Keywords
Artificial intelligence
Machine learning
Antibiotic discovery
Resistance prediction
Clinical decision support
Categories
How to Cite
Abstract
Antimicrobial resistance (AMR) has become one of the most urgent threats to global public health, with an estimated 4.95 million deaths associated with bacterial resistance in 2019 alone. Conventional approaches to antibiotic development, resistance detection, and treatment selection are increasingly unable to keep pace with the evolution of resistant pathogens. In recent years, artificial intelligence (AI) has emerged as a powerful complement across the entire AMR control pipeline, from the discovery of novel antibiotics and antimicrobial peptides to the prediction of resistance phenotypes, the interpretation of genomic and mass-spectrometry data, and the personalization of antibiotic treatment. This review synthesizes the current state of research on AI-based approaches to controlling drug-resistant bacteria. It examines deep learning-driven antibiotic discovery, machine learning models for resistance gene surveillance and susceptibility prediction, and AI-assisted clinical decision support for antimicrobial stewardship. It then critically evaluates persistent challenges, including data heterogeneity and label noise, confounding by bacterial population structure, limited external validation, interpretability, and regulatory hurdles. Finally, it outlines promising directions, including foundation models, population-structure-aware learning, prospective clinical trials, and the integration of AI into One Health surveillance systems. The review concludes that AI is poised to become a central component of a data-driven response to AMR, but that its clinical impact will depend on rigorous validation, transparent reporting, and close collaboration between computational scientists, clinicians, and public health authorities.
References
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