胃TCM:一个大型语言模型助手,用于传统中医药中的胃肠病学
Lan Wang1, Kaiqiang Tang2, Zhichuan Yang3
1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Department of Automation, BNRIST, Tsinghua University, Beijing, China.
Chinese medicine
|January 22, 2026
概括
作为一种专门的大型语言模型 (LLM) 助手的 GastroTCM 增强了传统中医 (TCM) 的胃肠病诊断. 通过整合检索增强生成 (RAG) 和代理框架,它克服了LLM的局限性,以改善临床决策支持.
科学领域:
- 人工智能在医学中的应用
- 传统中国医学 (TCM) 是一种
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 为传统中医 (TCM) 实践提供了潜力.
- 当前的法学士面临TCM的局限性,包括知识差距,幻觉和弱多转推理.
- 需要专门的AI工具来弥合TCM临床应用中的这些差距.
研究的目的:
- 开发和评估GastroTCM,一个基于LLM的助理TCM胃肠病学.
- 解决临床TCM环境中一般LLMs的局限性.
- 提高诊断准确度,减少TCM胃肠病学中的错误.
主要方法:
- 精心调整了一个Llama3-8B模型,配备了检索增强生成 (RAG) 和代理框架.
- 开发了一个专门的TCM胃肠道病学载体数据库,用于知识检索.
- 使用ShareGPT风格的方法和一个自适应代理,优化了多轮对话.
- 在2000万个临床记录,指南和专家问答对上受过培训.
主要成果:
- 在自动评估中,GastroTCM在自动评估中表现优于强大的中国LLM基线 (BLEU得分:0.334比0.172-0.246).
- 获得了显著更高的临床适当的多转交互率 (27/60 vs ≤2/60病例).
- 专家审查证实了诊断准确性和安全性的提高,RAG减少了幻觉.
结论:
- 像GastroTCM这样的域特定,检索增强的LLM可以增强TCM从业者.
- 胃肠道医疗疗证明了在现实世界TCM胃肠道学中提供高质量,可解释的决策支持的潜力.
- 这种方法显著改善了专业医疗领域的一般LLM.
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