Machine Learning Advances Biomedical Raman Spectroscopy for Clinical Use

This comprehensive review examines the significant contributions of machine learning to biomedical Raman spectroscopy, a label-free technique used for chemical characterization in biological systems. The paper details how machine learning is applied at every stage of the process, from initial data preprocessing and signal correction to advanced tasks like unsupervised structure discovery, supervised diagnosis, molecular stratification, and biomarker identification. A key focus is placed on leveraging machine learning not just for diagnostic classification, but also for generating biologically interpretable and clinically actionable insights. The review highlights how ML can overcome challenges posed by high-dimensional, noisy spectral data, acquisition variability, and biological heterogeneity. Furthermore, the authors discuss the primary obstacles hindering the clinical translation of these technologies, including limited dataset sizes, inter-instrument variability, and issues with reproducibility and external validation. They argue that future progress requires both methodological advancements and greater standardization, robust validation, explainability, and the development of deployment-ready analytical frameworks.