A comprehensive review of intelligent question-answering systems in traditional Chinese medicine based on LLMs

Large language models (LLMs) are advanced deep learning models with billions or even trillions of parameters, enabling powerful natural language processing and knowledge reasoning capabilities. Their applications in the medical domain have been rapidly expanding, spanning medical research, clinical diagnosis, drug development, and patient management. As a cornerstone of China's healthcare system, traditional Chinese medicine (TCM) faces significant challenges, including difficulties in knowledge extraction, and lack of standardization. The emergence of TCM-focused LLMs presents a transformative opportunity, offering a novel technological framework to process vast amounts of TCM data, uncover hidden theoretical insights, and enhance both research and clinical applications. Despite the growing interest in AI-driven medical solutions, systematic research on LLMs in the TCM domain remains limited. This article provides a comprehensive review of LLM development, detailing their underlying mechanisms, training methodologies, and key technological advancements. It further explores the unique characteristics and diverse application scenarios of existing TCM-LLMs. Additionally, this study also conducts a horizontal comparison of the differences between intelligent question-answering (QA) systems on general LLMs and QA systems on TCM-LLMs, discusses challenges and potential risks, and offers strategic recommendations for future development. By synthesizing current advancements and addressing critical gaps, this work aims to support the continued modernization and intelligent evolution of TCM, fostering its integration into contemporary healthcare systems. • This article is the first systematic review of TCM-LLMs. • Discussed the characteristics of LLMs in four different stages of development. • Summarized and compared the working principles and key technologies of LLMs. • Evaluated the advantages and limitations of open-source and closed-source TCM-LLMs. • Discussed the prospects and potential impact of TCM-LLM applications.