Artificial Intelligence Applications in Antimicrobial Resistance: Comprehensive Review of Predictive Models, Diagnostic Innovations, and Clinical Integration

Antimicrobial resistance (AMR) represents a critical global health crisis, driving increased mortality, treatment failure, and economic burden. Artificial intelligence (AI) offers transformative potential to counter this threat by enhancing detection, diagnostics, and therapeutic precision. This narrative review synthesizes recent advances in AI-based approaches for AMR prediction, antimicrobial discovery, and clinical decision support, drawing on representative peer-reviewed studies published between January 1, 2015, and April 24, 2026. Models such as Deeparg-LS, XGBoost, and vision transformers achieved remarkable predictive accuracy using genomic, spectroscopic, and clinical data (AUC > 0.90; sensitivity/specificity >95%). AI-driven clinical decision support systems reduced antibiotic mismatches by up to 67%, while generative algorithms accelerated antimicrobial peptide discovery with 76% validation success. Deep learning frameworks improved metagenomic resistance profiling, and microscopy-based diagnostics shortened antimicrobial susceptibility testing by 50-70%. However, major challenges persist, including dataset heterogeneity, computational intensity, limited model transferability, and ethical concerns related to data privacy, bias, and interpretability. Emerging strategies such as explainable AI and federated learning show promise in addressing these issues. Overall, AI stands as a pivotal enabler in the fight against AMR, with future progress hinging on interdisciplinary collaboration, standardized validation, and responsible integration into clinical practice.