Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology
Traditional Chinese medicine (TCM), consisting of a complete TCM diagnosis and treatment system, is a valuable treasure in the long river of Chinese clinical history. However, the subjective diagnosis, ambiguous mechanisms, and complex formulas make it slightly lag behind the development of modern medicine. With the emergence of novel technologies such as artificial intelligence (AI) and nanotechnology, TCM modernization has regained its promise of hope. In this review, we provide an overview of applications of AI and nanotechnology to assist TCM modernization. Firstly, we summarize the auxiliary TCM diagnosis approaches based on machine learning and deep learning, which facilitate "four diagnostic methods" (inspection, auscultation-olfaction, inquiry, and pulse palpation) with standard and quantifiable data collection, and objective syndrome differentiation and diagnostic decisions. Secondly, a comprehensive overview of the nanotechnology used to enhance the therapeutic effects of TCM is provided, including optimizing TCM formulas and enhancing active targeting. Finally, we summarize the current challenges, clinical translation, and future perspectives of AI, TCM diagnosis, and nanotechnology. Our review and insights aim to provide valuable guidance for the continued advancement of TCM modernization.
Authors
- Mao Chen (ORCID: https://orcid.org/0000-0002-5504-3775)
- Wenqi Yu (ORCID: https://orcid.org/0000-0001-8097-8081)
- Xue Xu (ORCID: https://orcid.org/0000-0001-6045-3712)
- Hongcai Shang
- Xueqi Tan
- Fan Sun
- Xi Wei
- Hua Yan
Institutions
- Beijing University of Chinese Medicine (CN)
- Wuhan University of Technology (CN)
- Wuhan University (CN)
- Wuhan Asia Heart Hospital (CN)
- Wuhan University of Science and Technology (CN)
Publication Details
- Journal
- MedComm
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/mco2.70596
- Primary Topic
- Traditional Chinese Medicine Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China