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Wang C, Dai G, Luo Y, Wen C, Tang Q. Chinese Medicine in the Era of Artificial Intelligence: Challenges and Development Prospects. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2025; 53:353-384. [PMID: 40099393 DOI: 10.1142/s0192415x25500144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Traditional Chinese medicine (TCM) has protected the health of Chinese people for thousands of years. With the rapid development of artificial intelligence (AI), various fields of TCM are facing both opportunities and challenges. This review discusses the development prospects and challenges of Chinese medicine in the AI era, emphasizing that AI, as an important tool in the process of Chinese medicine healthcare services, can assist doctors in making objective, rational and professional treatment decisions, and that AI has a strong potential for development in the field of Chinese medicine. However, the emotions, complex thoughts, and humanistic values of doctors are qualities that AI is currently unable to realize, so as the dominant player, the doctor is indispensable to the medical process. By summarizing and analyzing the current development status of AI in diagnosis, drug research, health management and education in TCM, this paper reveals the development prospects and potential risks of combining TCM with AI, and suggests that AI is an important aid for modernizing and improving the quality of TCM medical care in a coordinated manner.
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Affiliation(s)
- Chaoyu Wang
- Chengdu University of Traditional Chinese Medicine, Chengdu 611137, P. R. China
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, P. R. China
| | - Guowei Dai
- College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
- National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, P. R. China
| | - Yue Luo
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu 611137, P. R. China
| | - Chuanbiao Wen
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu 611137, P. R. China
| | - Qingfeng Tang
- Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, P. R. China
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Pan H, Fu Y, Zhang Q, Zhang J, Qin X. The decoder design and performance comparative analysis for closed-loop brain-machine interface system. Cogn Neurodyn 2024; 18:147-164. [PMID: 39170600 PMCID: PMC11333431 DOI: 10.1007/s11571-022-09919-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Brain-machine interface (BMI) can convert electroencephalography signals (EEGs) into the control instructions of external devices, and the key of control performance is the accuracy and efficiency of decoder. However, the performance of different decoders obtaining control instructions from complex and variable EEG signals is very different and irregular in the different neural information transfer model. Aiming at this problem, the off-line and on-line performance of eight decoders based on the improved single-joint information transmission (SJIT) model is compared and analyzed in this paper, which can provide a theoretical guidance for decoder design. Firstly, in order to avoid the different types of neural activities in the decoding process on the decoder performance, eight decoders based on the improved SJIT model are designed. And then the off-line decoding performance of these decoders is tested and compared. Secondly, a closed-loop BMI system which combining by the designed decoder and the random forest encoder based on the improved SJIT model is constructed. Finally, based on the constructed closed-loop BMI system, the on-line decoding performance of decoders is compared and analyzed. The results show that the LSTM-based decoder has better on-line decoding performance than others in the improved SJIT model.
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Affiliation(s)
- Hongguang Pan
- College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054 Shaanxi China
- Xi’an Key Laboratory of Electrical Equipment Condition Monit oring and Power Supply Security, Xi’an, 710054 China
- Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,on Monit oring and Power Supply Security, Chongqing, 400065 China
| | - Yunpeng Fu
- College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054 Shaanxi China
- Xi’an Key Laboratory of Electrical Equipment Condition Monit oring and Power Supply Security, Xi’an, 710054 China
| | - Qi Zhang
- AVIC Xi’an Aviation Brake Technology Cl., Ltd, Xi’an, 710061 Shaanxi China
| | - Jingyuan Zhang
- College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054 Shaanxi China
- Xi’an Key Laboratory of Electrical Equipment Condition Monit oring and Power Supply Security, Xi’an, 710054 China
| | - Xuebin Qin
- College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054 Shaanxi China
- Xi’an Key Laboratory of Electrical Equipment Condition Monit oring and Power Supply Security, Xi’an, 710054 China
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Li M, Wang L, Wu Q, Zhu J, Zhang M. Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation. Artif Intell Med 2024; 147:102739. [PMID: 38044249 DOI: 10.1016/j.artmed.2023.102739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 10/16/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (""), 322 patterns(""), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.
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Affiliation(s)
- Meiwen Li
- School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
| | - Lin Wang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
| | - Qingtao Wu
- School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
| | - Junlong Zhu
- School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
| | - Mingchuan Zhang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.
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Bao Y, Ding H, Zhang Z, Yang K, Tran Q, Sun Q, Xu T. Intelligent acupuncture: data-driven revolution of traditional Chinese medicine. ACUPUNCTURE AND HERBAL MEDICINE 2023; 3:271-284. [DOI: 10.1097/hm9.0000000000000077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2025]
Abstract
Acupuncture, a form of traditional Chinese medicine with a history of 2,000 years in China, has gained wider acceptance worldwide as a complementary therapy. Studies have examined its effectiveness in various health conditions and it is commonly used alongside conventional medical treatments. With the development of artificial intelligence (AI) technology, new possibilities for improving the efficacy and precision of acupuncture have emerged. This study explored the combination of traditional acupuncture and AI technology from three perspectives: acupuncture diagnosis, prescription, and treatment evaluation. The study aimed to provide cutting-edge direction and theoretical assistance for the development of an acupuncture robot.
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Affiliation(s)
- Yunfan Bao
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, China
| | - Haokang Ding
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhihan Zhang
- School of Life Science and Chemistry and Chemical Engineering, Jiangsu Second Normal University, Nanjing, China
| | - Kunhuan Yang
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, China
| | - Queena Tran
- College of Letters & Science, University of California, Berkeley, USA
| | - Qi Sun
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Tiancheng Xu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, China
- Zhimei Kangmin (Zhuhai) Health Technology Co., LTD, Zhuhai, China
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Gong L, Jiang J, Chen S, Qi M. A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining. Front Genet 2023; 14:1272016. [PMID: 37854059 PMCID: PMC10579813 DOI: 10.3389/fgene.2023.1272016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/07/2023] [Indexed: 10/20/2023] Open
Abstract
Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients' medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features.
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Affiliation(s)
- Lejun Gong
- Jiangsu Key Lab of Big Data Security and Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing, China
| | - Jindou Jiang
- Jiangsu Key Lab of Big Data Security and Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Shiqi Chen
- Jiangsu Key Lab of Big Data Security and Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Mingming Qi
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
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Mi BH, Zhang WZ, Xiao YH, Hong WX, Song JL, Tu JF, Jiang BY, Ye C, Shi GX. An exploration of new methods for metabolic syndrome examination by infrared thermography and knowledge mining. Sci Rep 2022; 12:6377. [PMID: 35430598 PMCID: PMC9012989 DOI: 10.1038/s41598-022-10422-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 03/15/2022] [Indexed: 11/24/2022] Open
Abstract
Metabolic syndrome (MS) is a clinical syndrome with multiple metabolic disorders. As the diagnostic criteria for MS still lacking of imaging laboratory method, this study aimed to explore the differences between healthy people and MS patients through infrared thermography (IRT). However, the observation region of the IRT image is uncertain, and the research tried to solve this problem with the help of knowledge mining technology. 43 MS participants were randomly included through a cross-sectional method, and 43 healthy participants were recruited through number matching. The IRT image of each participant was segmented into the region of interest (ROI) through the preprocessing method proposed in this research, and then the ROI features were granulated by the K-means algorithm to generate the formal background, and finally, the two formal background were separately built into a knowledge graph through the knowledge mining method based on the attribute partial order structure. The baseline data shows that there is no difference in age, gender, and height between the two groups (P > 0.05). The image preprocessing method can segment the IRT image into 18 ROI. Through the K-means method, each group of data can be separately established with a 43 × 36 formal background and generated a knowledge graph. It can be found through knowledge mining and independent-samples T test that the average temperature and maximum temperature difference between the chest and face of the two groups are statistically different (P < 0.01). IRT could reflect the difference between healthy people and MS people. The measurement regions were found by the method of knowledge mining on the premise of unknown. The method proposed in this paper may add a new imaging method for MS laboratory examinations, and at the same time, through knowledge mining, it can also expand a new idea for clinical research of IRT.
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Xu Q, Guo Q, Wang CX, Zhang S, Wen CB, Sun T, Peng W, Chen J, Li WH. Network differentiation: A computational method of pathogenesis diagnosis in traditional Chinese medicine based on systems science. Artif Intell Med 2021; 118:102134. [PMID: 34412850 DOI: 10.1016/j.artmed.2021.102134] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 04/12/2021] [Accepted: 06/28/2021] [Indexed: 11/15/2022]
Abstract
Resembling the role of disease diagnosis in Western medicine, pathogenesis (also called Bing Ji) diagnosis is one of the utmost important tasks in traditional Chinese medicine (TCM). In TCM theory, pathogenesis is a complex system composed of a group of interrelated factors, which is highly consistent with the character of systems science (SS). In this paper, we introduce a heuristic definition called pathogenesis network (PN) to represent pathogenesis in the form of the directed graph. Accordingly, a computational method of pathogenesis diagnosis, called network differentiation (ND), is proposed by integrating the holism principle in SS. ND consists of three stages. The first stage is to generate all possible diagnoses by Cartesian Product operated on specified prior knowledge corresponding to the input symptoms. The second stage is to screen the validated diagnoses by holism principle. The third stage is to pick out the clinical diagnosis by physician-computer interaction. Some theorems are stated and proved for the further optimization of ND in this paper. We conducted simulation experiments on 100 clinical cases. The experimental results show that our proposed method has an excellent capability to fit the holistic thinking in the process of physician inference.
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Affiliation(s)
- Qiang Xu
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China.
| | - Qiang Guo
- Chengdu First People's Hospital, Chengdu 610100, China
| | - Chun-Xia Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China
| | - Song Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China
| | - Chuan-Biao Wen
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China
| | - Tao Sun
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China
| | - Wei Peng
- School of pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China
| | - Jun Chen
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China.
| | - Wei-Hong Li
- School of Basic Medical Science, Chengdu University of Traditional Chinese Medicine, Chengdu 610100, China.
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Peng T, Wei C, Yu F, Xu J, Zhou Q, Shi T, Hu X. Predicting nanotoxicity by an integrated machine learning and metabolomics approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 267:115434. [PMID: 32841907 DOI: 10.1016/j.envpol.2020.115434] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.
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Affiliation(s)
- Ting Peng
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Changhong Wei
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Fubo Yu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Jing Xu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Tonglei Shi
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
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Guo S, Zhao H. Hierarchical classification with multi-path selection based on granular computing. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09899-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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