Machine Learning Integration in Optical Sensing: Trends, Challenges, and Biomedical Applications

Optical sensors are essential for biomedical diagnostics, environmental monitoring, and personalized healthcare, but their performance is often limited by data complexity, non-specific responses, and real-time processing challenges. This review highlights how machine learning (ML) integration addresses these limitations across diverse platforms, including colorimetric, fluorescence, interferometric, plasmonic, and surface-enhanced Raman scattering (SERS)-based sensors. Key ML approaches, supervised, unsupervised, semi-supervised, reinforcement, and deep learning are discussed in relation to their roles in device design, signal interrogation, and post-processing. Emerging applications, particularly ML-enabled wearable sensors, demonstrate significant potential for continuous and non-invasive health monitoring. Finally, we outline future prospects and challenges, emphasizing the need for algorithm transparency, standardization, and hardware integration to advance scalable and clinically relevant ML-driven optical sensing systems.