Data Modeling
Mathematical and Computational Data Modeling | Online ISSN 3143-9217
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Volume 7 Number 1 2026
REVIEWS (Open Access)
AI-Driven Discovery and Diagnostics for Antimicrobial Resistance: Progress, Limitations, and Pathways to Clinical Translation
Md. Al Amin 1*, Jannatul Fardaus 2, Umaer Al Miraj 2,
Data Modeling 7 (1) 1-8 https://doi.org/10.25163/data.7110994
Submitted: 15 September 2026 Revised: 27 October 2026 Accepted: 30 October 2026 Published: 02 November 2026
Abstract
Background: Antimicrobial resistance (AMR) now ranks among the most consequential threats to global health, and the antibiotic discovery pipeline has been too slow, for too long, to keep pace with it. Artificial intelligence (AI) has been proposed as a way to close that gap, but how much of this promise is already realized in practice, rather than simply anticipated, remains unclear. Methods: We conducted a narrative-systematic synthesis of the peer-reviewed and preprint literature on AI applications across AMR diagnostics and antimicrobial discovery, drawing on genomic, transcriptomic, proteomic, and spectral evidence streams, and organizing findings around four domains: rapid phenotypic diagnostics, genomic and metagenomic resistome prediction, explainable AI, and de novo drug design. Results: AI-enabled diagnostics compressed susceptibility-testing turnaround from the conventional 36-72 hours to under 2-4 hours in several platforms, while genomic language models such as DNABERT outperformed conventional classifiers by 12-18% in resistance-gene classification. Explainable AI methods, particularly SHAP, linked model outputs to known resistance mechanisms, and generative frameworks produced antimicrobial peptide candidates with confirmed in vitro and in vivo activity. Even so, nearly all evidence derives from retrospective, single-center validation, and no AI-based AMR tool has yet secured regulatory clearance. Conclusion: AI has demonstrably matured beyond proof-of-concept in AMR diagnostics and discovery, but its clinical future now hinges less on further algorithmic refinement than on prospective validation, data equity, and interpretability standards capable of earning clinician trust.
Keywords: antimicrobial resistance; artificial intelligence; machine learning; whole-genome sequencing; explainable AI; antimicrobial peptides; clinical translation
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