Abstract Background ChatGPT-4.0 and the ChatGLM series are novel conversational large language models (LLMs). ChatGLM includes 3 versions: ChatGLM4 (with internet connectivity but no knowledge base pretraining), ChatGLM4+Knowledge base (combining internet search capabilities with knowledge base pretraining), ChatGLM3-6B (offline knowledge base pretraining but no internet connectivity). The ability of ChatGPT-4.0 and ChatGLM to apply medical knowledge in the Chinese environment has been preliminarily verified, but the potential of the 2 models for clinical assistance in traditional Chinese medicine (TCM) is still unknown. Objective This study aims to explore the performance of ChatGPT-4.0, ChatGLM4, ChatGLM4+Knowledge base, and ChatGLM3-6B in providing AI-assisted diagnosis and treatment for metabolic dysfunction-associated fatty liver disease within a TCM clinical framework, thereby assessing their potential as TCM clinical decision support tools. Methods This study evaluated 4 LLMs by providing them with medical records of 87 metabolic dysfunction-associated fatty liver disease cases treated with TCM and querying them about TCM treatment plans. The answering texts from 4 LLMs were evaluated using predefined scoring criteria, focusing on 3 critical dimensions: ability in syndrome differentiation and treatment principles, confusion of concepts between TCM and Western medicine, and comprehensive evaluation of question-answering texts (comprising 6 components: ability to integrate Chinese and Western medicine, ability to formulate treatment plans, health management capacity, disease monitoring ability, self-positioning awareness, and medication safety). Results In the evaluation module of “Ability in syndrome differentiation and treatment principles,” the performance ranking of the 4 models was: (1) ChatGLM4+ Knowledge Base, (2) ChatGLM4, (3) ChatGLM3-6B, and (4) ChatGPT-4.0. Regarding the assessment of confusion between TCM and Western medicine concepts, ChatGPT-4.0 exhibited conceptual confusion in 32 out of 87 cases, while the ChatGLM series of LLMs showed no such confusion (except for ChatGLM3-6B, which had 1 instance). In the “Comprehensive evaluation of question-answering texts” module (comprising 6 components: ability to integrate Chinese and Western medicine, ability to formulate treatment plans, health management capacity, disease monitoring ability, self-positioning awareness, and medication safety), the ranking was: (1) ChatGLM4+ Knowledge Base, (2) ChatGPT-4.0, (3) ChatGLM4, and (4) ChatGLM3-6B. Conclusions Our study results demonstrated that real-time internet connectivity played a critical role in LLM-assisted TCM diagnosis and treatment, while offline models showed significantly reduced performance in clinical decision support. Furthermore, pretraining LLMs with TCM-specific knowledge bases while maintaining internet search capabilities substantially enhanced their diagnostic and therapeutic performance in TCM applications. Importantly, general-purpose LLMs required both domain-specific medical fine-tuning and culturally sensitive adaptation to meet the rigorous standards of TCM clinical practice.
Wang et al. (Mon,) studied this question.