从临床笔记中提取信息的基于生成AI的方法:范围审查
Chaima Abdellaoui1, Akram Redjdal2, Brigitte Seroussi1,3
1Sorbonne Université, INSERM, Université Sorbonne Paris Nord, LIMICS, Paris, France.
Studies in health technology and informatics
|July 1, 2025
概括
大型语言模型 (LLM) 显示出临床数据提取的前景,其中GPT-4表现出色. 在临床广泛使用之前,需要进一步研究以解决可靠性和隐私方面的问题.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地被采用.
- 挑战包括临床文档分析中的可靠性,幻觉和数据隐私.
研究的目的:
- 评估LLM在从临床文档中提取结构化数据方面的有效性.
- 在这个领域评估即时工程策略和模型性能.
主要方法:
- 对2019年至2025年间发表的16项研究进行了范围审查.
- 专注于LLM在从临床实践指南和笔记中提取数据方面的表现.
主要成果:
- GPT-4表现出卓越的性能,在16项研究中的11项中领先.
- 在多项研究中,实体提取的准确度/F1分数达到了>85%.
- 在不同的临床文档类型中,性能有所不同.
结论:
- LLM,特别是GPT-4,显示出临床数据提取的巨大潜力.
- 性能变化和数据隐私需要进一步的研究和部署的伦理考虑.
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