通过使用大型语言模型,检索增强生成和代理系统来回答现实世界的临床问题
Yen Sia Low1, Michael L Jackson1, Rebecca J Hyde1
1Atropos Health, New York, NY, USA.
Digital health
|June 13, 2025
概括
专门的大型语言模型 (LLM) 在回答临床问题方面明显优于一般的LLM. 在证据稀缺的情况下,代理的LLM对于生成可操作的答案至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 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:
- 基于证据的医学面临着数据可用性和上下文化方面的挑战.
- 大型语言模型 (LLM) 通过总结文献和从现实数据中生成研究,提供潜在的解决方案.
研究的目的:
- 评估不同LLM系统在回答临床问题的表现.
- 将通用LLM与专用LLM进行比较,包括检索增强生成 (RAG) 和代理系统.
主要方法:
- 对五个LLM系统提出了50个临床问题:OpenEvidence (RAG),ChatRWD (agentic) 和三个通用LLM (ChatGPT-4,Claude 3 Opus,Gemini 1.5 Pro).
- 九名医生评估了LLM生成答案的相关性,证据质量和可操作性.
主要成果:
- 一般目的的LLM仅为2-10%的问题提供了相关的,基于证据的答案.
- 专用系统的性能更高:OpenEvidence (RAG) 在24%,ChatRWD (agentic) 在58%.
- 聊天RWD在为缺乏现有文献 (52%) 的问题提供可操作的答案方面表现出色.
结论:
- 专业的LLM在临床问题答案方面明显优于通用LLM.
- 当现有数据可用时,取回增强生成 (RAG) 系统是有效的.
- 像ChatRWD这样的代理LLM对于在数据稀缺的情况下产生可操作的见解至关重要.
- 未来的系统可以将RAG和代理能力结合起来,以提供全面的证据支持.
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