ABSTRACT Artificial intelligence (AI) is reshaping microbiology laboratories by improving diagnostic accuracy, workflow efficiency, and the interpretation of increasingly complex datasets. Machine learning algorithms enhance microbial identification, particularly when applied to MALDI-TOF MS spectral analysis, and support early prediction of antimicrobial resistance through genomic modelling. AI-driven image analysis accelerates microscopic detection of pathogens and reduces operator variability. In metagenomics, AI enables high-resolution profiling of microbial communities and reveals novel associations with human disease. Despite challenges involving data quality, validation, ethics, and regulatory oversight, AI’s integration into microbiology promises substantial gains in precision diagnostics, antimicrobial stewardship, and research capability.
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