Traditional Chinese medicine (TCM) not only embodies a wealth of medical knowledge and cultural value but also exerts a profound influence on the global health domain. However, due to the antiquity, complexity, and uniqueness of its content, traditional computer-aided diagnosis methods struggle to harness its potential fully. In recent years, with the rapid development of artificial intelligence technology, large language models (LLMs) have gradually been applied to the field of TCM due to their outstanding natural language processing capabilities. This paper investigates the current applications of LLMs in TCM, covering various aspects such as clinical assistance, education assessment, and knowledge mining. We first review the construction of datasets related to TCM, including corpus data from TCM literature, medical case records, and clinical data. Next, we explore the specific applications of LLMs in integrating TCM knowledge, supporting clinical decision-making, and assessing education, analyzing their performance in improving efficiency and optimizing resources. Finally, we discuss the challenges faced by LLMs in TCM applications, such as data scarcity, model adaptability, and standardization of evaluation, and provide an outlook on future research and development prospects.