The Fusion of New Materials and Smart Technologies: Redefining How We Detect Antibiotic Resistance

The global spread of antimicrobial resistance (AMR) poses an urgent threat to modern public health, driven by multiple, interacting mechanisms including enzyme inactivation, target modification, efflux pump overexpression, and biofilm formation. While conventional antimicrobial susceptibility testing (AST) and molecular techniques remain the clinical standard, their practical limitations, including time-consuming procedures, reliance on culture, inability to reveal underlying mechanisms, and poor quantification of resistance levels, highlight the urgent need for innovative solutions. This review proposes a paradigm shift in drug resistance diagnostics from static, single-indicator, endpoint detection to dynamic, multi-parameter, predictive diagnostics by integrating advanced functional materials and artificial intelligence (AI). Nanomaterials, metal-organic frameworks, graphene oxide, and microfluidic platforms allow rapid, culture-free detection of resistance enzymes, genes, and biofilms with exceptional sensitivity. Meanwhile, AI architectures—including convolutional neural networks, random forests, and graph neural networks—enable precise signal quantification, multi-modal data fusion, and accurate resistance phenotype prediction. However, clinical translation remains hindered by material reproducibility, biofouling, regulatory gaps, and cost constraints. This review provides a forward-looking roadmap for developing globally deployable, intelligent AMR diagnostic systems within a “One Health” framework.