Machine learning–enabled vibrational spectroscopy for translational bioclinical applications

This is the authors' abstract. We don't add key points for this paper.

Vibrational spectroscopy has become a versatile, label-free microanalytical platform for interrogating molecular alterations in biological and clinical analysis, exploiting the complementary strengths of infrared and Raman spectroscopies. Recent advances in FTIR microscopy, Raman microscopy, and surface-enhanced Raman scattering (SERS) have significantly improved chemical specificity, spatial resolution, and sensitivity, positioning these techniques as promising tools for bioclinical investigations. Likewise, the integration of machine learning has enhanced data interpretation and classification performance; however, clinical translation remains constrained by limited interpretability, inconsistent validation practices, and poor inter-study reproducibility. This review critically synthesises recent developments in machine learning–assisted vibrational spectroscopy with an emphasis on microchemical robustness rather than an exhaustive survey of applications. Evaluation of methodological choices across the analytical pipeline, from spectral acquisition and preprocessing to feature extraction, model selection, and validation, reveals how these decisions systematically influence analytical reliability and clinical credibility. The balance between classical chemometrics and modern machine learning is critically examined alongside challenges posed by high-dimensional heterogeneous data and the need for interpretable, regulation-aligned models. A structured roadmap is proposed for reproducible and clinically actionable infrared and Raman spectroscopic microchemical diagnostics.