Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning

Optical spectroscopic techniques relying on light–matter interactions, such as Raman scattering, fluorescence, and infrared absorbance spectroscopy, offer numerous advantages to complement existing cancer detection methods. By combining these spectroscopic techniques with rationally engineered nanomaterials, cancer cells and tissues can be more specifically targeted, and the readout signals can be substantially enhanced. Further integration of machine learning with its potential to identify subtle malignancy indicators may significantly improve the capability of nanomaterial-enabled optical spectroscopy to delineate cancer more precisely. As such, the synergistic integration of optical spectroscopy, nanomaterials, and machine learning may provide unique opportunities for the development of more selective, sensitive, and accurate cancer diagnostic technologies, which can be leveraged to optimize therapeutic strategies and minimize unnecessary interventions to ultimately enhance patient survival outcomes. This Perspective describes numerous strategies incorporating optical spectroscopy, nanomaterials, and machine learning to improve cancer detection and summarizes our outlook on the current landscape and potential future directions of this emerging field.