医疗保健的大型语言模型 文本分类:系统审查
1Binghamton University, Binghamton, NY, United States.
JMIR AI
|February 11, 2026
概括
大型语言模型 (LLM) 显著提高了医疗保健文本分类准确性,而不是传统方法. 本综述综合了LLM应用程序的证据,突出了他们在临床决策支持等任务中的卓越表现.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 为自然语言处理任务提供了高级功能.
- 准确有效的文本分类在医疗保健中对于临床笔记分析和诊断编码等应用至关重要.
- 现有的系统性审查没有特别关注医疗保健文本分类的LLM.
研究的目的:
- 系统地审查和批判性地评估有关LLM应用在医疗保健中的文本分类的文献.
- 综合当前关于LLM在医疗保健文本分类中的性能和方法的证据.
主要方法:
- 在主要数据库 (PubMed,Scopus等) 进行了系统的文献搜索. 对于在2018年至2024年之间发表的论文.
- 研究根据分类类型,应用,方法,文本类型和评估指标进行分类,遵循PRISMA指南.
- 65篇符合条件的研究文章被纳入了审查.
主要成果:
- 从2020年到2024年第三季度,出版物显著增加,在2024年前三个季度有28篇论文.
- 微调 (35篇论文) 和快速工程 (17篇论文) 是最常见的LLM方法.
- 在医疗保健文本分类方面,LLM始终优于传统的机器学习方法,特别是在临床决策支持应用中.
结论:
- 与传统的机器学习相比,LLM在医疗保健文本分类方面表现优越.
- 需要进一步的研究来解决文献中发现的空白,并探索未来的方向.
- 该审查提供了对LLM在医疗保健文本分类中的利用的全面概述.
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