在医疗保健中用于大型语言模型的检索增强生成:系统性审查
Lameck Mbangula Amugongo1, Pietro Mascheroni1, Steven Brooks2
1Biostatistics and Data Sciences Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany.
PLOS digital health
|June 11, 2025
概括
检索增强生成 (RAG) 通过使用外部数据来增强医疗保健的大型语言模型 (LLM). 本综述评估了RAG方法,数据集和评估,强调了医疗人工智能的标准化框架和伦理考虑的需要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在医疗保健中显示出潜力,但面临着诸如过时数据,幻觉和缺乏透明度等局限性.
- 检索增强生成 (RAG) 通过整合外部知识来源来解决LLM的局限性.
- 目前缺乏对医疗保健中的RAG应用,数据集和评估的系统理解.
研究的目的:
- 系统地审查基于RAG的医疗保健领域LLM的方法.
- 在医疗保健RAG系统中分析检索,增强和生成策略.
- 确定医疗应用现有的RAG文献中的局限性,优势和研究缺口.
主要方法:
- 在医疗保健中应用到LLM的RAG方法的综合文献综述.
- 分析使用的数据集,RAG技术 (原始,高级,模块化) 和LLM模型 (例如GPT-3.5/4).
- 评估框架的评估和考虑在审查的研究中的伦理影响.
主要成果:
- 大多数审查的研究 (78.9%) 使用英语数据集,较小的部分 (21.1%) 使用中国数据集.
- 像GPT-3.5/4这样的专有模型主要用于医疗保健中的RAG.
- 他们发现,医疗保健RAG中显著缺乏标准化的评估框架,对伦理考虑的关注不足.
结论:
- RAG在改善医疗保健中的LLM绩效方面表现有前途,但目前的实施缺乏标准化和道德监督.
- 进一步的研究对于开发可靠的评估指标和解决在临床环境中负责任地采用人工智能的伦理挑战至关重要.
- 需要标准化框架和伦理准则,以确保在医学中安全有效地部署基于RAG的LLM.
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