Vibrational spectroscopy and artificial intelligence for dynamic phenotypic characterization of antimicrobial resistance

The rapid emergence of antimicrobial resistance has created an urgent need for diagnostic technologies capable of rapidly identifying resistant phenotypes and informing precision antimicrobial therapy. Conventional antimicrobial susceptibility testing methods remain clinically reliable; however, they are limited by relatively long turnaround times and restricted physiological resolution. In recent years, vibrational spectroscopy techniques have emerged as promising platforms for rapid phenotypic microbiology because they generate label-free biochemical fingerprints associated with proteins, lipids, nucleic acids, carbohydrates, and membrane biomolecules. When integrated with machine learning and artificial intelligence, these technologies could provide highly sensitive tools for rapid bacterial classification, resistance prediction, and monitoring of dynamic physiological adaptation during antibiotic exposure. This review discusses the integration of chemometrics, machine learning, and deep learning algorithms with spectroscopic datasets, emphasizing both predictive performance and biological interpretability. It is highly useful for bacterial identification, biochemical characterization, and resistance profiling across multiple microbial species. Major controversies regarding genotype-versus-phenotype diagnostics, reproducibility, standardization, and AI explainability are also examined. Finally, we highlight current research gaps and future directions, including single-cell spectroscopy, multimodal phenotyping, explainable AI frameworks, and approaches in systems microbiology. Taken together, the available evidence indicates that integrating vibrational spectroscopy with artificial intelligence could substantially reshape future antimicrobial resistance diagnostics and mechanistic microbiology.