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Cao S, Wei Y, Yue Y, Wang D, Xiong A, Yang J, Zeng H. Uncovering the scientific landscape: A bibliometric and Visualized Analysis of artificial intelligence in Traditional Chinese Medicine. Heliyon 2024; 10:e37439. [PMID: 39315188 PMCID: PMC11417164 DOI: 10.1016/j.heliyon.2024.e37439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/13/2024] [Accepted: 09/03/2024] [Indexed: 09/25/2024] Open
Abstract
The emergence of artificial intelligence (AI) technology has presented new challenges and opportunities for Traditional Chinese Medicine (TCM), aiming to provide objective assessments and improve clinical effectiveness. However, there is a lack of comprehensive analyses on the research trajectory, key directions, current trends, and future perspectives in this field. This research aims to comprehensively update the progress of AI in TCM over the past 24 years, based on data from the Web of Science database covering January 1, 2000, to March 1, 2024. Using advanced analytical tools, we conducted detailed bibliometric and visual analyses. The results highlight China's predominant influence, contributing 54.35 % of the total publications and playing a key role in shaping research in this field. Significant productivity was observed at institutions such as the China Academy of Chinese Medical Sciences, Beijing University of Chinese Medicine, and Shanghai University of Traditional Chinese Medicine, with Wang Yu being the most prolific contributor. The journal Molecules contributed the most publications in this field. This study identified hepatocellular carcinoma, chemical and drug-induced liver injury, Papillon-Lefèvre disease, Parkinson's disease, and anorexia as the most significant disorders researched. This comprehensive bibliometric assessment benefits both seasoned researchers and newcomers, offering quick access to essential information and fostering the generation of innovative ideas in this field.
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Affiliation(s)
- Siyang Cao
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yihao Wei
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yaohang Yue
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Deli Wang
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Ao Xiong
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jun Yang
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Hui Zeng
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
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Li W, Ge X, Liu S, Xu L, Zhai X, Yu L. Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Front Med (Lausanne) 2024; 10:1336175. [PMID: 38274445 PMCID: PMC10808796 DOI: 10.3389/fmed.2023.1336175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners' expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.
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Affiliation(s)
- Wenyu Li
- School of Marxism, Capital Normal University, Beijing, China
| | - Xiaolei Ge
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Shuai Liu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Lili Xu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Xu Zhai
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Linyong Yu
- China Academy of Chinese Medical Sciences, Beijing, China
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