A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation

• Published in: Computers in biology and medicine Vol. 170; p. 108074
• Main Authors: Tian, Dingcheng, Chen, Weihao, Xu, Dechao, Xu, Lisheng, Xu, Gang, Guo, Yaochen, Yao, Yudong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Alternative medicine; Artificial Intelligence; Auscultation; Deep learning; Diagnosis; Humans; Inspection; Intelligent diagnosis; Internal Medicine; Learning algorithms; Machine Learning; Medical diagnosis; Medicine, Chinese Traditional - methods; Olfaction; Other; Palpation; Smell; Traditional Chinese medicine; Deep learning; Traditional Chinese medicine; Machine learning; Intelligent diagnosis
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108074

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient’s illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors’ experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis. •We have summarized the current state of research on ML methods in TCM diagnosis.•Discussed the key factors in intelligent TCM diagnosis based on ML.•Reviewed ML applications in the four TCM diagnosis methods.•Discussed the challenges and directions in applying ML in TCM diagnosis.