管理推理任务中获得了明显更高的分数 (平均差异6.5%,P<0.001).
- 每个案例的LLM用户花费的时间更长 (平均差异为119.3秒,P = 0.02).
- 在LLM增强医生和单独使用LLM的医生之间没有观察到显著的绩效差异.
结论:
- 在复杂的临床场景中,LLM辅助明显提高了医生管理推理技能.
- 这些发现表明,LLM可以成为增强临床决策的有价值工具.
- 在现实世界的临床实践中进一步验证是有必要的,以确认这些好处.
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