Applications of Machine Learning-Assisted MALDI-TOF Mass Spectrometry in Clinical Bacterial Analysis: Research Progress and Clinical Translation

Abstract The integration of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning offers clinical microbiology laboratories a way to rapidly obtain information on antimicrobial resistance, typing, and molecular markers beyond routine species identification. However, translation from research to clinical practice remains constrained by three major bottlenecks: substantial heterogeneity in spectral acquisition and preprocessing, the decisive influence of validation design on the strength of evidence, and a systematic lack of workflow evaluation data in implementation studies. Available evidence indicates that culture duration, measurement mode, and cross-center or temporal changes can substantially affect model generalizability. Yet most studies still focus on internal discrimination, with insufficient evaluation of calibration reliability, automated output coverage, confirmatory testing burden, and time gained in reporting; few studies directly link model outputs to antimicrobial adjustment or infection-control actions. This review synthesizes advances across five dimensions: spectrum formation, clinical tasks, validation design, laboratory workflow integration, and postdeployment monitoring. Based on the hierarchy of validation evidence, we propose that the field is moving from feature-association discovery toward validation of clinical transferability. Future priorities should therefore shift from increasingly complex algorithms to standardized multicenter spectrum-sharing and validation frameworks and prospective workflow studies that determine whether additional spectral information can yield measurable time or resource savings.