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[Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing]
Hui Li1, Zhen Song1, Yanan Zhao1
1（ 200025）College of Health Science and Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Summary
Artificial intelligence (AI) is revolutionizing clinical microbiology by improving pathogen identification and antimicrobial susceptibility testing. While AI shows great promise, its integration into routine diagnostics faces challenges but offers significant future potential.
Area of Science:
- Clinical microbiology
- Artificial intelligence (AI)
- Medical diagnostics
Background:
- Traditional microbiological detection methods face limitations in speed, sensitivity, and specificity.
- Clinical microbiology demands are increasing, necessitating advanced diagnostic tools.
- Artificial intelligence (AI) offers potential solutions for enhancing pathogen identification, antimicrobial susceptibility testing, and laboratory automation.
Purpose of the Study:
- To systematically review classical AI algorithms and their advancements in clinical microbiology.
- To discuss the application of AI in analyzing visual and non-visual data for microbiological testing.
- To explore the challenges and opportunities in translating AI from research to clinical practice.
Main Methods:
- Review of classical AI algorithms and deep learning models.
- Analysis of AI applications in visual data (microscopy images, colony morphology).
- Examination of AI in non-visual data (multi-omics data: genomics, transcriptomics, metagenomics).
Main Results:
- Deep learning models significantly improve recognition efficiency and diagnostic accuracy for visual data.
- AI achieves breakthroughs in analyzing multi-omics data for rapid pathogen identification and antimicrobial resistance prediction.
- AI integration in clinical microbiology is currently in the early stages of research-to-practice transition.
Conclusions:
- AI demonstrates significant potential to enhance pathogen identification, predict antimicrobial resistance, and automate laboratory processes in clinical microbiology.
- Key challenges exist in the translation of AI technologies from research to routine clinical practice.
- Further development is needed to establish AI as reliable and scalable diagnostic methods in clinical microbiology.
Keywords:
Antimicrobial resistanceArtificial intelligenceClinical microbiological testingMachine learningReviewMore Related Videos
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