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Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Published on: July 11, 2016

Artificial intelligence and its application in clinical microbiology.

Assia Mairi¹, Lamia Hamza², Abdelaziz Touati¹

¹Université de Bejaia, Laboratoire d'Ecologie Microbienne, Bejaia, Algeria.

Expert Review of Anti-Infective Therapy

|March 25, 2025

Summary

Artificial intelligence (AI) enhances pathogen identification and antimicrobial resistance (AMR) detection in clinical microbiology. Further research and collaboration are needed to overcome challenges and implement AI tools effectively in global healthcare.

Keywords:

Artificial intelligence antibiotic resistance convolutional neural networks deep learning infectious diseases machine learning parasitology virology

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Area of Science:

Clinical Microbiology
Artificial Intelligence in Diagnostics
Infectious Disease Management

Background:

Traditional microbiological diagnostics struggle with speed and accuracy in pathogen identification and antimicrobial resistance (AMR) evaluation.
Artificial intelligence (AI) presents a paradigm shift for enhancing diagnostic capabilities in clinical microbiology.
The integration of AI necessitates a thorough review of its applications, advancements, and implementation challenges.

Purpose of the Study:

To comprehensively review AI-driven methodologies in clinical microbiology.
To analyze the application of AI in pathogen detection, AMR prediction, and diagnostic imaging.
To identify challenges and future directions for AI integration in infectious disease diagnostics.

Main Methods:

Literature search of PubMed, Scopus, and Web of Science (2018-2024).
Prioritization of peer-reviewed studies focusing on AI's diagnostic accuracy, workflow efficiency, and clinical validation.
Analysis of AI methodologies including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs).

Main Results:

AI significantly improves diagnostic precision and operational efficiency in microbiology.
AI applications demonstrated success in virology (e.g., COVID-19 RT-PCR), parasitology (e.g., malaria detection), and bacteriology (e.g., automated colony counting).
Key challenges include data heterogeneity, model interpretability, and ethical considerations.

Conclusions:

AI holds significant potential to revolutionize clinical microbiology diagnostics and combat infectious diseases.
Robust validation, interdisciplinary collaboration, and development of explainable AI are crucial for successful implementation.
Standardized and equitable AI tools are needed for global healthcare settings to bridge implementation gaps.