A Bibliometric Analysis of CiteSpace-Based Machine Learning Research in Chinese Medicine

Abstract

Artificial intelligence (AI) is widely used in various fields, among which machine learning (ML) is the core of AI that can be rapidly updated and developed. ML has been continuously applied to the field of traditional Chinese medicine (TCM) in the past decades, and it has also attracted more and more attention. This study uses CiteSpace and Excel software to explore the development trends and research hotspots of TCM combined with ML. The Web of Science core database was searched using ML algorithms and TCM. The annual publication volume, country (region), institution, author, journal, cited literature, and keywords were analyzed. The results of the study showed that China contributed the most publications, with 95% of the literature originating from China, followed by the United States. The most prolific institution and authors belonged to the Shanghai University of Traditional Chinese Medicine. Evidence Based Complementary and Alternative Medicine was the most focused publication in this research area. The burst detection by cited literature and keywords shows that convolutional neural networks and tongue images as diagnostic methods are the current research hotspots in this field.