Background: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results: China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation.Conclusions: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.

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Aims: To determine the clinical predictors of symptoms of TCM and tongue features in type 2 diabetes mellitus (T2DM) with diabetic peripheral neuropathy (DPN), in further to verify whether these parameters of TCM can be used to develop a clinical model for predicting onset of DPN among T2DM. Methods: We collect information from a standardized questionnaire. The questionnaire survey was performed on 3590 T2DM, participants were randomly divided the training group (n = 3297) and the validation group (n = 1246). Symptoms of TCM and tongue features had used to evaluate the risk to develop DPN in T2DM. The least absolute shrinkage and selection operator (LASSO) regression analysis method and logistic regression analysis had used to optimize variable selection by running 5-fold cross-validation in the training group. Multi-factor logistic regression analysis was used to establish a predictive model. The nomogram had been developed based on the relevant risk factors. A receiver operating characteristic curve (ROC), calibration plot and decision curve analysis (DCA) were used to assess the model in training group and validation group. Results: A total of eight predictors were found to be independently associated with the DNP in multivariate logistic regression analyses, namely such as advanced age of grading (OR 1.575, 95% CI 1.236–2.006, p = 0.000), smoke (OR 2.815, 95% CI 2.079–3.811, p = 0.000), insomnia (OR 0.557, 95% CI 0.408–0.761, p = 0.000), sweating (OR 0.535, 95% CI 0.362–0.791, p = 0.002), loose teeth (OR1.713, 95% CI 1.258–2.331, p = 0.001), dry skin (OR1.831, 95% CI 1.303–2.574, p = 0.000), purple tongue (OR 2.278, 95% CI 1.514–3.428, p = 0.000) and dark red tongue (OR 0.139, 95% CI 0.044–0.441, p = 0.001). The model constructed with using these eight predictors exhibited medium discriminative capabilities, with an area under the ROC of 0.727 in the training group and 0.744 in the validation group. The calibration plot is shown that the model possesses satisfactory in goodness-of-fit. Conclusions: Introducing age of grading, purple tongue and symptoms of TCM into the risk model increased its usefulness for predicting DPN risk in patients with T2DM.