Research and Prospects on the Effectiveness of Artificial Intelligence in Enhancing Diagnostic Homogenization within Medical Consortia
Chen Hu
Chen Min
Liu Xingyuan
Abstract：Objective To systematically review the application models,practical outcomes,and influencing factors of Artificial Intelligence(AI)in improving diagnostic homogenization within medical consortia,providing references for optimizing AI's deployment,policy formulation,and future research.Methods To search relevant literature in both Chinese and English databases such as CNKI,Wanfang,PubMed,MEDLINE,and Web of Science,combining keywords including"Artificial Intelligence","Medical Consorti","Diagnostic Standardization",and"Hierarchical Diagnosis and Treatment".Results AI primarily enhances diagnostic accuracy,consistency,and efficiency in medical consortia particularly in radiology,pathology,and electrocardiography through models such as remote diagnostic centers and clinical decision support systems.However,its effectiveness is constrained by multiple factors,including algorithm vulnerability,data quality,as well as organizational management and user acceptance.Conclusion Artificial intelligence serves as a powerful technological lever for promoting diagnostic homogenization in medical consortia.Future research should prioritize effectiveness evaluation in real-world clinical settings and optimization of human-AI collaboration models.Additionally,it is imperative to establish an AI clinical application evaluation and regulatory framework tailored to China's healthcare context,ensuring the technology can safely and effectively enhance healthcare equity and accessibility.
Keywords：Artificial Intelligencemedical consortiadiagnostic homogenizationhierarchical diagnosis and treatment
Publication Date：2026-03-05
Online Publishing Date：2026-03-18(First online date of this platform, not the publication date of the document)
Pages：4( 93-96 )