AI-Powered Advances in TCM for Chronic Disease

TOPICAL COLLECTIONS

Guest Editors

Yiming Chen

Anhui University of Chinese Medicine

Yiming Chen is a doctoral researcher at Anhui University of Chinese Medicine. His research focuses on the systematic analysis of the complex action networks of Chinese herbal medicine and acupuncture using artificial intelligence, as well as advancing the development of an evidence-based precision diagnosis and treatment system for traditional Chinese medicine. His latest research integrates bioinformatics and virtual screening based on AI algorithms, and has discovered a novel mechanism of action of a herbal formula against inflammation. He has published over 30 papers and authored three academic monographs, including Application and Practice of Data Science in Research on Rheumatoid Diseases in TCM. He has received funding from the Young Elite Scientists Sponsorship Program of the China Association for Science and Technology. He currently serves as a youth editorial board member for journals such as Chinese Acupuncture & Moxibustion and Asia Pacific Journal of Clinical Medical Research.

Aihong Yuan

Anhui University of Chinese Medicine

Professor Yuan Aihong is the director of the Department of Acupuncture at the First Affiliated Hospital of Anhui University of Chinese Medicine. She has been awarded a Special Government Allowance by the State Council of China and is recognized as an Outstanding Talent of Clinical TCM in China. She serves as vice chair of the Gynecological Professional Committee of the China Association of Acupuncture, as well as vice chair of the Neuroregulation Professional Committee of the Anhui Provincial Association of Physicians. Since her doctoral studies, her research has focused on the neuro-endocrine regulatory mechanisms of acupuncture in treating conditions such as osteoarthritis, diabetes, and post-stroke sequelae. Leveraging AI-driven neuroimaging analytics, she has identified aberrant central nervous system mechanisms associated with chronic pain from brain magnetic resonance imaging data in patients with osteoarthritis.

Tiancheng Xu

Nanjing University of Chinese Medicine

Tiancheng Xu is a distinguished professor at Nanjing University of Chinese Medicine. He has been selected for the Young Talent Support Program of the China Association of Acupuncture-Moxibustion, the Jiangsu Provincial Young Science & Technology Talent Program, and the Young Talent Training Scheme of the Key Laboratory under the Ministry of Education. He leads five national and provincial research projects, including the Youth Program of the National Natural Science Foundation of China, and has published 150 papers in domestic and international journals and conference proceedings. He was named one of the Top 1% Highly Cited Scholars by CNKI in 2024. He pioneered the "Digital Meridian" theory and translated it into industrial applications, including intelligent acupuncture robots. He ranked among the top 36 in the 9th Lee Kuan Yew Global Business Plan Competition and became the first Chinese winner of the Prince Nasser International Youth Innovation Award.

Xiang Ding

Shanghai University of Traditional Chinese Medicine

Dr. Xiang Ding received her PhD from Anhui University of Chinese Medicine and currently works as a postdoctoral researcher focusing on TCM treatment of rheumatic autoimmune diseases. A licensed TCM physician, she studies epigenetic mechanisms, including m6A modification, in ankylosing spondylitis and rheumatoid arthritis and investigates the therapeutic effects and mechanisms of herbal compounds. She has participated in five national projects, including NSFC grants, published 12 first-author papers, and received awards for excellent presentations at international conferences. She is well-versed in bioinformatics and AI-driven research tools and specializes in multi-omics studies and prospective clinical studies in rheumatology.

Collection Overview

This Topical Collection explores the synergistic intersection of artificial intelligence (AI) and traditional Chinese medicine (TCM) in addressing chronic diseases, such as diabetes, cardiovascular disorders, degenerative diseases, and rheumatic diseases. It moves beyond simplistic “TCM digitization” to embrace AI’s capacity for deciphering TCM through multi-omics biological information, multimodal clinical data, and multidimensional imaging.

Chronic diseases are multifactorial, long-term, and poorly managed. TCM offers rich empirical wisdom, yet its tacit knowledge and complex mechanisms remain underutilized. AI provides a transformative lens to decode these complexities. This convergence is timely, as global healthcare shifts toward value-based, preventive, and patient-centered models.

This collection aims to establish a rigorous, interdisciplinary platform that bridges TCM practitioners, artificial intelligence experts, data scientists, pharmacologists, and clinicians. Ultimately, it empowers the research community to move from retrospective correlation to prospective prediction, enabling smarter, safer, and more cost-effective chronic disease management worldwide.

This collection considers, but is not limited to, the following submissions:

Multi-omics integrative analysis coupled with AI to deconvolve TCM formula synergies and elucidate their systems-level mechanisms in chronic disease.
Virtual screening, deep learning, bioinformatics, and drug design inspired by TCM natural products, with a focus on poly-targeted therapeutic agents for complex chronic conditions.
Generative AI for synthesizing TCM prescription recommendations, patient education materials, and clinical trial simulation protocols tailored to chronic disease.
AI-powered radiomics and medical image analysis (e.g., tongue, pulse, and facial diagnostics) for non-invasive, quantitative TCM phenotype characterization and progression prediction across chronic disease.
Natural language processing for mining ancient TCM texts and real-world clinical data to generate novel hypotheses.
Wearable sensors and AI-enabled longitudinal monitoring for personalized TCM lifestyle and treatment regimens.
Explainable AI frameworks and clinical decision support systems for integrating the TCM chronic disease management pathway.

Abstracts

Artificial Intelligence–Enabled Quantitative Imaging in RA: From Automated Synovitis Assessment to Prediction of Structural Damage Progression

Shuai Yuan¹,

Xiaoxiao Xu¹,

Zhannan Wang¹,

Huiqin Qu¹,

Hanfei Peng¹,

Guangyan Yang^*1

¹1.Preventive Treatment Department, Wuhan No.1 Hospital; 2.Preventive Treatment Department, Wuhan Hospital of Traditional Chinese And Western Medicine; 3.Preventive Treatment Department, Traditional Chinese and Western Medicine Hospital of Wuhan; 4.Preventive Treatment Department, Traditional Chinese and Western Medicine Hospital of Wuhan,Tongji Medical College, Huazhong University of Science and Technology

Integrated Single-cell Transcriptomics Identifies Epi_C4 and Supports a Pro-tumor Role for FAM20C in Lung Adenocarcinoma

Xiaokui Sun¹,

Hangbo Shen^*2

¹Department of Respiratory and Critical Care Medicine, Hangzhou Linping Hospital of Traditional Chinese Medicine,

²Department of Medical Oncology, Hangzhou Linping Hospital of Traditional Chinese Medicine

Data Governance for AI-Enabled TCM Patient Education: Algorithmic Visibility and the Quality of Facial Paralysis Rehabilitation Videos

Dong Li^*1

¹The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine

Twelve Shugan Lidan Granules prevent hepatolithiasis recurrence: Molecular docking and in vivo validation of Hippo signaling pathway

Wenkai Wu¹,

Gaobin Hu¹,

Tingting Lu²,

Qingsheng Yu¹,

Qi Zhang^*1,

Wei Qi^*1

¹The First Affiliated Hospital of Anhui University of Chinese Medicine,

²College of Integrated Traditional Chinese and Western Medicine, Anhui University of Traditional Chinese Medicine