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人工智能技术的快速发展为中医诊断现代化提供了新的机遇。分析人工智能与中医诊断结合的基础、研究进展和难点问题，认为人工智能在中医舌诊、脉诊、闻诊、文本处理智能化方面已取得显著发展，在构建数据驱动型中医诊断模式、多学科融合的中医诊断模式领域亦有有益探索；但目前人工智能技术应用于中医诊断的融合过程仍面临临床数据稀缺且质量参差不齐、人工智能算法对中医辨证思维模式和经验性知识的表达与建模能力有限、可能存在伦理与隐私问题等诸多挑战。通过对人工智能赋能中医诊断的研究现状及发展方向的系统梳理，提出从加强中医大数据建设及人才培养、鼓励跨领域合作、完善法律与伦理框架、推动技术在基层医疗中的普及四个方面促进人工智能技术在中医诊断学中的应用，从而提升中医诊断的现代化水平。
Abstract：The rapid development of artificial intelligence(AI) technology provides new opportunities for the modernisation of traditional Chinese medicine(TCM) diagnosis. By analysing the foundation, research progress and difficulties of the combination of AI and TCM diagnosis, it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis, pulse diagnosis, listening and smelling diagnosis and text processing, and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However, the current integration of AI technology in TCM diagnosis still faces many challenges, such as the scarcity and uneven quality of clinical data, the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge, and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics, it is proposed to promote the application of AI technology in TCM diagnostics in four aspects, namely, strengthening the construction of TCM big data and talent cultivation, encouraging cross-disciplinary cooperation, improving the legal and ethical framework, and promoting the popularity of the technology in primary care, so as to enhance the modernisation of TCM diagnostics.
[1]MANICKAM P, MARIAPPAN SA, MURUGESAN SM,et al. Artificial intelligence(AI)and internet of medical things(IoMT)assisted biomedical systems for intelligent healthcare[J]. Biosensors(Basel), 2022, 12(8):562.
[2]KIM TW. Application of artificial intelligence chatbots,including ChatGPT, in education, scholarly work, programming, and content generation and its prospects:a narrative review[J]. J Educ Eval Health Prof, 2023, 20:38. doi:10. 3352/jeehp. 2023. 20. 38.
[3]JAFAR A, BIBI N, NAQVI RA, et al. Revolutionizing agriculture with artificial intelligence:plant disease detection methods, applications, and their limitations[J].Front Plant Sci, 2024, 15:1356260. doi:10. 3389/fpls.2024. 1356260.
[4]NOORAIN N, SRIVASTAVA V, PARVEEN B, et al. Artificial intelligence in drug formulation and development:applications and future prospects[J]. Curr Drug Metab,2023, 24(9):622-634.
[5]WANG Y, SHI X, LI L, et al. The impact of artificial intelligence on traditional Chinese medicine[J]. Am J Chin Med, 2021, 49(6):1297-1314.
[6]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]. Comput Biol Med, 2024, 170:108074. doi:10.1016/j. compbiomed. 2024. 108074.
[7]孙肇阳，吕桂娇，郭义，等.中医诊疗复杂巨系统的人工神经网络建模原理[J].中华中医药杂志，2022, 37(10):5841-5844.
[8]宋海贝，温川飙，程小恩.基于AI的中医舌象面象辅助诊疗系统构建[J].时珍国医国药，2020, 31(2):502-505.
[9]张立.基于综合性临床诊断数据分析系统的医院导诊应用[J].医学信息学杂志，2020, 41(12):68-72.
[10]邵美萍.基于深度学习的人工智能文本处理系统设计[J].电子技术，2022, 51(6):190-191.
[11]袁方，夏宇轩，周香琴，等.人工智能在医院医疗风险管理中的应用研究[J].卫生经济研究，2024, 41(8):79-82,86.
[12]汪莉，彭成东，徐文兵，等.基于人工智能的舌诊实训与考核平台的设计与应用[J].陕西中医药大学学报，2023, 46(1):108-112.
[13]张冬，张俊华，孟昭鹏，等.基于高光谱图像技术的中医舌诊客观化研究展望[J].中国中医基础医学杂志，2019, 25(9):1324-1326.
[14]LI J, YUAN P, HU X, et al. A tongue features fusion approach to predicting prediabetes and diabetes with machine learning[J]. J Biomed Inform, 2021, 115:103693. doi:10. 1016/j. jbi. 2021. 103693.
[15]黄霖，颜建军，蔡诗雨，等.基于机器视觉的三部脉象仪智能定位的研究[J].世界科学技术-中医药现代化，2023, 25(2):806-812.
[16]CHEN C, LI Z, ZHANG Y, et al. A 3D wrist pulse signal acquisition system for width information of pulse wave[J]. Sensors(Basel), 2019, 20(1):11.
[17]YAN JJ, GUO R, WANG YQ, et al. Objective auscultation of TCM based on wavelet packet fractal dimension and support vector machine[J]. Evid Based Complement Alternat Med, 2014:502348. doi:10. 1155/2014/502348.
[18]江益靓，张旭龙，邓晋，等.数据增强基础上使用卷积神经网络进行闻诊[J].复旦学报（自然科学版），2019,58(3):328-334.
[19]林雪娟，李灿东，吴青海，等．基于电子鼻技术的不同体质青年学生口腔呼气气味图谱研究[J]．中华中医药杂志，2012,27(12):3184-3188.
[20]KELLER A, GERKIN RC, GUAN Y, et al. Predicting human olfactory perception from chemical features of odor molecules[J]. Science, 2017, 355(6327):820-826.
[21]LI H, PANWAR B, OMENN GS, et al. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features[J]. Gigascience, 2018, 7(2):1-11.
[22]陶竹，徐梓铭，郭艳，等.数据挖掘在名老中医经验传承的应用现状与智能化趋势[J].世界中医药，2023, 18(13):1918-1922,1927.
[23]何菊，戴彩艳，胡晨骏，等.基于属性相似度的中药加权网络构建及社团划分方法[J].世界科学技术-中医药现代化，2019, 21(12):2746-2752.
[24]LIANG Z, LIU J, OU A, et al. Deep generative learning for automated EHR diagnosis of traditional Chinese medicine[J]. Comput Methods Programs Biomed, 2019,174:17-23. doi:10. 1016/j. cmpb. 2018. 05. 008.
[25]LIU L, WU X, LIU H, et al. A semi-supervised approach for extracting TCM clinical terms based on feature words[J]. BMC Med Inform Decis Mak, 2020, 20(Suppl 3):118.
[26]宋晔冲，毛沁娜，寿飞萍.中医药辅助下重症疾病管理的多模态融合策略探讨[J].中医药管理杂志，2024, 32(10):248-250.
[27]肖雯迪，朱龙，王洋，等.中医望诊多模态融合技术的应用与思考[J].中医杂志，2024, 65(17):1741-1746.
[28]王俊文，叶壮志.人工智能技术在中医诊断领域应用述评[J].世界科学技术-中医药现代化，2022, 24(2):810-814.
[29]董云春，晏峻峰.融合知识图谱与图卷积神经网络中医证型分类模型[J].计算机技术与发展，2025,35(3):140-147.
[30]李珂.从当代人工智能的发展看马克思的人机关系思想[J].自然辩证法研究，2019, 35(4):71-75.
[31]FATIMA I, REHMAN A, DING Y, et al. Breakthroughs in AI and multi-omics for cancer drug discovery:a review[J]. Eur J Med Chem, 2024, 280:116925.doi:10. 1016/j. ejmech. 2024. 116925.
[32]VATANSEVER S, SCHLESSINGER A, WACKER D,et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases:stateof-the-arts and future directions[J]. Med Res Rev,2021, 41(3):1427-1473.
[33]HAMILTON S, KINGSTON BR. Applying artificial intelligence and computational modeling to nanomedicine[J].Curr Opin Biotechnol, 2024, 85:103043. doi:10. 1016/j. copbio. 2023. 103043.
[34]CRAMPON K, GIORKALLOS A, DELDOSSI M, et al.Machine-learning methods for ligand-protein molecular docking[J]. Drug Discov Today, 2022, 27(1):151-164.
[35]石康乐，谢晴宇，孟庆刚.大数据时代的中医药小数据研究探讨[J].中华中医药学刊，2019, 37(2):372-377.
[36]杨蕴，钟薏，于观贞，等.人工智能促进中医药传承发展的机遇与挑战[J].北京中医药，2019, 38(8):835-838.
[37]马晓彤，刘燕池.信息医学是中医学的本征[J].中医杂志，2018, 59(20):1711-1716.
[38]伊明洋，关建新，闫丽祯，等.基于纵向研究数据的非线性混合效应模型在中医证候演变规律研究中的应用[J].中国中医药信息杂志，2023, 30(10):17-21.
[39]吴蓓，陆树程.反思与应对：AI医疗的伦理与法律风险[J].医学与哲学，2019, 40(21):9-13.
[40]张清，张蓉.“人工智能+法律”发展的两个面向[J].求是学刊，2018, 45(4):97-106,173.
[41]刘建利.医疗人工智能临床应用的法律挑战及应对[J].东方法学，2019(5):133-139.
[42]林静怡，李诗翩，郭义，等.人工智能助力中医药发展现状、问题及建议[J].世界中医药，2022, 17(6):864-867.
[43]赵建华.人工智能时代的教育转型与重塑[J].电化教育研究，2024(12):37-43,97.
基本信息:
DOI：10.13288/j.11-2166/r.2025.14.001
中图分类号:R241;TP18
引用信息:
[1]朱文俊,汤曼诗,佘楷杰等.人工智能赋能下中医诊断学的研究进展及发展对策[J].中医杂志,2025,66(14):1413-1418.DOI:10.13288/j.11-2166/r.2025.14.001.
基金信息:
国家自然科学基金（82430126,82474365,82304990）; 国家级大学生创新创业训练计划项目（202310559128）; 广州市中医方证重点实验室（202102010014）; 暨南大学博士研究生拔尖创新人才培养项目（2024CXB023）