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传统中医学的数字辅助临床决策:对5个大型语言模型的比较研究

Weiwei Liu¹, Shuchang Miao², Qun Ma¹

¹Department of Preventive Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.

JMIR formative research

|March 12, 2026

概括

大型语言模型 (LLM) 在传统中医 (TCM) 临床决策方面表现有前途,而DeepSeek-R1在知识和病例分析方面表现出色. 人与人工智能的协作显著提高了TCM护理的质量和效率.

科学领域:

综合医学是一个整体的医学.
医疗保健中的人工智能
传统中国医学 (TCM) 是一种

背景情况:

传统中医 (TCM) 临床决策因其复杂性而面临标准化和质量保证方面的挑战.
大型语言模型 (LLM) 为整合医学知识和临床推理提供了潜力,但它们在TCM中的应用尚未得到充分探索.

研究的目的:

评估5个当代LLM在TCM临床决策中的表现.
评估人 - 人工智能 (AI) 合作的好处,与TCM的独立方法相比.
确定最佳的LLM并评估人类-AI合作的质量,效率和可接受性.

主要方法:

通过160个标准化问题和30个病例的临床案例分析,对5个主流的LLM进行了TCM知识评估.
最佳模型的选择是基于加权得分 (40%的知识,60%的临床分析).
人与人工智能的协作由10名中医医师和2名专家在5个临床病例中进行评估,比较了只有医生,只有人工智能和协作方法.

主要成果:

DeepSeek-R1在知识评估方面表现出卓越的性能,准确度为96.7%,在临床病例分析方面得分高 (P<.001).
人与人工智能的协作显著提高了决策质量16.1%,时间减少了66.1% (P<.001).
人工智能辅助在处方配方和药物选择方面显示出最大的好处,用户接受度很高 (76.8 系统可用性评分).

结论:

法律法规,特别是DeepSeek-R1,在TCM知识评估和临床病例分析方面具有显著的能力.
人与人工智能的合作大大提高了TCM临床决策的质量和效率,医生接受度很高.
人工智能辅助决策提供了一个有希望的解决方案,以提高TCM标准化,培训和医疗保健提供效率.

关键词:

人工智能的人工智能是人工智能.临床决策临床决策临床决策支持临床决策支持数字健康数字健康人与人工智能的协作大型语言模型.医疗信息学是一门医学信息学专业.传统的中国医药传统的中国医药.

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