Role of Artificial Intelligence in Clinical Microbiology: Evidence, Implementation Challenges, and Future Directions
Corresponding author: Bahzad Ahmad Farhan & Hafiz Muhammad Sultan; farhanbehzad@gmail.com & Sultanjameel909@gmail.com
Abstract
Artificial intelligence (AI) is increasingly applied to clinical microbiology workflows, but strong technical performance does not necessarily establish clinical usefulness. This narrative review, based on literature searched through 30 September 2026, critically examines AI-assisted culture-plate analysis, microscopy, MALDI-TOF mass spectrometry, genomic and rapid phenotypic antimicrobial susceptibility prediction, molecular-result interpretation, and laboratory-linked surveillance. Evidence was evaluated according to the independence of the test data, validation design, measurement unit, clinical comparator, and implementation outcomes. Culture-image screening has been assessed in a three-laboratory study involving 104,730 specimens, whereas some promising spectral models rely on very few independent test isolates. Machine-learning-assisted nanomotion AST has demonstrated 89.5–98.9% accuracy in an independent test series, but performance varies across antibiotic–organism combinations. A published PCR-interpretation study reported agreement across 22,200 interpretations and reduced manual analysis time. Separately, total laboratory automation shortened urine-culture reporting time in a before–after study, although this improvement cannot be attributed to AI alone. Evidence for genomic resistance prediction, prescribing support, and outbreak prediction remains heterogeneous, predominantly retrospective, or insufficiently externally validated. Important barriers include dataset shift, in adequate reference standards, sample-level data leakage, limited prospective clinical-impact studies, workflow costs, equity, and regulatory oversight. The central priority is therefore not higher accuracy alone but transparent reporting, independent multicentre validation, and evaluation of patient and laboratory outcomes. AI should be introduced as a monitored addition to microbiologist-led practice, with implementation decisions matched to the strength of evidence for each specific task.
Keywords: Clinical microbiology; Artificial intelligence; Antimicrobial resistance; Diagnostic accuracy; MALDI-TOF; Laboratory automation; Model validation
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Declarations
Funding
This work received no external funding, as reported by the authors.
Conflict of interest
The authors declare no conflicts of interest.
Ethics approval
Not applicable; this narrative review uses previously published literature and includes no original participants, patient-level data collection, or animal experiments.
Informed consent
Not applicable
Data availability
No new research datasets were generated. The reviewed publications are identified in the References; an exhaustive database-screening log is not included.
Author contributions
Anisha Khan and Bahzad Ahmad Farhan contributed to the conceptualization and planning of the manuscript. Anisha Khan, Wooda Farooq, Bahzad Ahmad Farhan, and Shahzeen Mushier contributed to the original manuscript drafting. Anisha Khan and Wooda Farooq prepared the figures. Bahzad Ahmad Farhan, Hafiz Muhammad Sultan and Wooda Farooq critically reviewed and revised the manuscript and contributed to its final formatting and preparation for submission. All authors read and approved the final version of the manuscript.
AI-use disclosure
Generative AI tools (Claude and ChatGPT) assisted with language editing, literature and citation checking, figure planning, and manuscript formatting. The authors take full responsibility for the final manuscript and must verify every cited source, scientific claim, and submission requirement.
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Anisha Khan, Wooda Farooq, Bahzad Ahmad Farhan*, Hafiz Muhammad Sultan*, Shahzeen Mushier. Role of Artificial Intelligence in Clinical Microbiology: Evidence, Implementation Challenges, and Future Directions. Journal of Biological and Sustainability Sciences. 2026;2(1):37-45. https://doi.org/10.5281/zenodo.23923742