Advances in the Application of Artificial Intelligence in Chinese Medicine Diagnosis

Abstract

Objective: To study the current situation of the application of artificial intelligence in Chinese medicine diagnosis. Methods: In the past ten years, the Chinese databases China Knowledge Network, Wanfang database, and English databases Pub Med, web of science, Science Direct, and Google scholars were used to study the application of artificial intelligence in Chinese medicine diagnosis with the theme words or keywords artificial intelligence, machine learning, deep learning, Chinese medicine diagnosis, Chinese medicine diagnosis, and Chinese medicine diagnosis respectively. Machine learning, deep learning, TCM diagnosis, four diagnoses. Intelligent diagnosis, intelligent Chinese medicine, and so on, to filter out the literature related to intelligent Chinese medicine diagnosis, and categorize them by looking, intelligent Chinese medicine diagnosis by smelling, by asking, by cutting, and so on, and conduct more literature related to intelligent diagnosis of TCM was categorized into the intelligent diagnosis of TCM viewing, intelligent diagnosis of TCM smelling, intelligent diagnosis of TCM questioning and intelligent diagnosis of TCM cutting, and analyzed in depth. Results: The Chinese and English literature on the intelligent research of the four diagnoses of TCM in the past ten years was summarized, and the research hotspots in this field were analyzed in-depth on this basis. Conclusion: Artificial intelligence technology is widely used in TCM clinical diagnosis, and the application of artificial intelligence technology makes TCM diagnosis technology more accurate and can effectively help modernize and standardize TCM research.

The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review

“…In this way, if a word is repeated frequently in one text (let's say it's repeated 10 times) and not at all in the other (0), it will count as 10*0=0 times in the new vector. This approach provides a measure of similarity [97]. If we sum up all the coordinates of this new vector, we obtain an initial measure of the similarity between the two phrases [98].…”