Artificial Intelligence in Bacterial Identification and Antimicrobial Resistance

Dear Colleagues,

Antimicrobial resistance (AMR) and the increasing complexity of bacterial infections represent major challenges for healthcare systems worldwide. Rapid and accurate bacterial identification, antimicrobial susceptibility testing (AST), resistance prediction, and surveillance are essential for appropriate clinical management and effective antimicrobial stewardship. However, conventional microbiological approaches may be constrained by turnaround time, laboratory workload, data complexity, and the need for specialized expertise.

Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning are creating new opportunities to transform microbiological diagnostics and AMR research. AI-based approaches can integrate and analyze complex and heterogeneous datasets generated through genomic and metagenomic sequencing, MALDI-TOF mass spectrometry, microscopy, imaging, spectroscopy, and conventional microbiological methods. These technologies offer promising opportunities to improve bacterial identification, enhance AST and resistance prediction, support AMR surveillance, and accelerate antimicrobial discovery.

We are pleased to invite you, as researchers, clinicians, microbiologists, data scientists, bioinformaticians, engineers, and other experts, to contribute to this Special Issue, which will highlight recent advances in the application of AI to bacterial identification and antimicrobial resistance.

This Special Issue aims to highlight recent advances in AI, machine learning, and deep learning for bacterial identification and antimicrobial resistance, focusing on microbiological diagnostics, AMR detection and prediction, surveillance, and antimicrobial discovery. The topic is well aligned with the scope of Applied Sciences, particularly in applied computational methods, intelligent systems, automation, biosensing, biomedical technologies, and data-driven applications.

In this Special Issue, original research articles, reviews, systematic reviews, and methodological contributions are welcome. Research areas may include (but are not limited to) the following:

• AI and machine learning for bacterial identification, classification, microscopy, image analysis, and antimicrobial susceptibility testing;
• AI-based prediction and detection of antimicrobial resistance using genomic, metagenomic, phenotypic, and resistome data;
• AI applications in whole-genome sequencing, metagenomics, AMR surveillance, epidemiology, and outbreak investigation;
• AI-enabled diagnostic technologies, including MALDI-TOF MS, Raman spectroscopy, biosensors, and other analytical platforms;
• Intelligent laboratory automation, robotics, and AI-assisted microbiology workflows;
• AI-assisted discovery and design of novel antibiotics, antimicrobial peptides, and other antibacterial compounds;
• Explainable, robust, and clinically transferable AI approaches, including data quality, validation, reproducibility, standardization, ethics, implementation, and One Health applications.

We look forward to receiving your contributions.

Prof. Dr. Said Ezrari
Guest Editor

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