Artificial Intelligence in Clinical Microbiology: Applications, Challenges, and Future Directions

Abstract

Clinical microbiology sits at an unusual inflection point: the diagnostic targets have not changed, but the tools available to detect, interpret, and act on them have expanded faster than most laboratories can absorb. Artificial intelligence (AI) has moved from a speculative research interest to a technology with documented, if uneven, clinical utility across microscopy, mass spectrometry, molecular diagnostics, and antimicrobial resistance (AMR) prediction. This narrative review critically examines AI across the clinical microbiology workflow, highlighting AI-assisted Gram stain interpretation, MALDI-TOF-based resistance prediction, genotype-to-phenotype AMR modelling, tuberculosis screening, mycology-specific applications, and the emerging but largely unvalidated role of large language models (LLMs) in laboratory and clinical practice. Recurring weaknesses include small single-centre datasets, near-total absence of external validation, and a persistent gap between published accuracy and real-world deployment. We address bias, explainability, regulatory, and low-resource-setting barriers, and conclude that the field's next phase of progress depends less on model novelty than on rigorous external validation and workflow integration designed with practising microbiologists.

Keywords: Artificial intelligence; machine learning; deep learning; clinical microbiology; antimicrobial resistance; MALDI-TOF mass spectrometry; digital microbiology; large language models