[1]罗思言,王心舟,饶向荣.人工智能在中医诊断中的应用进展[J].中国医学物理学杂志,2022,39(5):647-654.[doi:DOI:10.3969/j.issn.1005-202X.2022.05.021]
　LUO Siyan,WANG Xinzhou,et al.Advances in the application of artificial intelligence in traditional Chinese medicine diagnosis[J].Chinese Journal of Medical Physics,2022,39(5):647-654.[doi:DOI:10.3969/j.issn.1005-202X.2022.05.021]
点击复制

人工智能在中医诊断中的应用进展()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第5期
页码:
647-654
栏目:
医学人工智能
出版日期:
2022-05-27

文章信息/Info

Title:
Advances in the application of artificial intelligence in traditional Chinese medicine diagnosis
文章编号:
1005-202X（2022）05-0647-08
作者:
罗思言1; 2; 王心舟3; 饶向荣1
1.中国中医科学院广安门医院肾病科， 北京 100053； 2.北京中医药大学广安门医院， 北京 100029； 3.同济大学电子与信息工程学院， 上海 201804
Author(s):
LUO Siyan1;  2;  WANG Xinzhou3;  RAO Xiangrong1
1. Department of Nephrology, Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China 2. Guanganmen Hospital, Beijing University of Chinese Medicine, Beijing 100029, China 3. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
关键词:
人工智能; 中医四诊; 深度学习; 综述
Keywords:
Keywords: artificial intelligence 4 diagnostic methods of traditional Chinese medicine deep learning review
分类号:
R318；TP18
DOI:
DOI:10.3969/j.issn.1005-202X.2022.05.021
文献标志码:
A
摘要:
本研究立足于各类人工智能算法的数学原理，阐述了人工智能在中医诊断中的应用现状及问题。其中传统机器学习算法，如支持向量机、贝叶斯算法等因其小样本学习的特性，在闻诊、问诊等场景具备较高的精度与稳健性；而近年来新兴的深度学习算法则可以处理如图像、音频信号、文本等非结构化数据，与望诊、切诊等场景相契合；多模态深度学习则可以充分挖掘望闻问切数据中的信息，并在特征空间中进行隐式的四诊合参。人工智能的引入可以进一步推动中医的客观化、定量化发展，但其数据驱动的特性要求进一步规范现行的中医数据库建立流程。
Abstract:
Abstract: Based on the mathematical principles of various artificial intelligence algorithms, the current situation and problems of artificial intelligence application in traditional Chinese medicine (TCM) diagnosis is expounded. The traditional machine learning algorithms, such as support vector machines and Bayesian algorithms, have high accuracy and robustness in auscultation, inquiry and other scenarios because of their characteristic of small sample learning. Some deep learning algorithms emerging in recent years which can process unstructured data such as images, audio signals, texts, etc. are suitable for scenarios such as inspection and palpation. Multi-modal deep learning can fully mine the information in the data of inspection, auscultation, inquiry and palpation, and perform implicit analysis of 4 TCM diagnostic methods in the feature space. The introduction of artificial intelligence can further promote the objective and quantitative development of TCM, but its data-driven nature requires further standardization of the current TCM database establishment.

相似文献/References:

[1]王弈,李传富.人工智能方法在医学图像处理中的研究新进展[J].中国医学物理学杂志,2013,30(03):4138.[doi:10.3969/j.issn.1005-202X.2013.03.013]
[2]王亚,李永欣,黄文华.人类脑计划的研究进展[J].中国医学物理学杂志,2016,33(2):109.[doi:10.3969/j.issn.1005-202X.2016.02.001]
　[J].Chinese Journal of Medical Physics,2016,33(5):109.[doi:10.3969/j.issn.1005-202X.2016.02.001]
[3]祁红琳,胡先玲,李传明,等. 基于MRI纹理特征的早期肝癌术后复发预测[J].中国医学物理学杂志,2017,34(9):908.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.010]
　[J].Chinese Journal of Medical Physics,2017,34(5):908.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.010]
[4]纪春阳,徐秀林,王燕. 深度神经网络技术在肿瘤细胞识别中的应用[J].中国医学物理学杂志,2019,36(9):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
　JI Chunyang,XU Xiulin,WANG Yan. Application of deep neural network in tumor cell recognition[J].Chinese Journal of Medical Physics,2019,36(5):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
[5]王沛沛,李金凯,李彩虹,等.基于人工智能技术的危及器官自动勾画在胸部肿瘤中的应用[J].中国医学物理学杂志,2019,36(11):1346.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.019]
　WANG Peipei,LI Jinkai,LI Caihong,et al.Application of automatic organs-at-risk segmentation based on artificial intelligence technology in thoracic tumors[J].Chinese Journal of Medical Physics,2019,36(5):1346.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.019]
[6]莫梓华,高红霞,黄飚.人工智能在中枢神经系统疾病影像诊断中的应用进展[J].中国医学物理学杂志,2020,37(6):792.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.025]
　MO Zihua,GAO Hongxia,et al.Application of artificial intelligence in imaging diagnosis of central nervous system diseases: a review[J].Chinese Journal of Medical Physics,2020,37(5):792.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.025]
[7]刘思远,张丽军,刘雷.人工智能在抗击新型冠状病毒肺炎疫情中的应用[J].中国医学物理学杂志,2020,37(8):1076.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.026]
　LIU Siyuan,ZHANG Lijun,LIU Lei.Application of artificial intelligence in fighting against COVID-19 pandemic[J].Chinese Journal of Medical Physics,2020,37(5):1076.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.026]
[8]陈思佳,石丽婉,林勤.基于知识的放射治疗技术研究[J].中国医学物理学杂志,2020,37(11):1350.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.002]
　CHEN Sijia,SHI Liwan,LIN Qin.Research on knowledge-based radiation therapy[J].Chinese Journal of Medical Physics,2020,37(5):1350.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.002]
[9]陈子印,白艳春,徐巍,等.人工智能云技术在乳腺癌患者心脏亚结构自动勾画中的应用[J].中国医学物理学杂志,2020,37(12):1599.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.024]
　CHEN Ziyin,BAI Yanchun,XU Wei,et al.Application of artificial intelligence cloud technology in auto-segmentation of cardiac substructure of breast cancer patients[J].Chinese Journal of Medical Physics,2020,37(5):1599.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.024]
[10]邓灵波,周雯,赵双全,等.人工智能辅助诊断系统在新型冠状病毒肺炎诊断中的初步应用[J].中国医学物理学杂志,2020,37(12):1604.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.025]
　DENG Lingbo,ZHOU Wen,ZHAO Shuangquan,et al.Preliminary application of AI diagnosis system in the diagnosis of the novel coronavirus infected pneumonia[J].Chinese Journal of Medical Physics,2020,37(5):1604.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.025]

备注/Memo

备注/Memo:
【收稿日期】2021-12-11 【基金项目】国家自然科学基金（81973683） 【作者简介】罗思言，硕士研究生，研究方向：中西医结合，E-mail: 374638245@qq.com 【通信作者】饶向荣，主任医师，教授，研究方向：中西医结合，E-mail: raoyisheng@163.com
更新日期/Last Update: 2022-05-27