Artificial intelligence-assisted spectral microbiology for rapid microbial identification and antimicrobial resistance prediction: advances, challenges, and future perspectives.

Abstract

Rapid microbial identification and antimicrobial resistance prediction are fundamental to precision diagnostics, effective infection control, and antimicrobial stewardship. Artificial intelligence-assisted spectral microbiology is increasingly being investigated to convert complex spectral and imaging data into rapid microbiological identification and antimicrobial resistance prediction outputs. This review critically examines the integration of AI with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, Raman spectroscopy, Fourier-transform infrared spectroscopy, ultraviolet-visible-near-infrared spectroscopy, hyperspectral imaging, and automated colony imaging for microbial identification, strain discrimination, and AMR prediction. This work focuses on spectral preprocessing, feature engineering, machine-learning architectures, interpretability, validation, and clinical translation. Emerging evidence shows substantial potential to accelerate diagnostics while improving analytical precision and scalability. Nevertheless, dataset heterogeneity, instrument-dependent variability, limited external validation, and insufficient model transparency continue to constrain real-world adoption. Future integration of multimodal spectral, genomic, and clinical data could deliver robust, explainable, and interoperable diagnostic systems. It may support precision microbiology and antimicrobial-resistance surveillance, provided standardized acquisition, external validation, quality control, and uncertainty-aware interpretation are in place.