[Application practice and exploration of artificial intelligence technology in entire industrial chain of traditional Chinese medicine resources].

With the growing awareness of public health, the value and importance of traditional Chinese medicine(TCM) resources have become increasingly prominent. Despite the undeniable significance of TCM in medical treatment and healthcare, the protection, development, and utilization of TCM resources still face numerous challenges. Under the traditional model, the development and utilization of TCM resources heavily rely on manual labor and empirical decision-making, which not only leads to inefficiencies and high costs but also causes serious issues such as unstable drug quality and imbalances in market supply and demand. In the current era of rapid advancements in artificial intelligence(AI) and technology, AI has emerged as a new engine to address many challenges and difficulties throughout the entire TCM resource industry chain. By leveraging AI technology, intelligent management, precise production, and optimized utilization of TCM resources can be achieved, thereby improving efficiency, reducing costs, ensuring stable quality, and balancing market supply and demand. This article primarily explores the application of AI technology in the entire TCM resource industry chain from different perspectives and provides an in-depth analysis of the future development of AI in the TCM industry. It holds significant importance and value in promoting the intelligent development of the TCM sector and facilitating the healthy development of the entire TCM resource industry chain.

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Publication Details

Journal
PubMed
Published
2025-05-01
DOI
https://doi.org/10.19540/j.cnki.cjcmm.20250216.103
Citations
1
Primary Topic
Traditional Chinese Medicine Studies
Type
article
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[Application practice and exploration of artificial intelligence technology in entire industrial chain of traditional Chinese medicine resources].

Hui Yan, Sheng Guo, Shu-Lan Su, Hao Zhu et al.
1 citations
PubMed
Traditional Chinese Medicine Studies
article

[Application practice and exploration of artificial intelligence technology in entire industrial chain of traditional Chinese medicine resources].

Hui Yan, Sheng Guo, Shu-Lan Su, Hao Zhu, Jin-Ao Duan, Ping Xiao
article en
1 citations

Abstract

With the growing awareness of public health, the value and importance of traditional Chinese medicine(TCM) resources have become increasingly prominent. Despite the undeniable significance of TCM in medical treatment and healthcare, the protection, development, and utilization of TCM resources still face numerous challenges. Under the traditional model, the development and utilization of TCM resources heavily rely on manual labor and empirical decision-making, which not only leads to inefficiencies and high costs but also causes serious issues such as unstable drug quality and imbalances in market supply and demand. In the current era of rapid advancements in artificial intelligence(AI) and technology, AI has emerged as a new engine to address many challenges and difficulties throughout the entire TCM resource industry chain. By leveraging AI technology, intelligent management, precise production, and optimized utilization of TCM resources can be achieved, thereby improving efficiency, reducing costs, ensuring stable quality, and balancing market supply and demand. This article primarily explores the application of AI technology in the entire TCM resource industry chain from different perspectives and provides an in-depth analysis of the future development of AI in the TCM industry. It holds significant importance and value in promoting the intelligent development of the TCM sector and facilitating the healthy development of the entire TCM resource industry chain.

PubMedVol. 50(10)
Nanjing University of Chinese Medicine (CN)
Traditional Chinese Medicine Studies
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[Application practice and exploration of artificial intelligence technology in entire industrial chain of traditional Chinese medicine resources]. — Hui Yan, Sheng Guo, et al. · PubMed (2025) | TGRS Research Map | TGRS