JOURNAL ARTICLE
Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.
Published In: American Journal of Chinese Medicine, 2024, v. 52, n. 3. P. 605 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Pan, Danping; Guo, Yilei; Fan, Yongfu; Wan, Haitong 3 of 3
Abstract
Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:American Journal of Chinese Medicine. 2024/03, Vol. 52, Issue 3, p605
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:0192-415X
- DOI:10.1142/S0192415X24500265
- Accession Number:177537786
- Copyright Statement:Copyright of American Journal of Chinese Medicine is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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