Antimicrobial resistance (AMR) represents an escalating global health threat, demanding diagnostic strategies capable of rapid, accurate, and comprehensive pathogen characterization. Genomic sequencing has transformed our ability to elucidate resistance mechanisms and track their evolution, yet its routine clinical adoption remains limited by cost, workflow constraints, and extended turnaround times. This narrative review examines how artificial intelligence (AI) and machine learning (ML) can enhance and operationalize sequencing-based diagnostics across the clinical microbiology continuum. We summarize current AI applications in whole-genome sequencing for AMR prediction, pan-genome feature extraction, and multicenter model generalizability, including emerging approaches such as federated learning. We then explore AI-driven metagenomic analytics for pathogen detection, resistome profiling, outbreak investigation, and prognostic modeling. Complementary non-genomic technologies, Raman spectroscopy and MALDI-TOF MS, are also evaluated for their potential to deliver rapid resistance profiling when integrated with ML. Finally, we discuss practical barriers, including cost, dataset standardization, interpretability, and regulatory challenges, while outlining future directions toward scalable, explainable, and equitable AI-guided diagnostics. Integrating AI with genomic and rapid phenotypic tools offers a pathway to real-time surveillance, optimized antimicrobial stewardship, and strengthened preparedness against emerging infectious threats.
Scaglione et al. (2026) studied this question.