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Review
. 2025 May 1:15:1545646.
doi: 10.3389/fcimb.2025.1545646. eCollection 2025.

The application of machine learning in clinical microbiology and infectious diseases

Cheng Xu #  1 , Ling-Yun Zhao #  2 , Cun-Si Ye  3 , Ke-Chen Xu  4   5 , Ke-Yang Xu  6
Affiliations
Review

The application of machine learning in clinical microbiology and infectious diseases

Cheng Xu et al. Front Cell Infect Microbiol. .

Abstract

With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases. Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning's current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning.

Keywords: application; artificial intelligence; clinical microbiology; infectious diseases; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The development and application for example machine learning model. The building process of machine learning model is mainly from data processing, feature coding, model training, model evaluation and selection, and finally to test data prediction. The applications in clinical microbiology and infectious diseases are included.
See this image and copyright information in PMC

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