Home Journals Articles Updates About
CN
image
收藏切换
Exploring Current Status and Development Trends of Machine Learning Research in Traditional Chinese Medicine Field
收藏切换
Wenyu WU1, 2, Shaofeng ZHAN1, 2, Xin JIAO3, Wanning LAN1, 2, Peng WU4, Kai WANG1, 2
Chinese Archives of Traditional Chinese Medicine | 2026, 44(2) : 11 - 16
Less
收藏切换
Chinese Archives of Traditional Chinese Medicine | 2026, 44(2): 11-16
• Digital Traditional Chinese Medicine •
Exploring Current Status and Development Trends of Machine Learning Research in Traditional Chinese Medicine Field
Full
Wenyu WU1, 2, Shaofeng ZHAN1, 2, Xin JIAO3, Wanning LAN1, 2, Peng WU4, Kai WANG1, 2
Affiliations
  • 1.College of First Clinical Medical，Guangzhou University of Chinese Medicine，Guangzhou 510405，Guangdong，China
  • 2.The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine，Guangzhou 510405，Guangdong，China
  • 3.The Second Clinical Medical School of Guangzhou University of Chinese Medicine，Guangzhou 510405，Guangdong，China
  • 4.Jiangsu Provincial Hospital of Traditional Chinese Medicine，Nanjing 210029，Jiangsu，China
Published: 2026-02-10 doi: 10.13193/j.issn.1673-7717.2026.02.003
Outline
收藏切换

Machine learning，as the core driving force of artificial intelligence，brings opportunities and challenges to the development of traditional Chinese medicine in the context of rapid development of information technology. Its combination with traditional Chinese medicine research has significantly promoted the standardization，efficiency and intelligence process of traditional Chinese medicine disease syndrome system，but it is still in the exploration stage. In this paper，it provided an overview of the research progress of machine learning in the field of traditional Chinese medicine from the fields of ancient literature，experience of famous doctors，syndrome-based diagnosis，disease prediction，as well as traditional Chinese medicine and acupuncture，and discussed its future development trend，with a view to providing new methodological ideas for the application of intelligent technologies，such as machine learning，to the study of traditional Chinese medicine.

machine learning  /  artificial intelligence  /  traditional Chinese medicine
吴文玉, 詹少锋, 焦欣, 蓝婉宁, 吴鹏, 王凯. 中医药领域机器学习研究的现状与发展趋势探讨. 中华中医药学刊, 2026 , 44 (2) : 11 -16 . DOI: 10.13193/j.issn.1673-7717.2026.02.003
Wenyu WU, Shaofeng ZHAN, Xin JIAO, Wanning LAN, Peng WU, Kai WANG. Exploring Current Status and Development Trends of Machine Learning Research in Traditional Chinese Medicine Field[J]. Chinese Archives of Traditional Chinese Medicine, 2026 , 44 (2) : 11 -16 . DOI: 10.13193/j.issn.1673-7717.2026.02.003
Appendix
Less
Year 2026 volume 44 Issue 2
106
20
Cite this Article
BibTeX
Article Info
doi: 10.13193/j.issn.1673-7717.2026.02.003
  • Online Date：2026-10-14
  • Published：2026-02-10
Article Data
Affiliations
History
Affiliations
    1.College of First Clinical Medical，Guangzhou University of Chinese Medicine，Guangzhou 510405，Guangdong，China
    2.The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine，Guangzhou 510405，Guangdong，China
    3.The Second Clinical Medical School of Guangzhou University of Chinese Medicine，Guangzhou 510405，Guangdong，China
    4.Jiangsu Provincial Hospital of Traditional Chinese Medicine，Nanjing 210029，Jiangsu，China
References
Share
https://castjournals.cast.org.cn/joweb/zhzyyxk/EN/10.13193/j.issn.1673-7717.2026.02.003
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House