BEIJING, CHINA / RankWire.AI / – Artificial intelligence is increasingly penetrating traditional Chinese medicine education, research, and clinical applications across China. Beijing University of Chinese Medicine has created a dedicated large model centered on comprehensive TCM knowledge and educational resources. The Xinhuo TCM system encompasses classical texts, medical theories, herbal data, prescriptions, and teaching materials. The initial version was launched in 2025, with enhancements made to the platform throughout 2026.
According to the university, Xinhuo TCM now functions as a 70-billion-parameter system built upon leading Chinese artificial intelligence platforms. Its developers designed the model to facilitate learning, teaching, research, administrative tasks, and international exchanges related to traditional Chinese medicine. Students can inquire about herbal combinations and receive detailed explanations of formulas and their fundamental principles. Additionally, the university has developed an intelligent robot capable of demonstrating traditional massage techniques for practical instruction purposes.
The application of AI in China’s traditional Chinese medicine sector extends beyond education and research to include medical knowledge management and clinical decision support tools. Digital systems are capable of organizing historical medical literature and structuring practitioner expertise for educational and research needs. Researchers are also exploring AI applications for diagnostic data analysis, pre-consultation systems, and other decision-making support tools. These initiatives exemplify a broader effort to integrate traditional medical knowledge with modern data and computational technologies.
China boosts digital infrastructure development for TCM
Within its national traditional medicine development strategy for 2026 to 2030, China has incorporated artificial intelligence as a key component. The National Administration of Traditional Chinese Medicine together with the National Development and Reform Commission issued a plan in July emphasizing the need for high-quality AI datasets specifically tailored for the traditional Chinese medicine sector. The strategy also promotes the development of digital infrastructure, intelligent traditional medicine hospitals, and diagnostic support systems at primary healthcare institutions.
This national framework emphasizes the importance of data governance alongside the deployment of AI tools across the traditional medicine system, calling for enhanced data classification, protection, and secure circulation within healthcare, research, and related digital services. It also advocates for increased adoption of standardized intelligent devices and digital technologies in clinical practice, education, scientific research, and the management of traditional medicine data.
AI technologies transition from educational tools to clinical support systems
Research published in 2026 has investigated the performance of large language models within traditional Chinese medicine healthcare environments. One study assessed an AI pre-consultation system at a tertiary traditional medicine hospital and found initial positive responses from physicians, who valued its ability to gather information prior to consultations more than its decision-support functionalities. However, the study also identified ongoing challenges related to capturing patient complaints, integrating workflows, managing documentation, and ensuring accessibility for elderly patients.
The recent developments indicate that artificial intelligence is expanding across multiple facets of China’s traditional Chinese medicine framework. Xinhuo TCM has obtained registration for national generative AI services, positioning the specialized model within China’s regulatory landscape for public AI applications. Current uses include education, research, knowledge management, hospital workflows, and auxiliary diagnostic tools. The country’s 2026 to 2030 TCM plan explicitly assigns a formal role to AI datasets and digital medical infrastructure in fostering the sector’s growth and modernization.