From Days to Hours: Artificial Intelligence in Antimicrobial Resistance Diagnostics and Drug Discovery, and Why No Tool Has Yet Reached the Clinic

Abstract

Background: Antimicrobial resistance (AMR) is projected to contribute to millions of deaths in the coming decades, and the conventional antibiotic-discovery pipeline has, by most accounts, not kept pace with it. Artificial intelligence (AI) is frequently proposed as a corrective, though whether that promise has translated into demonstrable clinical benefit is less often examined directly. Methods: We conducted a narrative-systematic review of peer-reviewed and preprint literature on AI applications in AMR diagnostics and antimicrobial discovery, searching PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library through mid-2026, and organized findings across four domains: rapid phenotypic diagnostics, genomic and metagenomic resistome prediction, explainable AI, and de novo drug design. Results: AI-enabled diagnostics reduced susceptibility-testing turnaround from a conventional 36–72 hours to under 2–4 hours in several platforms; genomic language models such as DNABERT outperformed conventional classifiers by 12–18% in resistance-gene classification; explainable AI methods, SHAP in particular, linked model predictions to known resistance mechanisms; and generative frameworks yielded antimicrobial peptide candidates with confirmed in vitro and in vivo activity. Nearly all of this evidence, however, derives from retrospective, single-center validation, and no AI-based AMR tool has yet secured regulatory clearance anywhere. Conclusion: AI has moved convincingly beyond proof-of-concept in AMR diagnostics and discovery, but its path to the clinic now depends less on further algorithmic refinement than on prospective validation, equitable data representation, and interpretability standards that clinicians can reasonably trust.