Research Progress on the Application of Intelligent Traditional Chinese Medicine Diagnosis and Treatment Technology
SHEN Ao
LIU Chuang
LIU Zhimei
LI Zanhua
XIONG Changjiang
FU Liwen
LU Rong
SONG Aifeng
LIU Yanzhu
Abstract：The traditional Chinese medicine(TCM)diagnosis and treatment system mainly relies on the four diagnostic method of observation,auscultation and olfaction,inquiry,and palpation,as well as therapeutic approaches such as acupuncture,herbal medicine,and massage.However,the traditional diagnosis and treatment methods are highly dependent on the sub-jective experience of physicians,facing development difficulties in standardization and objectification.Therefore,integrating intelligent technology into TCM diagnosis and treatment has become a key path and a popular research hotspot for promoting the modernization of TCM.This paper focuses on this topic,summarizes the research status of intelligent Chinese medicine diagnosis technologies at home and abroad,and elaborates on their application and development trends in diagnosis and treatment.In the field of diagnosis,the technology is extending from the objective collection of four diagnostic information to the quantitative analysis of characteristic diagnostic methods.In the field of treatment,intelligent robots of acupuncture,moxibustion,and massage have achieved precise quantification of operations and shown good therapeutic effects in the clinical application of some indications.Intelligent technology has a promising future in TCM diagnosis and treatment,but there are still some common challenges in areas such as human-machine flexible interaction and the difficulty in precisely reproducing diagnosis and treatment techniques.This study aims to sort out the development trajectory and technical bottlenecks in this field and provide valuable references for the application and optimization of artificial intelligence technology in the future of TCM medical field.
Keywords：artificial intelligencetraditional Chinese medicine diagnosis and treatment technologyquantitative analysisintelligent robots
Publication Date：2026-03-05
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：6( 241-246 )