人工智能在中医诊疗领域的研究综述

doi:10.3778/j.issn.1002-8331.2312-0400

摘要/Abstract

摘要： 中医诊疗领域正逐步迈向标准化、客观化、现代化与智能化。在此过程中，人工智能的融入极大地推动了中医诊疗、科学研究及中医传承的发展。从人工智能在中医领域的研究现状出发，梳理了从最初的专家系统和规则引擎，到逐渐成熟的传统机器学习算法，再到如今引领潮流的深度学习三个阶段，人工智能在中医领域的应用发展情况。总结了近年来涌现出的中医知识管理工具和大型模型，这些工具和模型为中医诊疗的智能化提供了坚实的支持。最后针对现阶段人工智能在中医领域中存在的数据公平性、多模态数据理解、模型鲁棒性、个性化医疗及可解释性等多重挑战进行分析。为应对这些挑战，需要持续探索并提出可能的解决方案，以推动中医诊疗智能化的深入发展，更好地满足人民健康需求。

关键词: 中医诊疗, 人工智能, 中医知识库, 中医大模型

Abstract: The field of traditional Chinese medicine (TCM) diagnosis and treatment is gradually moving towards standardization, objectification, modernization, and intelligence. In this process, the integration of artificial intelligence (AI) has greatly propelled the advancement of TCM diagnosis and treatment, scientific research, and TCM inheritance. The review starts from the current research status of AI in TCM, combs through the application and development of AI in TCM in three stages from expert system and rule engines, traditional machine learning algorithm to deep learning, and then summarizes the knowledge management tools and large language models of TCM in recent years. Finally, this paper analyzes the multiple challenges of data fairness, multimodal data understanding, model robustness, personalized medicine, and interpretability that exist at this stage of AI in TCM. To address these challenges, it is necessary to continuously explore and propose possible solutions to promote the in-depth development of intelligent TCM diagnosis and treatment, thus better meeting the health needs of people.

Key words: traditional Chinese medicine diagnosis and treatment, artificial intelligence, traditional Chinese medicine knowledge base, traditional Chinese medicine large language model

苏尤丽, 胡宣宇, 马世杰, 张雨宁, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 人工智能在中医诊疗领域的研究综述[J]. 计算机工程与应用, 2024, 60(16): 1-18.

SU Youli, HU Xuanyu, MA Shijie, ZHANG Yuning, Abudukelimu Abulizi, Halidanmu Abudukelimu. Review of Research on Artificial Intelligence in Traditional Chinese Medicine Diagnosis and Treatment[J]. Computer Engineering and Applications, 2024, 60(16): 1-18.

参考文献

[1] TU Y. The discovery of artemisinin (Qinghaosu) and gifts from Chinese medicine[J]. Nature Medicine, 2011, 17(10): 1217-1220.
[2] 国务院办公厅. “十四五”中医药发展规划[R]. 2022.
General Office of the State Council. The “fourteenth five-year plan” for the development of traditional Chinese medicine[R]. 2022.
[3] FENG C, SHAO Y, WANG B, et al. Development and application of artificial intelligence in auxiliary TCM diagnosis[J]. Evidence-Based Complementary and Alternative Medicine, 2021. DOI:10.1155/2021/6656053.
[4] LI N, YU J, MAO X, et al. The research and development thinking on the status of artificial intelligence in traditional Chinese medicine[J]. Evidence-Based Complementary and Alternative Medicine, 2022. DOI:10.1155/2022/7644524.
[5] 施俊, 汪琳琳, 王珊珊, 等. 深度学习在医学影像中的应用综述[J]. 中国图象图形学报, 2020, 25(10): 1953-1981.
SHI J, WANG L L, WANG S S, et al. Applications of deep learning in medical imaging: a survey[J]. Journal of Image and Graphics, 2020, 25(10): 1953-1981.
[6] 张玉洁, 白如江, 许海云, 等. 融合多自然语言处理任务的中医辅助诊疗方案研究——以糖尿病为例[J]. 数据分析与知识发现, 2022, 6(1): 122-133.
ZHANG Y J, BAI R J, XU H Y, et al. Assisted TCM diagnosis and treatment for diabetes with multi-NLP tasks[J]. Data Analysis and Knowledge Discovery, 2022, 6(1): 122-133.
[7] 关菀, 马志龙, 徐春, 等. 深度学习技术在中医领域中的应用[J]. 中国卫生信息管理杂志, 2022, 19(2): 281-285.
GUAN W, MA Z L, XU C, et al. Application of deep learning in traditional Chinese medicine (TCM): a literature review[J]. Chinese Journal of Health Information Management, 2022, 19(2): 281-285.
[8] ZHANG J Y, PENG Q H, YAN J F. Research hotspots and evolutionary trends of artificial intelligence application in the field of traditional Chinese medicine diagnosis from the perspective of bibliometrics[J]. Digital Chinese Medicine, 2023, 6(2): 136-150.
[9] 李红岩, 李灿, 郎许锋, 等. 中医四诊智能化现状及关键技术探讨[J]. 中医杂志, 2022, 63(12): 1101-1108.
LI H Y, LI C, LANG X F et al. The current status and key techniques of intelligentization of the four examinations in traditional Chinese medicine[J]. Chinese Medicine, 2022, 63(12): 1101-1108.
[10] TIAN D, CHEN W, XU D, et al. A review of traditional Chinese medicine diagnosis using machine learning: inspection, auscultation-olfaction, inquiry, and palpation[J]. Computers in Biology and Medicine, 2024, 170: 108074.
[11] 吴欣, 徐红, 林卓胜, 等. 深度学习在舌象分类中的研究综述[J]. 计算机科学与探索, 2023, 17(2): 303-323.
WU X, XU H, LIN Z S, et al. A research review on deep learning in tongue image classification[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(2): 303-323.
[12] 陈瑞, 刘璐, 王忆勤, 等. 人工神经网络在中医舌面诊中的研究进展[J]. 中华中医药杂志, 2020, 35(4): 1924-1926.
CHEN R, LIU L, WANG Y Q, et al. Research progress of artificial neural network in tongue and face diagnosis in Chinese medicine[J]. Chinese Journal of Traditional Chinese Medicine, 2020, 35(4): 1924-1926.
[13] 陈勇, 陈增潭, 谢敏, 等. 关幼波治疗肝炎电子计算机第二诊疗程序临床应用总结[J]. 辽宁中医杂志, 1985(2): 18-19.
CHEN Y, CHEN Z T, XIE M, et al. Summary of clinical application of the second diagnosis and treatment procedure of electronic computer in the treatment of hepatitis by Guan Youbo[J]. Liaoning Journal of Traditional Chinese Medicine, 1985(2): 18-19.
[14] 杨亚利. 面向中医内科常见病的专家系统设计与实现研究[D]. 秦皇岛: 燕山大学, 2016.
YANG Y L. Study of expert system design and realization for common disease of Chinese internal medicine[D]. Qinhuangdao: Yanshan University, 2016.
[15] 刘健, 蒋卫民, 沈宫建. 面向大数据的高血压中医专家诊疗系统构建及应用[J]. 中国中医药图书情报杂志, 2019, 43(5): 5-9.
LIU J, JIANG W M, SHEN G J. Construction and application of diagnosis and treatment system of hypertension TCM experts for big data[J]. China Journal of Library and Information Science of Traditional Chinese Medicine, 2019, 43(5): 5-9.
[16] 刘健, 蒋卫民, 沈宫建. 基于数据分析的高血压中医智能诊疗专家系统设计[J]. 北京中医药, 2019, 38(9): 904-906.
LIU J, JIANG W M, SHEN G J. Design of expert system for intelligent diagnosis and treatment of hypertension in traditional Chinese medicine based on data analysis[J]. Beijing Journal of Traditional Chinese Medicine, 2019, 38(9): 904-906.
[17] 张宇. 基于Java规则引擎的中医专家系统[J]. 郑州轻工业学院学报 (自然科学版), 2008, 23(3): 20-22.
ZHANG Y. Traditional Chinese medicine expert system based on Java rule engine[J]. Journal of Zhengzhou Institute of Light Industry (Natural Science Edition), 2008, 23(3): 20-22.
[18] 杨健, 马小兰, 杨邓奇. 基于案例推理的中医诊疗专家系统[J]. 计算机工程, 2008, 34(21): 178-180.
YANG J, MA X L, YANG D Q. Diagnosis and treatment expert system of Chinese traditional medicine using case-based reasoning[J]. Computer Engineering, 2008, 34(21): 178-180.
[19] 徐佳君, 罗志明, 赵文, 等. 基于人工智能算法的中医状态辨识规则[J]. 中医杂志, 2020, 61(3): 204-208.
XU J J, LUO Z M, ZHAO W, et al. Rules of traditional Chinese medicine state identification based on artificial intelligence algorithm[J]. Journal of Traditional Chinese Medicine, 2020, 61(3): 204-208.
[20] 徐佳君, 赵文, 雷黄伟, 等. 基于状态辨识的人工智能组方模型设计[J]. 中国中医基础医学杂志, 2021, 27(12): 1906-1908.
XU J J, ZHAO W, LEI H W, et al. Design of artificial intelligence formulation model based on state recognition[J]. Chinese Journal of Basic Medicine of Traditional Chinese Medicine, 2021, 27(12): 1906-1908.
[21] LI M, WEN G, ZHONG J, et al. Personalized intelligent syndrome differentiation guided by TCM consultation philosophy[J]. Journal of Healthcare Engineering, 2022: 6553017.
[22] 王芬, 刘铜华, 丁雷, 等. 基于概率图的中医动态交互问诊与智能辨证的数学模型的探索[J]. 世界科学技术-中医药现代化, 2023, 25(10): 3370-3376.
WANG F, LIU T H, DING L, et al. Exploration of mathematical model of dynamic interactive consultation and intelligent syndrome differentiation of traditional Chinese medicine based on probability graph[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2023, 25(10): 3370-3376.
[23] 白逸晨, 史利卿, 孙美玲, 等. 基于本体和语义网的中医专家问诊信息模型构建与呼吸科应用示范研究[J]. 中华中医药学刊, 2024, 42(2): 17-22.
BAI Y C, SHI L Q, SUN M L, et al. Construction of information model of TCM expert consultation based on ontology and semantic network and demonstration study of respiratory application[J]. Chinese Journal of Traditional Chinese Medicine, 2024, 42(2): 17-22.
[24] ZRUBKA Z, KERTESZ G, GULACSI L, et al. The reporting quality of machine learning studies on pediatric diabetes mellitus: systematic review[J]. Journal of Medical Internet Research, 2024, 26: e47430.
[25] 李壮, 侯堃. 316例男性急性痛风性关节炎中医证候分类研究[J]. 中医杂志, 2019, 60(1): 47-50.
LI Z, HOU K. Classification of traditional Chinese medicine syndromes of 316 male acute gout arthritis patients[J]. Chinese Medicine, 2019, 60(1): 47-50.
[26] 曹倩倩, 何庆勇, 王永霞, 等. 基于因子和聚类分析的冠心病合并血脂异常中医证候分类及诊断[J]. 世界科学技术-中医药现代化, 2021, 23(9): 3081-3085.
CAO Q Q, HE Q Y, WANG Y X, et al. TCM syndrome classification and diagnosis of coronary heart disease with dyslipidemia based on factor and cluster analysis[J]. World Science and Technology?Modernization of Traditional Chinese Medicine, 2021, 23(9): 3081-3085.
[27] 陈明, 万廷信, 戴恩来, 等. 基于因子分析与聚类分析的IgA肾病中医证候分类研究[J]. 北京中医药大学学报, 2014, 37(2): 135-140.
CHEN M, WAN T X, DAI E L, et al. Classification of TCM syndromes of IgA nephropathy: factor analysis and clustering analysis[J]. Journal of Beijing University of Traditional Chinese Medicine, 2014, 37(2): 135-140.
[28] 王益德, 田宗祥, 李争, 等. 基于微观辨证理论的结核相关阻塞性肺疾病临床特征及中医证型判别分析[J]. 世界科学技术-中医药现代化, 2023, 25(1): 372-379.
WANG Y D, TIAN Z X, LI Z, et al. Clinical features and discriminant analysis of TCM syndromes of tuberculosis associated obstructive pulmonary disease based on the micro syndrome differentiation theory[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2023, 25(1): 372-379.
[29] 徐玮斐, 刘国萍, 王忆勤. 多元统计学方法在中医证候分类识别中的应用及展望[J]. 中国中医药信息杂志, 2015, 22(8): 124-128.
XU W F, LIU G P, WANG Y Q. Application and prospect of multivariate statistical method in classification and recognition of traditional Chinese medicine[J]. Journal of Chinese Traditional Medicine, 2015, 22(8): 124-128.
[30] 颜建军, 胡宗杰, 刘国萍, 等. 基于极值随机森林的慢性胃炎中医证候分类[J]. 华东理工大学学报(自然科学版), 2017, 43(5): 698-703.
YAN J J, HU Z J, LIU G P, et al. Classification of Chinese medicine symptoms of chronic gastritis based on polar random forest[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2017, 43(5): 698-703.
[31] 顾天宇, 严壮志, 蒋皆恢. 基于支持向量机的中风病中医证候分类[J]. 中医药信息, 2021, 38(9): 1-3.
GU T Y, YAN Z Z, JIANG J H. Classification of TCM syndrome patterns of stroke based on SVM[J]. Chinese Medicine Information, 2021, 38(9): 1-3.
[32] 李人亮, 张平, 胡子毅, 等. 基于CART决策树与BP神经网络算法探析蒋小敏教授治疗骨痹的辨证规律[J]. 世界科学技术-中医药现代化, 2023, 25(1): 401-412.
LI R L, ZHANG P, HU Z Y et al. Exploring the identification pattern of professor Jiang Xiaomin’s treatment of bone paralysis based on CART decision tree and BP neural network algorithm[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2023, 25(1): 401-412.
[33] SHI Y, GUO D, CHUN Y, et al. A lung cancer risk warning model based on tongue images[J]. Frontiers in Physiology, 2023, 14: 1154294.
[34] WANG R R, CHEN J L, DUAN S J, et al. Noninvasive diagnostic technique for nonalcoholic fatty liver disease based on features of tongue images[J]. Chinese Journal of Integrative Medicine, 2024, 30(3): 203-212.
[35] FAN S Y, CHEN B, ZHANG X R, et al. Machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease[J]. European Journal of Integrative Medicine, 2021, 43: 121288.
[36] WANG W Y, ZENG W W, HE S L, et al. A new model for predicting the occurrence of polycystic ovary syndrome: based on data of tongue and pulse[J]. Digital Health, 2023, 9: 708742.
[37] SHI Y L, YAO X H, XU J T, et al. A new approach of fatigue classification based on data of tongue and pulse with machine learning[J]. Frontiers in Physiology, 2022, 12: 708742.
[38] 宋诗博, 安二匣, 樊西倩, 等. 中医四诊合参客观化研究思考[J]. 中华中医药杂志, 2021, 36(11): 6560-6562.
SONG S B, AN E Z, FAN X Q, et al. Thinking on the objective research of the four-diagnosis of traditional Chinese medicine[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2021, 36(11): 6560-6562.
[39] XUE Y, LI X, WU P, et al. Automated tongue segmentation in Chinese medicine based on deep learning[C]//Proceedings of the 25th International Conference on Neural Information Processing, Siem Reap, 2018: 542-553.
[40] GHOLAMI E, TABBAKH SRK, KHEIRABADIH M. Proposing method to increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM[J]. arXiv:2011.09962, 2020.
[41] CHEN P, MEN S, LIN H, et al. Detection of local lesions in tongue recognition based on improved Faster R-CNN[C]//Proceedings of the 2021 6th International Conference on Computational Intelligence and Applications, Xiamen, Jun 11-13, 2021. Piscataway: IEEE, 2021: 165-168.
[42] LI J, CHEN Q, HU X, et al. Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques[J]. International Journal of Medical Informatics, 2021, 149: 104429.
[43] LU P H, CHIANG C C, YU W H, et al. Machine learning-based technique for the severity classification of sublingual varices according to traditional Chinese medicine[J]. Computational and Mathematical Methods in Medicine, 2022. DOI: 10.1155/2022/3545712.
[44] TANG Y, SUN Y, CHIANG J Y, et al. Research on multiple-instance learning for tongue coating classification[J]. IEEE Access, 2021, 9: 66361-66370.
[45] 李渊彤, 罗裕升, 朱珍民. 基于深度学习的舌象特征分析[J]. 计算机科学, 2020, 47(11): 148-158.
LI Y T, LUO Y S, ZHU Z M. Tongue image analysis in traditional Chinese medicine based on deep learning[J]. Computer Science, 2020, 47(11): 148-158.
[46] YUAN Z, SHAO P, LI J, et al. YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection[J]. Frontiers in Neurorobotics, 2024: 1355857.
[47] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[48] 宋超, 王斌, 许家佗. 基于深度迁移学习的舌象特征分类方法研究[J]. 计算机工程与科学, 2021, 43(8): 1488-1496.
SONG C, WANG B, XU J T. Research on tongue feature classification method based on deep migration learning[J]. Computer Engineering and Science, 2021, 43(8): 1488-1496.
[49] WANG X, LIU J, WU C, et al. Artificial intelligence in tongue diagnosis: using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark[J]. Computational and Structural Biotechnology Journal, 2020, 18: 973-980.
[50] 王一丁, 孙常浩, 崔家礼, 等. 基于深度学习的舌裂分割算法研究[J]. 世界科学技术-中医药现代化, 2021, 23(9): 3065-3073.
WANG Y D, SUN C H, CUI J L, et al. Research on tongue cleft segmentation algorithm based on deep learning[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2021, 23(9): 3065-3073.
[51] TANIA A, HANIF M A, MRAN M K, et al. Improved transfer-learning-based facial recognition framework to detect autistic children at an early stage[J]. Brain sciences, 2021, 11(6): 734-734.
[52] 周孟齐. 基于面部图像的早期脏腑癌风险分类研究[D]. 北京: 北京工业大学, 2022.
ZHOU M Q. Research on early visceral cancer risk classification based on facial images[D]. Beijing: Beijing Institute of Technology, 2022.
[53] WANG L, ZHANG W, HE X, et al. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018: 2447-2456.
[54] SUN X, BEE YM, LAM SW, et al. Effective treatment recommendations for type 2 diabetes management using reinforcement learning: treatment recommendation model development and validation[J]. Journal of Medical Internet Research, 2021, 23(7): e27858.
[55] XIE Y, HU L, CHEN X, et al. Auxiliary diagnosis based on the knowledge graph of TCM syndrome[J]. Computers, Materials & Continua, 2020, 65(1): 481-494.
[56] 杨涛, 王欣宇, 朱垚, 等. 大语言模型驱动的中医智能诊疗研究思路与方法[J]. 南京中医药大学学报, 2023, 39(10): 967-971.
YANG T, WANG X Y, ZHU Y, et al. Research ideas and methods of intelligent diagnosis and treatment of traditional Chinese medicine driven by large language model[J]. Journal of Nanjing University of Traditional Chinese Medicine, 2023, 39(10): 967-971.
[57] 赵紫娟, 强彦, 赵涓涓, 等. 图像智能处理方法在中医中的应用与挑战[J]. 太原理工大学学报, 2022, 53(3): 405-419.
ZHAO Z J, QIANG Y, ZHAO J J, et al. Application and challenges of image intelligent processing method in traditional Chinese medicine[J]. Journal of Taiyuan University of Technology, 2022, 53(3): 405-419.
[58] ZHOU C G, FAN H Y, ZHAO W, et al. Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation[J]. Concurrency and Computation: Practice and Experience, 2020, 32(22): e5844.
[59] IAN J G, JEAN P A, MEHDI M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 2672-2680.
[60] CHANG J X, HU F, XU H X, et al. data augmentation of wrist pulse signal for traditional Chinese medicine using Wasserstein GAN[C]//Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences, 2021: 426-430.
[61] RONG C, LI X, SUN X. Chinese medicine prescription recommendation using generative adversarial network[J]. IEEE Access, 2022, 10: 12219-12228.
[62] 沈苏泽. 基于深度学习的疾病辅助诊断研究[D]. 南京: 东南大学, 2022.
SHEN S Z. A disease auxiliary diagnosis method based on machine learning[D]. Nanjing: Southeast University, 2022.
[63] YAN J J, CAI X L, CHEN S Y, et al. Ensemble learning-based pulse signal recognition: classification model development study[J]. JMIR Medical Informatics, 2021, 9(10): e28039.
[64] 张明琪, 邓鑫. 基于集成学习后融合神经网络算法构建肝硬化代偿期中医智能辨证模型的探讨[J]. 广州中医药大学学报, 2023, 40(10): 2650-2660.
ZHANG M Q, DENG X. Exploration of constructing an intelligent identification model of liver cirrhosis in the compensatory phase of Chinese medicine based on integrated learning followed by fusion neural network algorithm[J]. Journal of Guangzhou University of Traditional Chinese Medicine, 2023, 40(10): 2650-2660.
[65] LIU Q P, ZHANG L L, REN G, et al. Research on named entity recognition of traditional Chinese medicine chest discomfort cases incorporating domain vocabulary features[J]. Computers in Biology and Medicine, 2023, 166: 107466.
[66] 卓力, 张雷, 贾童瑶, 等. 基于双阶段元学习的小样本中医舌色域自适应分类方法[J]. 电子与信息学报, 2024, 46(3): 986-994.
ZHUO L, ZHANG L, JIA T Y, et al. Adaptive classification method of color gamut of small sample Chinese medicine tongue based on two-stage meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994.
[67] WEN G, MA J, HU Y, et al. Grouping attributes zero-shot learning for tongue constitution recognition[J]. Artificial Intelligence in Medicine, 2020, 109: 101951.
[68] ZHAO Z J, SONG K, REN X, et al. Attention matching network for few-shot learning in the syndrome differentiation of cerebral stroke[J]. International Journal of Machine Learning and Cybernetics, 2023, 14(3): 911-927.
[69] 殷泽成. 基于对比学习和图网络的中医药处方生成研究[D]. 广州: 广州大学, 2023.
YIN Z C. Research on prescription generation of traditional Chinese medicine based on contrastive learning and graph network[D]. Guangzhou: Guangzhou University, 2023.
[70] DIAO L, YANG W, ZHU P, et al. The research of clinical temporal knowledge graph based on deep learning[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41(3): 4265-4274.
[71] WU Y, ZHANG F, YANG K, et al. SymMap: an integrative database of traditional Chinese medicine enhanced by symptom map[J]. Nucleic Acids Research, 2019, 47(D1): D1110-D1117.
[72] 牟梓君, 何丽云, 周雪忠, 等. 中医知识库的应用需求与构建方法分析[J]. 中国数字医学, 2021, 16(1): 35-39.
MOU Z J, HE L Y, ZHOU X Z, et al. Analysis on the application requirements and construction methods of traditional Chinese medicine knowledge base[J]. China Digital Medicine, 2021, 16(1): 35-39.
[73] 钟俐芹, 辛国江, 彭清华, 等. 中医舌诊染苔图像数据集[J]. 中国科学数据(中英文网络版), 2023, 8(3): 444-453.
ZHONG L Q, XIN G J, PENG Q H, et al. A dataset of stained tongue fur images of TCM[J]. Chinese Journal of Scientific Data (Chinese and English Online Edition), 2023, 8(3): 444-453.
[74] WU C Y, CHEN J X, CHEN Y, et al. Pulse rate estimation based on facial videos: an evaluation and optimization of the classical methods using both self-constructed and public datasets[J]. Traditional Medicine Research, 2024, 9(1): 14-22.
[75] CHEN C Y C. TCM Database@ Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico[J]. PLoS One, 2011, 6(1): e15939.
[76] XUE R, FANG Z, ZHANG M, et al. TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis[J]. Nucleic Acids Research, 2012, 41(D1): D1089-D1095.
[77] ZHANG R, YU S, BAI H, et al. TCM-Mesh: the database and analytical system for network pharmacology analysis for TCM preparations[J]. Scientific Reports, 2017, 7(1): 2821.
[78] HUANG L, XIE D, YU Y, et al. TCMID 2.0: a comprehensive resource for TCM[J]. Nucleic Acids Research, 2018, 46(D1): D1117-D1120.
[79] LONG LI B, MA C, ZHAO X, et al. YaTCM: yet another traditional Chinese medicine database for drug discovery[J]. Computational and Structural Biotechnology Journal, 2018, 16: 600-610.
[80] XU H Y, ZHANG Y Q, LIU Z M, et al. ETCM: an encyclopaedia of traditional Chinese medicine[J]. Nucleic Acids Research, 2019, 47(D1): D976-D982.
[81] ZHANG L X, DONG J, WEI H, et al. TCMSID: a simplified integrated database for drug discovery from traditional Chinese medicine[J]. Journal of Cheminformatics, 2022, 14: 89.
[82] ZHENG Z, LIU Y, ZHANG Y, et al. TCMKG: a deep learning based traditional Chinese medicine knowledge graph platform[C]//Proceedings of the 2020 IEEE International Conference on Knowledge Graph, 2020: 560-564.
[83] LIU Z H, CAI C P, DU J W, et al. TCMIO: a comprehensive database of traditional Chinese medicine on immuno-oncology[J]. Frontiers in Pharmacology, 2020, 11: 439.
[84] ZHAO Y, WANG C, BAO M. Study on design of acupoint information database for Mongolian medicine[C]//Proceedings of the 2nd International Conference on Computing and Data Science, 2021: 1-4.
[85] FANG S S, DONG L, LIU L, et al. HERB: a high-throughput experiment-and reference-guided database of traditional Chinese medicine[J]. Nucleic Acids Research, 2021, 49(D1): D1197-D1206.
[86] YAN D Y, ZHENG G H, WANG C C, et al. HIT 2.0: an enhanced platform for herbal ingredients’ targets[J]. Nucleic Acids Research, 2022, 50(D1): D1238-D1243.
[87] LI X, REN J, ZHANG W, et al. LTM-TCM: a comprehensive database for the linking of traditional Chinese medicine with modern medicine at molecular and phenotypic levels[J]. Pharmacological Research, 2022, 178: 106185.
[88] LI P, ZHANG H, ZHANG W, et al. TMNP: a transcriptome-based multi-scale network pharmacology platform for herbal medicine[J]. Briefings in Bioinformatics, 2022, 23(1): bbab542.
[89] YANG, P, LANG J, LI H, et al. TCM‐Suite: a comprehensive and holistic platform for traditional Chinese medicine component identification and network pharmacology analysis[J]. iMeta, 2022, 1(4): e47.
[90] LV Q, CHEN G, HE H, et al. TCMBank-the largest TCM database provides deep learning-based Chinese -Western medicine exclusion prediction[J]. Signal Transduction and Targeted Therapy, 2023, 8(1): 127.
[91] TIAN S, ZHANG J, YUAN S, et al. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM[J]. Briefings in Bioinformatics, 2023, 24(2): bbad027.
[92] LI D, QU J, TIAN Z, et al. Knowledge-based recurrent neural network for TCM cerebral palsy diagnosis[J]. Evidence-Based Complementary and Alternative Medicine, 2022. DOI: 10.1155/2022/7708376.
[93] 杨涛, 漆隽之, 胡孔法, 等. 知识驱动的中医智能诊疗研究思路与方法[J/OL]. 中华中医药学刊 [2023-12-11]. http://kns.cnki.net/kcms/detail/21.1546.R.20231130.1320.002.html.
YANG T, QI J Z, HU K F, et al. Knowledge-driven research ideas and methods for intelligent diagnosis and treatment in Chinese medicine[J/OL]. Chinese Journal of Traditional Chinese Medicine [2023-12-11]. http://kns.cnki.net/kcms/detail/21.1546.R.20231130.1320.002.html.
[94] 杨凤, 侯鉴宸, 邢琛林, 等. 基于知识元标引与知识图谱的中医古籍知识表示、获取与发现研究[J]. 中国中医基础医学杂志, 2023, 29(6): 954-959.
YANG F, HOU J C, XING C L, et al. Research on knowledge representation, acquisition and discovery of ancient books of traditional Chinese medicine based on knowledge element indexing and knowledge graph[J]. China Journal of Basic Medicine of Traditional Chinese Medicine, 2023, 29(6): 954-959.
[95] HAN X, LI X, LIANG Y, X, et al. Acupuncture and Tuina knowledge graph for ancient literature of traditional Chinese medicine[C]//Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, Houston, 2021: 674-677.
[96] 刘悦悦, 李燕, 李春雨, 等. 基于深度强化学习的中医古籍图谱推理研究[J]. 中国中医药信息杂志, 2024, 31(6): 54-59.
LIU Y Y, LI Y, LI C Y, et al. Research on graph reasoning of ancient books of traditional Chinese medicine based on deep reinforcement learning[J]. China Journal of Traditional Chinese Medicine Information, 2024, 31(6): 54-59.
[97] 高晓苑, 高文佳, 王欣宇, 等. 基于医案文本的名老中医诊疗知识图谱构建方法及应用[J]. 世界科学技术-中医药现代化, 2023, 25(9): 2967-2974.
GAO X Y, GAO W J, WANG X Y, et al. Construction method and application of knowledge mapping of famous and old Chinese medicine practitioners’ diagnosis and treatment based on medical case text[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2023, 25(9): 2967-2974.
[98] XIE Y, YAN C, ZHANG D. Personalized diagnostic modal discovery of traditional Chinese medicine knowledge graph[C]//Proceedings of the 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Huangshan, 2018: 1096-1103.
[99] 董广通, 高琳. 论人工智能技术下名老中医诊疗经验传承发展[J]. 中华中医药杂志, 2023, 38(9): 4026-4029.
DONG G T, GAO L. On the inheritance and development of the diagnosis and treatment experience of famous old Chinese medicine under artificial intelligence technology[J]. Chinese Journal of Traditional Chinese Medicine, 2023, 38(9): 4026-4029.
[100] ZHANG Y, WU X, FANG Q, et al. Knowledge -enhanced attributed multi-task learning for medicine recommendation[J]. ACM Transactions on Information Systems, 2023, 41(1): 1-24.
[101] LIU J, ZHUO H H, JIN K, et al. Sequential condition evolved interaction knowledge graph for traditional Chinese medicine recommendation[J]. arXiv:2305.17866, 2023.
[102] ZHENG Z, LIU Y, ZHANG Y, et al. TCMKG: a deep learning based traditional Chinese medicine knowledge graph platform[C]//Proceedings of the 2020 IEEE International Conference on Knowledge Graph, Nanjing, 2020: 560-564.
[103] CHENG B, ZHANG Y, CAI D, et al. Construction of traditional Chinese medicine knowledge graph using data mining and expert knowledge[C]//Proceedings of the 2018 International Conference on Network Infrastructure and Digital Content, Guiyang, 2018: 209-213.
[104] ZHAO X, WANG Y, LI P, et al. The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data[J]. Frontiers in Pharmacology, 2023, 14: 1147677.
[105] WANG X, QU H, LIU P, et al. A self-learning expert system for diagnosis in traditional Chinese medicine[J]. Expert Systems with Applications, 2004, 26(4): 557-566.
[106] 孙康宁, 孙琦, 李新霞, 等. 基于卷积神经网络的中医面色提取识别研究[J]. 中华中医药杂志, 2021, 36(7): 4286-4290.
SUN K N, SUN Q, LI X X, et al. Research on TCM color extraction and recognition based on the convolutional neural network[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2021, 36(7): 4286-4290.
[107] WANG J, ZHANG G, WANG W, et al. Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT[J]. Journal of Cloud Computing, 2021, 10: 1-12.
[108] LIU Z, ZHENG Z, GUO X, et al. Attentive herb: a novel method for traditional medicine prescription generation[J]. IEEE Access, 2019, 7: 139069-139085.
[109] 陈璐, 赵宇涵, 李伟峰, 等. 基于深度学习的智能中医辅助诊疗系统[J]. 计算机时代, 2023(4): 72-76.
CHEN L, ZHAO Y H, LI W F, et al. Intelligent Chinese medicine auxiliary diagnosis and treatment system based on deep learning[J]. Computer Age, 2023(4): 72-76.
[110] HAN J, DAI Y, RONG X, et al. Medical intelligent processor system and traditional Chinese medicine to treat endometriosis[J]. Microprocessors and Microsystems, 2021, 82: 103842.
[111] LU S, WANG R, CUI L, et al. Wireless networked Chinese telemedicine system: method and apparatus for remote pulse information retrieval and diagnosis[C]//Proceedings of the 2008 6th Annual IEEE International Conference on Pervasive Computing and Communications, 2008: 698-703.
[112] 倪敬年, 曹天雨, 陈雅静, 等. 名老中医经验数字化传承的思考[J]. 中医杂志, 2023, 64(17): 1754-1758.
NI J N, CAO T Y, CHEN Y J, et al. Reflections on the digital inheritance of famous and old Chinese medicine practitioners’ experience[J]. Journal of Traditional Chinese Medicine, 2023, 64(17): 1754-1758.
[113] 王丽婷, 刘长松, 魏玮, 等. 中医学术客观化、智能化传承的思考及初步实现[J]. 中医杂志, 2021, 62(12): 1036-1039.
WANG L T, LIU C S, WEI W, et al. Reflections and preliminary realization of objectivization and intelligent inheritance of Chinese medicine scholarship[J]. Journal of Traditional Chinese Medicine, 2021, 62(12): 1036-1039.
[114] WANG J, DUAN L, LI H, et al. Construction of an artificial intelligence traditional Chinese medicine diagnosis and treatment model based on syndrome elements and small-sample data[J]. Engineering, 2022, 8: 29-32.
[115] BAO Y F, ZHANG Z H, YU H Y, et al. Development of intelligent acupuncture applications and related technologies[J]. World Journal of Traditional Chinese Medicine, 2023, 9(1): 21-28.
[116] OPENAI. GPT-4 technical report[R]. arXiv:2303.08774, 2023.
[117] YANG S, ZHAO H, ZHU S, et al. Zhongjing: enhancing the Chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue[J]. arXiv:2308.03549, 2023.
[118] 张君冬, 杨松桦, 刘江峰, 等. AIGC赋能中医古籍活化: Huang-Di大模型的构建[J/OL]. 图书馆论坛 [2024-03-23]. http://kns.cnki.net/kcms/detail/44.1306.G2.20240124.1341.
002.html.
ZHANG J D, YANG S H, LIU J F, et al. AIGC empowers the activation of ancient books of traditional Chinese medicine: the construction of Huang-Di large model[J/OL]. Library Forum [2024-03-23]. http://kns.cnki.net/kcms/detail/44.1306.G2.20240124.1341.002.html.
[119] TOUVRON H, LAVRIL T, IZACARD G, et al. LLaMA: open and efficient foundation language models[J]. arXiv: 2302.13971, 2023.
[120] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30, 2017.
[121] HU E J, SHEN Y, WALLIS P, et al. LoRA: low-rank adaptation of large language models[J]. arXiv:2106.09685, 2021.
[122] PANG G, SHI J, WANG Z, et al. TCM-GPT: efficient pre-training of large language models for domain adaptation in traditional Chinese medicine[J]. arXiv:2311.01786, 2023.
[123] 叶冠成, 陈佳祺, 张少辉, 等. 中医互联网医疗发展现状、问题及应对策略探究[J]. 中国医院, 2023, 27(11): 34-39.
YE G C, CHEN J Q, ZHANG S H, et al. Exploration of the development status quo, problems and coping strategies of Chinese medicine internet healthcare[J]. China Hospital, 2023, 27(11): 34-39.
[124] NGUYEN T D, NGUYEN T, LE N P, et al. Backdoor attacks and defenses in federated learning: survey, challenges and future research directions[J]. Engineering Applications of Artificial Intelligence, 2024, 127: 107166.
[125] 熊世强, 何道敬, 王振东, 等. 联邦学习及其安全与隐私保护研究综述[J]. 计算机工程, 2024, 50(5): 1-15.
XIONG S Q, HE D J, WANG Z D, et al. A review of research on federated learning and its security and privacy protection[J]. Computer Engineering, 2024, 50(5): 1-15.
[126] ZILLER A, USYNIN D, BRAREN R, et al. Medical imaging deep learning with differential privacy[J]. Scientific Reports, 2021, 11(1): 13524.
[127] 李新龙, 黄培冬, 朱爽，等. 智能化挖掘中医临床诊疗数据面临的问题和挑战[J]. 中华中医药杂志, 2022, 37(12): 6962-6965.
LI X L, HUANG P D, ZHU S, et al. Problems and challenges of intelligent mining of Chinese medicine clinical diagnosis and treatment data[J]. Chinese Journal of Traditional Chinese Medicine, 2022, 37(12): 6962-6965.
[128] 沈晓雄. 个性化医疗: 中医走向世界的契机[J]. 中医药导报, 2019, 25(4): 1-5.
SHEN X X. Personalized medicine: an opportunity for Chinese medicine to go global[J]. Chinese Medicine Herald, 2019, 25(4): 1-5.
[129] FAN F L, XIONG J, LI M, et al. On interpretability of artificial neural networks: a survey[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021, 5(6): 741-760.
[130] WEI J, WANG X, SCHUURMANS D, et al. Chain-of-thought prompting elicits reasoning in large language models[C]//Advances in Neural Information Processing Systems 35, 2022: 24824-24837.