STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
Research on the Development of an AI-based Information Platform for Traditional Chinese Medicine Education
DOI: https://doi.org/10.62517/jike.202504213
Author(s)
Yuhan Zhang
Affiliation(s)
Chengde Medical University, Chengde, Hebei, China
Abstract
Traditional Chinese Medicine (TCM) education faces challenges including abstract theoretical frameworks, insufficient practical resources, and difficulties in achieving standardized teaching. This study aims to explore the integrated application of knowledge graphs, natural language processing (NLP), and computer vision technologies to develop an AI-driven TCM teaching platform. The platform is designed to enhance instructional efficiency and improve students' clinical thinking abilities in TCM. By constructing a tripartite AI teaching model that integrates "TCM syndrome differentiation–skill training–classical knowledge inheritance", this research seeks to establish a unified framework for bridging theoretical learning, practical competency development, and cultural heritage preservation in TCM education.
Keywords
Artificial Intelligence; Chinese Medicine Education; Knowledge Graphs; Syndrome Differentiation and Treatment; Virtual Reality; Machine Learning
References
[1] Jianmin Li. The Cognitive Logic of Chinese Medicine's Image-Based Thinking and Its Modern Transformation. Philosophical Research, 2019, (8), 112-120. [2] Xiaochen Fu, Ziyang Liu, Lingbo Hou. Current Situation and Prospect of Comparative Research on the Construction of Chinese and Western Medicine from the Perspective of Culture. Shi Zhen, Chinese Medicine and Pharmacy, 2023, 34 (7), 1689-1691. [3] Yuxuan Fang, Mingyi Shao, Rongrong Zhang, et al. Research on the Inheritance and Transformation of Chinese Medicine Classic Prescriptions Based on Medical Case Data. Science and Technology Review, 2024, 42 (21), 66-69. [4] Jianguang Xu's team. The Design and Implementation of the Virtual Digital Human System for Zhongjing Fangzheng. Journal of Shanghai University of Traditional Chinese Medicine, 2024, 38 (5), 12-18. [5] Ali l-Naji, Joy Shahriar. Computer-Aided Tongue Diagnosis for Chronic Disease Prediction. Technologies, 2024, 12 (8), 214. [6] Yizhuo Zhang, Yan Sun. Construction and Application of a Knowledge Graph of Shang Han Lun (Treatise on Cold Damage) Categories. China Digital Medicine, 2025, 20 (1), 89-92. [7] Yijie Song, Suoya Ma, Yasheng Dai, Jun Lu. Key Issues and Technical Challenges of AI-Assisted Chinese Medicine Syndrome Differentiation. Chinese Journal of Engineering Science, 2024, 26 (2), 234-244. [8] Shuyuan Lin, Chang Liu, Yu Li, Lingyong Cao. Challenges of Traditional Chinese Medicine in the AI Era and Research Ideas for the Intelligentization of Classic Prescriptions. Chinese Journal of Traditional Chinese Medicine, 2019, 34 (2), 448-451. [9] Xinlong Li. Data Standardization Bottlenecks and Solutions in the Research of Intelligent Chinese Medicine Diagnosis and Treatment. Chinese Journal of Traditional Chinese Medicine, 2024, 39 (3), 1123-1126. [10] Yujie Zhou, Bin Ye. Research on the Cultivation of Clinical Thinking in Traditional Chinese Medicine in the Era of Artificial Intelligence. Chinese Journal of Ethnic Medicine and Pharmacy, 2023, 23 (1), 115-118.
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