With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.

The aim of the present study is to use entropy to explore the change of pulse generated by normal pregnant women with gestational. Firstly, the subjects were divided into early (E), middle (M), and late (L) three stages according to gestational age. Then, pulse signals of the Chi position of 90 pregnant women at different gestational ages were collected. Secondly, the four entropies, namely fuzzy entropy (FuEn), approximate entropy (ApEn), sample entropy (SamEn), and permutation entropy (PerEn), were applied to the analysis of the long-term pulse changes of the pregnancy. Finally, the related information about pulse in different stages of pregnancy is given by the analysis of four kinds of entropy. Furthermore, the statistical tests are conducted for further comparison, and the descriptive statistics and the results are presented. In addition, boxplots are employed to show the distribution of four entropies of pregnancy. This work has studied the changes in pulse during pregnancy from quantitative and qualitative aspects. Our results show that entropy improves the diagnostic value of pulse analysis during pregnancy and could be applied to facilitate noninvasive diagnosis of pregnant women’s physiological signals in the future.

Objective: Pulse diagnosis holds significant importance in traditional Chinese medicine (TCM). The accurate identification of pregnancy pulse patterns is crucial in TCM diagnosis, as it provides valuable insights into a patient′s health status and enables the development of personalized treatment strategies. However, the complexity and variability of pulse characteristics across different stages of pregnancy pose a significant challenge for TCM practitioners. Methods: To address the issue, a novel one‐dimensional convolutional neural network (1DCNN) integrated with a gated recurrent unit (GRU) and an improved squeeze‐and‐excitation network (ISENet), named 1DCNN–GRU–ISENet, is proposed to identify the pregnancy pulse of three stages. Specifically, the 1DCNN is employed to construct parallel convolutional branches and series‐stacked blocks. This architecture not only functions as a residual network but also enhances the network′s depth, enabling effective extraction of pulse features. By incorporating this structure, the model is capable of capturing more intricate and subtle characteristics of the pulse within different frequency ranges, leading to improved feature representation and enhanced performance. Simultaneously, the integration of GRUs facilitates comprehensive learning of pulse temporal features. Moreover, the ISENet assigns varying weights to different feature components based on pulse trends, effectively focusing on and highlighting crucial spatial features. Results: Through rigorous evaluation with a locally collected research dataset, our network achieves accuracy rates of 90.8%, 91.6%, and 91.5% in identifying pregnancy pulse patterns for the three stages, respectively. Additionally, our model demonstrates good performance across various metrics, including average precision, sensitivity, F1 score, and AUC, with scores of 91.7%, 91.5%, 0.916, and 97.1%, respectively. Conclusion: These results underscore the effectiveness of our proposed network in accurately classifying pulse patterns and showcase its potential for practical application in assisting with auxiliary diagnosis in the context of TCM pulse evaluation.