四川大学学报（医学版）2026，Vol.57Issue(2)：313-318,6.DOI:10.12182/20260360501
人工智能在临床微生物检测中的应用进展
Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing
摘要
Abstract
Traditional microbiological detection methods have inherent limitations in detection speed,sensitivity,and specificity,making them increasingly unable to meet growing clinical demands.In recent years,artificial intelligence(AI)has been rapidly integrated into clinical microbiological testing,with numerous studies demonstrating its significant potential to enhance pathogen identification,predict antimicrobial susceptibility testing,and advance laboratory automation.This article systematically reviews classical AI algorithms and their latest advancements in this field.For visual data applications,deep learning-based models are used to automatically analyze microscopy images or colony morphology,significantly improving recognition efficiency and diagnostic accuracy.For non-visual data,AI has achieved breakthroughs in analyzing multi-omics data such as genomics,transcriptomics,and metagenomics,and is widely used for rapid pathogen identification and prediction of antimicrobial resistance.Despite its promising prospects,the application of AI in clinical microbiological testing remains in the early stages of transitioning from scientific research to clinical practice.This paper further discusses the key challenges and opportunities encountered during this technological translation,aiming to help clinical professionals comprehensively understand the current status,future trends,and potential impact of AI in this field,thereby promoting its development into reliable and scalable routine diagnostic methods.关键词
人工智能/机器学习/临床微生物检测/抗生素耐药性/综述Key words
Artificial intelligence/Machine learning/Clinical microbiological testing/Antimicrobial resistance/Review引用本文复制引用
李惠,宋珍,赵亚楠,李敏..人工智能在临床微生物检测中的应用进展[J].四川大学学报（医学版）,2026,57(2):313-318,6.基金项目
This work was supported by the National Natural Science Foundation of China(No.82302538),the"AI+Course"cultivation project of the Medical Technology College of Shanghai Jiao Tong University in 2025,and the undergraduate teaching reform project of Shanghai Jiao Tong University in 2020. 国家自然科学基金(No.82302538)、上海交通大学医学院医学技术学院2025 年度"AI+课程"培育项目和上海交通大学医学院2020年本科教学改革项目资助 （No.82302538）
