Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study.

Autor:	Tian Z; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Wang D; College of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan, China., Sun X; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Fan Y; Graduate School, Nanjing University of Chinese Medicine, Nanjing, China.; Institute of Hypertension, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China., Guan Y; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Zhang N; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Zhou M; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Zeng X; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Yuan Y; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Bu H; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China., Wang H; School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Jazyk:	angličtina
Zdroj:	Annals of translational medicine [Ann Transl Med] 2023 Feb 15; Vol. 11 (3), pp. 145. Date of Electronic Publication: 2023 Feb 02.
DOI:	10.21037/atm-22-6431
Abstrakt:	Background: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results: China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation. Conclusions: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models. Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6431/coif). The authors have no conflicts of interest to declare. (2023 Annals of Translational Medicine. All rights reserved.)
Databáze:	MEDLINE
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