Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation

2026-06-12

AI Summary

This work addresses the challenges posed by high dimensionality, strong noise, fluorescence background interference, and sample heterogeneity in biomedical Raman spectroscopy by developing an end-to-end machine learning framework that integrates preprocessing, unsupervised structure discovery, supervised diagnosis, interpretability analysis, and multimodal fusion. Innovatively combining signal correction, representation learning, transfer learning, and explainable AI techniques with pathological and molecular profiling, the framework not only enhances performance in cancer diagnosis and molecular subtyping but also prioritizes biological interpretability and clinical usability. The study further introduces a standardized pipeline, robust validation protocols, and a deployment-ready architecture, offering methodological solutions to overcome limitations arising from data scarcity, instrumental variability, and translational validation bottlenecks, thereby advancing Raman–AI systems toward reliable clinical application.

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, from preprocessing and signal correction to unsupervised structure discovery, supervised diagnosis and molecular stratification, representation and transfer learning, explainability, biomarker discovery, and multimodal integration with imaging, pathology, and molecular profiling. Emphasis is placed on the use of machine learning not only for diagnostic classification, but also for biologically interpretable and clinically actionable analysis. We also discuss the main barriers to clinical translation, including limited dataset sizes, inter-instrument variability, inconsistent preprocessing, insufficient external validation, reproducibility concerns, and limited sharing of software, data, and metadata. We argue that progress will require methodological advances together with standardization, robust validation, explainability, and deployment-ready analytical frameworks. By integrating methodological, biomedical, and translational perspectives, this review outlines key directions for developing reliable and clinically deployable Raman-AI systems.

Problem

• Raman spectroscopy
• machine learning
• clinical translation
• biomedical data
• computational analysis

Innovation

• Raman spectroscopy
• machine learning
• clinical translation
• explainable AI
• multimodal integration