The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.
Xiaoqing Zhang (Mon,) studied this question.