为医疗保健决策支持建立可信的大型语言模型驱动的生成推系统:对语料库来源,定制技术和评估框架的范围审查
Shuqi Yang1, Mingrui Jing1, Shuai Wang2
1School of Nursing, Fudan University, Shanghai, China.
Artificial intelligence in medicine
|November 28, 2025
概括
大型语言模型驱动的生成推系统 (LLM-GRSs) 越来越多地用于临床问答. 它们在医疗保健中的安全和可靠应用取决于基于证据的机构,透明的设计和标准化的评估.
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
- 在医疗保健中的自然语言处理.
背景情况:
- 大型语言模型驱动的生成推系统 (LLM-GRSs) 是医疗保健中的新兴工具,特别是用于临床问答.
- 本综述考察了医学应用中的LLM-GRS的语料库来源,定制技术和评估指标.
研究的目的:
- 系统地审查应用到医疗和医疗保健信息传递的LLM-GRS的文献.
- 识别这些系统中使用的共同的语料库来源,定制策略和评估指标.
主要方法:
- 在PubMed,Embase,Scopus和Web of Science (2021年1月至2025年8月) 进行了系统的文献搜索.
- 包括专注于通过对话接口支持临床决策的LLM的研究.
- 数据库来源,模型架构,定制和评估指标的数据被提取和分析.
主要成果:
- 包含了61篇文章,使用了各种各样的数据库来源 (临床数据,文献,开放数据集,网络数据),经常结合多个来源.
- 定制方法包括快速工程,检索增强生成和微调,通常在组合中使用.
- 评估指标跨越了过程,可用性和结果领域,结果指标涉及基于模型和手动评估.
结论:
- 在医疗保健环境中,LLM-GRS显示出巨大的潜力.
- 确保安全性和可靠性需要基于证据的培训数据,透明的系统设计和标准化的评估协议,以实现现实世界的临床实施.
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