Call for paper for the Special Issue: Artificial Intelligence in Traditional Chinese Medicine
Aims and Scope
The theme of this special issue is "Artificial Intelligence in Traditional Chinese Medicine". This Special Issue focuses on the application of artificial intelligence in the field of traditional Chinese medicine (TCM), with the aim to solve challenges in TCM intelligence and promote the development, application and clinical trails of artificial intelligence in TCM.
Prof. Junwen Wang
Institute of Basic Theory of Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine, China
Dr. Biqing Chen
Research Center of Chinese Medicine / Central Laboratory, Jiangsu Province Hospital of Chinese Medicine, China
Prof. Xie Dan
Hubei University of Chinese Medicine, China
Prof. Xuekun Song
Henan University of Chinese Medicine, China
The potential topics of this special issue include, but are not limited to:
- New mode of TCM diagnosis and treatment service under the background of Internet and artificial intelligence
- Research on the clinical experience of famous old Chinese medicine
- Development and utilization of real world TCM big data
- Knowledge graph and knowledge service in the field of traditional Chinese medicine
- Virtual simulation and inheritance of traditional Chinese Medicine
- Information technology assists TCM diagnosis and treatmen
- Research and development of information collection and processing equipment for intelligent four diagnosis of traditional Chinese medicine
- Research and development of TCM intelligent wearable health monitoring device
- Research and development of TCM intelligent treatment equipment
- Data-based intelligent platform for ancient Chinese medicine books
- Electronic prescription and intelligent remote review
- Intelligent prescription screening and drug R & D
- Informatization of quality control of Chinese herbal medicines
- Informatization of research on medicinal properties and efficacy of traditional Chinese medicines
- Intelligent manufacturing technology of traditional Chinese medicine
- Deadline of Submission: 31 Dec. 2022
- Notification of review results: 28 Feb. 2023
- Submission of revised papers: 31 March 2023
- Notification of final review results: 30 Apr. 2023
Submit Your Paper
To access the online submission site for the journal, please visit: http://oapublishing-jaims.com/jaims/about/submissions. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. Please select “Special Issue - Artificial Intelligence in Traditional Chinese Medicine” in “Section/Category” of “General Information”. All manuscripts must be in the English language.
Note that if this is the first time that you submit to the Journal of Artificial Intelligence for Medical Sciences, you need to register as a user of the Editorial Manager system first.
NOTE: Before submitting your paper, please make sure to review the journal's Author Guidelines first.
Introduction of Guest Editors
Junwen Wang, Ph.D., post doctoral, visiting scholar in the United States, professor and doctoral supervisor; Outstanding young scientific and technological talents of the Chinese Academy of traditional Chinese medicine; She is the standing member and Deputy Secretary General of the sub health committee of the world China Alliance, the review expert of the National Natural Science Foundation of China and other projects, the editorial board member of medical artificial intelligence, and the reviewer of Chinese medicine journal and other journals.
Research direction: TCM knowledge atlas, big data and intelligent auxiliary diagnosis, integrated application and innovative R & D of TCM diagnosis and treatment equipment.
Dan Xie is a professor in the college of Information Engineering, Hubei University of Chinese Medicine in Wuhan, China. She received the PhD degree from the State Key Laboratory of Software Engineering of Wuhan University in 2008. Her current research interests include medical software development, machine learning, natural language processing in electronic medical records. She was a visiting scholar at the University of Tokyo in Japan and a postdoctoral fellow in the Department of Biostatistics at the Houston Health and Medical Center of the University of Texas in the United States. She is currently a member of the IEEE and a senior member of CCF, and the associate editor of the International Journal of Artificial Intelligence and Medical Sciences. She mainly participated projects in the National Institutes of Health of the United States, the Chinese medicine modernization project of the Ministry of science and technology of China, and published more than 60 papers. She has won the second prize of scientific and technological progress in Hubei Province and the third prize of teaching achievements in Hubei Province.
Xuekun Song, associate professor. He received the PhD degree in Biomedical Engineering from Harbin Medical University, PR China, in 2017. He is currently the visiting scholar at Tsinghua University and the director of the Key Laboratory of Health Big Data and Biomedical Informatics of Henan University of Traditional Chinese Medicine. He is also CCF member and IEEE member, deputy secretary general of CMIA medical informatics theory and education professional committee. He served as the communication review expert of NSFC and the contributing editor or reviewer of some journals at home and abroad, such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, Frontiers in Bioinformatics, China Digital Medicine, and ACTA CHINESE MEDICINE.
Biqing Chen, Doctor of Philosophy received at Peking University, associate research fellow at Central Laboratory, Jiangsu Provincial Hospital of Chinese Medicine, director of the Clinical Research Branch of China Information Association of Traditional Chinese Medicine. I preside over one National Natural Science Foundation of China project, published more than ten SCI papers, including eight first author or correspondence articles. My main research interest is the genetic and molecular mechanism of human cognitive behaviors, and employing multi-omics approaches such as genomics, transcriptomics, and single cell sequencing in experimental design and data analysis.