CHINESE JOURNAL OF MEDICINAL GUIDE >
Design and Deployment of a Robot-based Intelligent Auxiliary Diagnosis and Treatment System for Traditional Chinese Medicine
Received date: 2025-07-14
Revised date: 2025-09-22
Accepted date: 2026-03-18
Online published: 2026-03-19
Objective: To address the imbalance in the allocation of traditional Chinese medicine (TCM) medical resources and the efficiency bottleneck in the inheritance of master-apprentice experience through modern information technology.Methods: The integrated software and hardware technology was utilized to achieve the collection and processing of information from the four diagnostic methods of TCM (inspection of the tongue and face, inquiry, and palpation). Deep learning analysis of historical medical records from renowned veteran TCM practitioners was conducted through large-scale AI models to establish a mapping model between the four diagnostic data and auxiliary diagnosis.Results: The design and development of a robot-based intelligent auxiliary diagnosis and treatment system for TCM were completed, and the preliminary deployment of the system was carried out. This achieved the collection of four diagnostic information from 667 clinical cases and provided intelligent auxiliary diagnosis for 542 patient cases, laying the foundation for the verification of clinical diagnosis and treatment efficacy.Conclusion: The design and deployment of the robot-based intelligent auxiliary diagnosis and treatment system for TCM explores a novel pathway for optimizing the allocation of TCM resources and enhancing the efficiency of experience inheritance.
思木 童
,
珂 吉
,
祯 过
,
鹏程 刘
,
新锋 翁
.
Design and Deployment of a Robot-based
Intelligent Auxiliary Diagnosis and Treatment System for Traditional Chinese
Medicine
[1] 肖金华.基于AI的医疗信息可视化构建与效果评价[J].医学信息,2025,38(4):44-53.
[2] 宋元林,蒋维芃,胡洁,等.医学新质生产力研究进展及展望[J].中国医药导刊,2025,27(2):135-141.
[3] 郑琰莉,韩福海,李舒玉,等.人工智能大模型在医疗领域的应用现状与前景展望[J].医学信息学杂志,2024,45(6):24-29.
[4] 景武堂,万浩浩,苗长丰,等.5G远程机器人手术的应用现状及展望[J].机器人外科学杂志(中英文),2025,6(1):1-5.
[5] 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.
[6] 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.
[7] 钟臻,李晓洁,何萍.智慧中医服务新模式的探索与思考[J].中国数字医学,2024,19(7):12-16,28.
[8] 无锡市中医医院.我院被确定为国家中医药管理局首批中医诊疗模式创新试点单位[EB/OL].(2015-01-30)[2025-03-25].https://www.wxtcm.com/News/XinWenZhongXin/3078.html.
[9] 国家统计局.第七次全国人口普查主要数据情况[EB/OL].(2021-05-11)[2025-03-25].https://www.stats.gov.cn/sj/xwfbh/fbhwd/202302/t20230203_1901080.html.
[10] 王丽芹,王玉洁,高兆虹,等.“思政引领、专业赋能”中医学专业人才培养改革思路探讨[J].中国医药导报,2024,21(34):79-82,87.
[11] 孙荪,狄留庆,倪菲菲,等.健康中国战略下中医学类专业结构优化与人才培养创新实践[J].时珍国医国药,2024,35(9):2247-2250.
[12] 刘旺华,刘岩松,邓奕辉,等.新医科背景下中医拔尖人才培养模式探寻[J].湖南中医药大学学报,2024,44(10):1904-1907.
[13] 高阳,刘洋,毛泓珺,等.基于师承教育探析中医人才培养模式[J].湖南中医杂志,2024,40(3):108-111.
[14] 姜天童,赵宇平,赵玉凤,等.机器视觉技术在中医智能设备中的应用分析与探讨[J].中国中医基础医学杂志,2024,30(3):407-412.
[15] 谭建聪,肖晓霞,邹北骥.一种基于实例分割的舌体分割方法[J].中国卫生信息管理杂志,2023,20(3):459-464.
[16] 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].Comput Struct Biotechnol J, 2020,18:973-980.
[17] Zhou X, Li C, Su H, et al.Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning[J].Technol Health Care, 2024,32(S1):207-216.
[18] 张一帆,朱赠桦,钟方榕,等.基于知识图谱的中医智能养生系统的设计与应用[J].中国数字医学,2023,18(10):77-82.
[19] 杨雪霁.面向多人语音识别的对话系统研究[J].自动化与仪器仪表,2023,(8):286-290.
[20] 郭颖,王点墨,邹碧君,等.基于机器学习方法的临床研究报告规范和建议[J].中国医药导刊,2025,27(2):201-206.
[21] 夏天雨,周怡,尤翀,等.面向临床研究的统计软件系统开发[J].中国医药导刊,2024,26(11):1080-1086.
[22] 顾任钧,谷鑫.大语言模型在中医诊断学教学中的应用[J].中国医药导刊,2024,26(7):737-741.
[23] 毛乡芸,毕伯竹,曹晨龙,等.大语言模型技术在医药临床研发中的应用[J].中国医药导刊,2024,26(11):1093-1097.
/
| 〈 |
|
〉 |