RESUMEN
@#【Objective】 To explore the development and research hotspots on the application of artificial intelligence (AI) in traditional Chinese medicine (TCM) diagnosis and predict research trends in the area. 【Methods】 All articles were retrieved from China National Knowledge Infrastructure (CNKI), Wanfang Data (Wanfang), China Science and Technology Journal Database (VIP), and Web of Science Core Collection (WoSCC). All related papers published in journals from the foundation of the databases to December 31, 2022 were included. NoteExpress, Co-Occurrence(COOC), VOSviewer, and CiteSpace were used to visualize data about publication volumes, journals, authors, research institutions, and keywords as well as to analyze hotspots trending topics in the field. 【Results】 A total of 686 articles were retrieved from the databases, among which 610 papers were published in Chinese and 76 in English. In terms of the journals in which these papers were published, 238 of them were Chinese journals and 52 were English ones. The number of the papers published in journals presented a slow growth. According to the results from Chinese article analysis, WANG Yiqin from Shanghai University of Traditional Chinese Medicine published the most papers in the field. The authors of Chinese papers belonged to six long-term research teams, led by WANG Yiqin and XU Jiatuo (Shanghai University of Traditional Chinese Medicine), WEI Yuke (Guangdong University of Technology), LI Gang (Tianjin University), XI Guangcheng (Institute of Automation of the Chinese Academ of Sciences), and NIU Xin (Beijing University of Chinese Medicine), respectively. In accordance with results from English paper analysis, four authors equally publishing the most papers were YAN Haixia, HU Xiaojuan, and JIANG Tao (Shanghai University of Traditional Chinese Medicine), and WEN Chuanbiao (Chengdu University of Traditional Chinese Medicine). The authors of English papers were from two major research teams in the field of Shanghai University of Traditional Chinese Medicine. Currently, research hotspots on AI such as neural networks, data mining, machine learning, feature recognition, image processing, and expert systems, have been centered on tongue diagnosis, pulse diagnosis, and syndrome research in TCM. Additionally, it was found that research on the topic was gradually evolving from explorations of a single diagnosis method to investigations on the combination of multiple TCM diagnosis methods. 【Conclusion】 Research on AI application in TCM diagnosis is still in a slowly growing stage. As technology develops, AI has been applied to many aspects of TCM diagnosis. Therefore, how to combine the two for improving TCM diagnosis is something worthy of our brainstorming and exploring.
RESUMEN
@#Objective This study examined the research status and development process of FU Qingzhu’s Obstetrics and Gynecology (Fu Qing Zhu Nv Ke,《傅青主女科》, FQZNK) in the past 40 years with bibliometrics and visual analysis. Methods Retrieved all related literature in the research field of FQZNK from the domestic and foreign databases: China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Database, and Web of Science (WOS) core database, including Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). The search range was from January 1, 1980 to March 10, 2021. In addition, bibliometrics and CiteSpace 5.7.R2 software were used to analyze literature types, published journals, cited literature, the number of author publications, co-author networks, co-institution networks, keyword co-occurrence networks, keyword clusters, and keyword bursts. Results A total of 678 valid records were included in the final dataset. Literature types, high publication journals, highly cited literature, high-yield institutions, high-yield research teams, and high-productivity scholars in this research field were found through bibliometrics. Literature types can be divided into four categories, among which 451 are theoretical studies on academic thoughts of FQZNK, accounting for 66.5% of the included journals. The Journal of Shanxi Traditional Chinese Medicine had the largest volume of published articles (61), accounting for 9.0% of the total number of the included journals. The most cited literature was ZHOU Mingxin’s article “Using the quantitative method to discuss author’s authenticity and formula characteristics of FU Qingzhu’s Obstetrics and Gynecology”, which was cited 94 times. Hunan University of Chinese Medicine, the institution with the most publications, published 45 articles, and YOU Zhaoling, the most published author, published 33 articles. Moreover, it was found that most high-yield researchers came from high-yield institutions and that Hunan University of Chinese Medicine had the most research on FQZNK. Keyword co-occurrence analysis revealed that the keyword “FQZNK” had the highest frequency (597 times) and the highest centrality (1.00). Keyword cluster analysis used the Log-Likelihood Ratio (LLR) algorithm to form eleven important clusters: #0 treatment aiming at its root causes, #1 gynecopathy, #2 Siwu Decoction (四物汤), #3 FU Qingzhu, #4 post-partum, #5 infertility, #6 dysmenorrhea, #7 sterility, #8 coordinate the heart and kidney, #9 Danggui Buxue Decoction (当归补血汤), and #10 treatment. It was found that the prescriptions of FQZNK were studied mainly before 2000, the theoretical studies were mainly conducted before 2010, and its clinical application was mainly explored from 2010 until now. Diseases such as dysmenorrhea, morbid vaginal discharge, infertility, metrorrhagia, and polycystic ovarian syndrome (PCOS) have recently become popular topics in this field. Conclusion The current study provides more scientific, accurate, and comprehensive scientific support for further research and development of traditional Chinese medicine (TCM) in FQZNK. With this foundation, people can use burst detection to ascertain the current hotspots in research, get their development trends, and forecast future research directions. In addition, infertility, morbid vaginal discharge, flooding, and PCOS treatments based on TCM syndrome differentiation are currently popular research topics for FQZNK.