在大型语言模型中评估和改善综合症差异化思维能力:方法开发研究
Chunliang Chen1, Xinyu Wang1, Ming Guan1
1East China Normal University, No.3663 Zhongshanbei Road, Shanghai, China, 86 18621306726.
JMIR medical informatics
|June 20, 2025
概括
大型语言模型 (LLM) 对推进传统中医药 (TCM) 的发展有前途. 用专门的指令数据微调LLM显著改善了他们的综合征差异化思维,模仿专家临床推理.
科学领域:
- 人工智能的人工智能
- 计算医学是一种计算医学.
- 传统中国医药 传统中国医药
背景情况:
- 大型语言模型 (LLM) 为传统中医药 (TCM) 的智能发展提供了新的途径.
- 综合症差异化是TCM的核心组成部分,将这种能力整合到LLM中对于临床应用至关重要.
- 传统医学综合征分化的复杂性对当前的法学士来说是一个重大挑战.
研究的目的:
- 评估LLMs的综合征差异化思维能力.
- 开发和评估一种方法,以提高LLM在TCM综合征差异化方面的表现.
主要方法:
- 分解TCM综合征分化为致病发生推断,综合征推断和诊断建议.
- 创建了一个高质量的评估数据集,包含200个标准化的医疗病例.
- 开发了一种"开放式考试"方法,使用定制模板和动态知识检索来生成专业指导数据,以微调LLMs.
主要成果:
- 拟议的微调方法显著提高了LLM在综合征差异化任务中的性能.
- 微调的模型在病变发生推断方面达到85.7%的准确率,在综合征推断方面达到81.2%.
- 该模型的诊断建议与专家意见相似84.3%,表现优于现有的一般和TCM特定的LLM.
结论:
- 目前的LLM在TCM综合征分化方面表现出局限性.
- 微调的LLM与量身定制的指令数据和专业知识大大提高了他们的诊断推理能力.
- 优化的LLM证明了专家水平的一致性,为TCM临床实践提供了重要的理论和实际价值.
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