Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Objective To explore Professor Wu Liqun's medication law for treating tic disorders in children.Methods Medical records of tic disorders children treated by Professor Wu Liqun from September 2016 to October 2022 were collected,and a database was established.SPSS Statistics 23 was used for frequency statistics,and Liquorice software was used for multi-scale backbone network analysis and multi-layer core network analysis to summarize the medication law.Results Totally 709 medical records were included,involving 834 prescriptions and 203 kinds of Chinese materia medica.The drug categories were mostly tonic drugs,heat-clearing drugs,and liver-pacifying and wind-relieving drugs.The properties of the drugs were mainly warming,cold,and mild,and the meridians were mostly in the lung,liver,and spleen meridians.Complex network analysis showed that the core prescription consisted of 20 kinds of Chinese materia medica,such as Angelicae Sinensis Radix,Paeoniae Radix Alba,Chuanxiong Rhizoma,Codonopsis Radix,and 30 groups of commonly used medicine pairs and commonly used medicines with different symptoms and commodities were obtained.Conclusion Professor Wu Liqun's treatment for tic disorders in children focuses on treating"liver wind",evacuating external wind to cut off the course of the disease,soothing the liver and subduing yang to extinguish internal wind,paying attention to regulating liver qi,nourishing yin and soothing the liver,nourishing blood and extinguishing wind.

Objective To study the medication law of postoperative treatment of colorectal cancer by national TCM doctor Professor Piao Bingkui.Methods Professor Piao Bingkui's electronic medical records and paper medical records of colorectal cancer postoperative patients at Guang'anmen Hospital,China Academy of Chinese Medical Sciences and Beijing Yiqingtang Chinese Medicine Clinic were collected and organized from their outpatients from January 1st,2002 to February 28th,2022.R4.2.1 was used to study the prescriptions,including high-frequency drugs,drug types,properties of Chinese materia medica,yin yang and five elements and dosage of drugs,as well as the law of multi-drug association in postoperative patients with colorectal cancer.Results Totally 642 colorectal postoperative cancer patients were included,involving 2 226 prescriptions,180 kinds of Chinese materia medica,and a total frequency of 39 988 times.The high-frequency drugs were Astragali Radix,fried Aurantii Fructus with wheat bran,Dioscoreae Rhizoma,etc.The main drugs in terms of efficacy were tonics for tonifying deficiency,disinfectants,etc.;the main properties were warm and neutral,the main tastes were sweet,pungent and bitter,and the main meridians were spleen meridians and stomach meridians.Ascending medicines were often used,and the drug were basically non-toxic.The frequency of using yang tonifying medicine was high,and the five elements were commonly used as local medicines;the dosage was mostly 10,15 and 20 g.The complex network analysis and clustering analysis of the association between multiple drugs found that Professor Piao Bingkui's basic prescription for treating colorectal cancer included Astragali Radix,fried Aurantii Fructus with wheat bran,Dioscoreae Rhizoma,salt Alpiniae Oxyphyllae Fructus,Citri Reticulatae Pericarpium,Smilacis Glabrae Rhizoma,Pseudostellariae Radix,and fried Atractylodis Macrocephalae Rhizoma with wheat bran.Conclusion In the treatment of colorectal cancer,Professor Piao Bingkui focuses on reinforcing the healthy qi,nourishing spleen and stomach function,combined with detoxication method and clearing heat,expelling phlegm and dampness,guiding stagnation and dispelling stasis methods,making syndrome differentiation as well as tonifying and benefiting qi,regulating qi movement,so as to realize the"treating middle-energizer as balance"and achieve mild level when treating colorectal cancer.

Objective:To analyze the medication rules of modern physicians on treating depression syndrome, so as to provide reference for clinical treatment of depression syndrome.Methods:We collect and sort out the modern medical records of depression syndrome in the cloud platform of ancient and modern medical records (v1.5) and the modern medical records' database of famous doctors, and search CNKI, Chongqing VIP and Wanfang databases for the modern depression medical cases from journals during database establishment time to October 1, 2018. We extracted traditional Chinese medicine prescription data in the medical records, and applied the ancient and modern medical records cloud platform (v 1.5) to standardize the acquired TCM data, and integrate data mining function for frequency statistics, cluster analysis, association analysis and complex network analysis to obtain the high frequency of drugs' attributes, classification, and common drug pairs and core prescriptions of modern doctors. Results:After screening, a total of 1 984 cases were included, 3 618 cases were diagnosed, and 3 089 pieces of data of TCM prescriptions were included. A total of 1 458 medicines were involved, and the total frequency of medication was 39 634. The high frequency medicines were: Bupleurum chinense (1 546 times), Poria cocos (1 210 times), Angelica sinensis (1 167 times), Paeonia lactiflora (1 109 times) and Tulip (1 091 times). The commonly used Chinese medicines were mainly warm, flat, bitter and bitter, mostly attributed to the four meridians of spleen, lung, liver and heart. Commonly used medicines were Radix Paeoniae Alba- Bupleurum, Radix Angelicae- Bupleurum, Radix Bupleurum- Tulip, Radix Angelicae- Paeoniae Alba, Radix Angelicae- Paeoniae Alba- Bupleurum, etc. The core prescriptions were Suanzaoren Decoction, Chaihu- Shugan San and Erchen Decoction. Conclusions:Modern physicians' treating principles for depression syndrome is soothing liver and regulating qi, invigorating spleen and nourishing heart, emphasizing qi and blood, harmonizing liver, spleen and heart, and linking TCM pathogeneses.

Pediatric cough is a heterogeneous condition in terms of symptoms and the underlying disease mechanisms. Symptom phenotypes hold complicated interactions between each other to form an intricate network structure. This study aims to investigate whether the network structure of pediatric cough symptoms is associated with the prognosis and outcome of patients. A total of 384 cases were derived from the electronic medical records of a highly experienced traditional Chinese medicine (TCM) physician. The data were divided into two groups according to the therapeutic effect, namely, an invalid group (group A with 40 cases of poor efficacy) and a valid group (group B with 344 cases of good efficacy). Several well-established analysis methods, namely, statistical test, correlation analysis, and complex network analysis, were used to analyze the data. This study reports that symptom networks of patients with pediatric cough are related to the effectiveness of treatment: a dense network of symptoms is associated with great difficulty in treatment. Interventions with the most different symptoms in the symptom network may have improved therapeutic effects.

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Case-Control Studies ; Child ; China ; Drugs, Chinese Herbal ; therapeutic use ; Female ; Humans ; Male ; Medicine, Chinese Traditional ; Middle Aged ; Propensity Score ; Sleep Initiation and Maintenance Disorders ; drug therapy ; Young Adult

Clinical cases of TCM are used as important clinical data to record the whole process of the interaction between doctors and patients in the form of text.However,in the context of big data,there is a lack of research on the use of information covered in clinical cases.Therefore,we studied the method of extracting the symptom term from the history of present illness in TCM clinic in this paper,in order to lay the foundation for the further use of clinical cases.First,twelve thousand,three hundred and sixty-seven history data of present illness were obtained by random selection and expert review.According to the different disease types,they were divided into the two groups of the experiments:4,838 data in the diabetes group,7,529 data in the spleen and stomach disease group and 12,367 data in the mixed or combined group.A glossary of symptom terms covering 22,996 words were compiled.Then,five feature templates,such as sliding window feature,prefix and suffix character and lexical features,were selected.CRFs model was adopted to carry out named entity extraction experiment.As a result,in the open test,the performance of diabetes,spleen and stomach disease and mixed group were (0.83,0.8,0.82),(0.9,0.9,0.89) and (0.88,0.87,0.87),respectively,while the results were (0.83,0.82,0.83),(0.95,0.95,0.95) and (0.93,0.92,0.92) in the ten-fold cross validation.In conclusion,the results showed that the CRFs algorithm was an excellent sequence labeling algorithm and applied to the named entity extraction task of symptom history.

How to discover the valuable knowledge from the large amount of prescription data accumulated in the long-term medical practice of traditional Chinese medicine (TCM) is one of the important contents of TCM modernization.Based on the prescription data of Xue' s clinical practice of many years,this paper explored the method of combining TCM network and prescription network to find out Xue' s common core drug combination.Based on 9,584 prescriptions in the Hospital Information System of Guang'anmen Hospital of China Academy of Chinese Medical Sciences,prescription network and drug network were constructed according to the similarity of prescription composition and drug co-occurrence relationship.Using the complex network analysis methods,such as community analysis method,to analyze the prescription and drug compatibility,the results were evaluated and analyzed by Xue and his successors.As a result,through complex network analysis,126 modular prescriptions and 4 TCM modules were obtained.One of the core components of the prescription module included Xiao Chai Hu decoction,Yin Qiao powder,and Sheng Jiang powder compound addition and subtraction.It was in consistent with the drug composition of exogenous febrile prescriptions excavated earlier.In conclusion,using the complex network methods,we can get some core drug combinations prescribed by Prof.Xue,and achieve the common compound core drug combination for treating diseases with certain vantages,laying a foundation for further inheriting and excavating Xue' s effective experience.

Optimum research on famous TCM doctors’ experienced prescriptions can optimize the composition of prescriptions which originated from experienced prescriptions, clarify the functions of indications and efficacy features of experienced prescriptions. Case report forms which are normally adapted to clinical new medicine study cannot meet the demand of observational study to optimize experienced prescriptions. This article expounded design features, structure elements and collection timing and proposed requirements for information collection through practice of case report forms in the optimum research on famous TCM doctors’ experienced prescriptions, with a purpose to realize the optimization of experienced prescriptions through observational study on famous TCM doctors’ experienced prescriptions and provide evidence for further clinical research on experienced prescriptions.

Traditional Chinese medicine (TCM) clinical symptoms, which were main evidences in syndrome differentiation and treatment, were also main data in the clinical individual diagnosis and treatment. However, the absence of high-quality symptom ontology was still obvious in TCM clinical and data using. By analyzing the research and data of clinical phenotype ontologies associated with symptoms, this paper was aimed to introduce and sort the existing ontologies, in order to discuss related problems. Furthermore, we provided ideas and expound the importance of building data network of symptoms, diseases, genes, drugs, chemicals, side effects with the data we collected and disposed around symptoms, in order to construct TCM clinical phenotype ontology. We believed that our work will help the development of individual treatment and precision medicine in TCM. It was also the key of individual big data analysis which was urgent need to promote.