Current status and future prospects of artificial intelligence in traditional Chinese medicine
Journal Title: Biomedical Transformation - Year 2022, Vol 3, Issue 3
Abstract
Traditional Chinese Medicine (TCM) has not been widely accepted in the world for the lack of objective data and complete theoretical system. In addition, the ambiguous of language, the difficulty in understanding theoretical knowledge, the abstractness of therapeutic mind, and the complexity of medical records have also brought great challenges to the development of TCM modernization. With the rapid development of information technology, big data and Artificial Intelligence (AI) technology have provided new methods for standardizing TCM diagnosis and treatment data, building an intelligent TCM diagnosis and treatment system and breaking through TCM diagnosis and treatment model, further promoting the development of TCM intellectualization. After more than half a century of development, the integration of TCM and AI technology has gradually deepened and certain application results have been achieved, such as the information database of TCM, four diagnosis collection equipment of TCM, the clinical auxiliary diagnosis and treatment system of TCM and intelligent health management of TCM, etc. However, for now, there are still some problems in the intellectualized development of TCM, such as the lack of data standards, the insufficient improvement of related systems and the lack of talents with interdisciplinary backgrounds, etc. The focus of future work is to establish normative data standards, improve relevant laws and regulations on data sharing, intellectual property rights, and cord of ethics, so as to promote the innovative development of TCM in the AI background. The study summarizes the progress of AI in TCM from the perspective of knowledge discovery and machine learning, in order to provide assistance for the intellectualization of TCM.
Authors and Affiliations
Li Xinyi, Luo Siyan, Xu Changsheng, Ding Guiguang, Zhang Yongdong, Tian Guihua
Keywords
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- EP ID EP766848
- DOI 10.12287/j.issn.2096-8965.20220310
- Views 9
- Downloads 0
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