评估大型语言和大型推理模型作为紧急内科医学的决策支持工具
Josip Vrdoljak1, Zvonimir Boban2, Ivan Males3
1University of Split, School of Medicine, Department of Pathophysiology, Split, Croatia.
Computers in biology and medicine
|May 13, 2025
概括
一个先进的大型语言模型 (LLM) 在紧急医疗中展示了专家级的临床性能,在决策支持中与人类医生相匹配. 这显示了LLMs.
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
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 显示出临床决策支持的潜力,但在现实世界中表现有变化.
- 我们对三个领先的LLM (OpenAI的o1,Anthropic的Claude-3.5-Sonnet,Meta的Llama-3.2-70B) 与人类专家进行了比较.
- 这项研究的重点是紧急内部医学环境.
研究的目的:
- 将领先的LLM与人类专家的临床性能进行比较.
- 评估LLM生成的报告,以确定诊断准确性,治疗规划和后续建议.
- 评估高级LLM作为临床决策支持工具在现实世界紧急情况下的实用性.
主要方法:
- 一项前性比较研究,涉及73名匿名患者病例,来自紧急内科病房.
- 两个独立的内科专家评估了LLM和人类撰写的报告.
- 评估包括诊断测试的相关性,最终诊断,治疗计划和随访,使用利克特秤和统计测试.
主要成果:
- 该o1 LLM取得了与人类医生统计学上无法区分的表现 (平均评分为3.63比3.67).
- 克劳德-3.5-索内特 (3.38) 和拉玛-3.2-70B (3.23) 的得分明显较低,在治疗计划中出现了错误.
- 所有LLM在最终诊断和入院决策中都表现出≥90%的准确性,其中o1在分类异常实验室值方面达到100%的准确性.
结论:
- 具有增强推理能力的高级LLM (o1) 可以与紧急医学的专家级临床性能相匹配.
- 在临床决策支持方面,LLM显示出作为有价值的临床决策支持工具的巨大潜力.
- 对LLM在医疗保健中的应用进行进一步研究是有必要的.
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