Guo Yan, Wang Heyuan, Ren Xue, Wang Tengjiao, Chen Wei, Xu Ziming, Ge Hui
Xiyuan Hospital, China Academy of Chinese Medicinal Sciences, Beijing, China.
School of Computer Science, Peking University, Beijing, China.
J Evid Based Med. 2025 Mar;18(1):e70004. doi: 10.1111/jebm.70004.
Intelligent traditional Chinese medicine (TCM) is a key pathway toward the modernization and globalization of TCM in the era of artificial intelligence. Due to its unique terminology and diagnostic framework, TCM's intelligentization process has long faced a range of challenges, from the digitization and formalization of knowledge bases to the differentiation of syndromes and personalized treatment. Recently, the advent of large language models (LLMs) like GPTs has marked a transformative milestone in semantic understanding tasks, attracting widespread attention from the medical, academic, and industrial communities. Nonetheless, LLMs often suffer from accuracy and logical reasoning limitations within specific fields and may manifest hallucinations in the generative outputs. Through a comprehensive review of existing literature and empirical analyses, this study delves into the potential and challenges of adapting LLMs to TCM. Promising perspectives on future developments at this innovative intersection are discussed.
智能中医药是人工智能时代中医药现代化和全球化的关键路径。由于其独特的术语和诊断框架，中医药的智能化进程长期面临一系列挑战，从知识库的数字化和形式化到证候辨别与个性化治疗。最近，像GPT这样的大语言模型的出现标志着语义理解任务中的一个变革性里程碑，引起了医学、学术界和产业界的广泛关注。尽管如此，大语言模型在特定领域往往存在准确性和逻辑推理限制，并且在生成输出中可能出现幻觉。通过对现有文献的全面综述和实证分析，本研究深入探讨了将大语言模型应用于中医药的潜力和挑战。并讨论了在这个创新交叉领域未来发展的前景。
