Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation
arXiv:2606.14169v1 Announce Type: new Abstract: Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support. Biomedical Raman spectra are, however, high-dimensional, noisy, and affected by fluorescence background, acquisition variability, and biological heterogeneity, making robust computational analysis essential. This review examines the role of machine learning across the biomedical Raman spectroscopy pipeline,
Advances in machine learning are increasingly enabling the robust analysis of complex biomedical data, making its application to Raman spectroscopy a timely development.
This integration promises to significantly enhance diagnostic capabilities and personalized medicine by translating high-dimensional spectral data into actionable clinical insights.
The ability to extract meaningful patterns from noisy and variable biomedical Raman spectra moves closer to routine clinical implementation, expanding the utility of this diagnostic tool.
- · Biomedical diagnostics companies
- · Oncology researchers
- · Machine learning developers
- · Healthcare providers
- · Traditional diagnostic methods (in specific niches)
- · Companies without ML integration capabilities
Machine learning becomes an indispensable component of advanced biomedical spectroscopic diagnostics.
Improved early disease detection and more personalized treatment strategies become widely available.
The economic burden of disease may be reduced through earlier intervention and more efficient therapies, shifting healthcare resource allocation.
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Read at arXiv cs.LG