Archives of Current Research International

Background and Significance: Clinical microbiology laboratories occupy a decisive position in the management of infection and in the containment of antimicrobial resistance, yet they face rising specimen volumes, contracting specialist workforces and persistent dependence on subjective visual interpretation. Artificial intelligence, and in particular supervised machine learning applied to images, mass spectra, genomes and structured laboratory data, has been proposed as a means of relieving these pressures.

Purpose and Scope: This critical narrative review evaluates the state of evidence for artificial intelligence applied across the full clinical microbiology diagnostic workflow, from test ordering and specimen triage through culture reading, microscopy, organism identification, susceptibility determination and interpretive reporting, and examines the conditions under which reported performance is likely to survive translation into routine service.

Approach: Peer-reviewed literature was identified through structured searching of biomedical and multidisciplinary bibliographic sources, supplemented by citation tracking and by consultation of authoritative institutional documents. Evidence was appraised for design adequacy, validation strategy, reference-standard quality, transparency of reporting and relevance to laboratory practice, and was synthesised thematically around workflow position rather than around algorithm architecture.

Principal Findings: Evidence maturity varies sharply by workflow stage. Image-based tasks with unambiguous reference standards, such as growth versus no growth on screening media and the categorisation of stained morphotypes, are supported by the most consistent evidence and by a small number of prospective evaluations. Prediction of antimicrobial susceptibility from mass spectra, genomes or electronic health records is supported by far weaker evidence, with performance that degrades across institutions, organisms and antimicrobial agents. Reported accuracy is systematically higher in retrospective single-centre studies using curated datasets than in evaluations conducted on unselected consecutive specimens. Very few studies report patient-relevant outcomes, and prospective comparative evidence remains scarce.

Unresolved Questions and Implications: The principal unresolved questions concern generalisability across laboratories and populations, the behaviour of models under distributional change, the design of human oversight that neither wastes nor blindly accepts algorithmic output, and the governance arrangements required for continuous post-deployment monitoring. Artificial intelligence in clinical microbiology is best understood as a workflow technology whose value depends on the decision it supports, and adoption should be calibrated to the consequences of error at each stage rather than to headline accuracy.