Machine learning (ML)-assisted surface-enhanced raman spectroscopy (SERS) technologies for sustainable health

Surface-enhanced Raman spectroscopy (SERS) is a powerful and highly sensitive analytical tool that has found application in healthcare and environmental monitoring. Significant progress has been made in developing SERS-based sensing technology, enabling ultra-high sensitivity through its label-free and fingerprint-level detection capabilities. They are being utilized for molecular diagnostics, screening of clinical samples for food safety, and environmental toxic monitoring. In SERS techniques, vibrational spectra of complex chemical mixtures are acquired as large datasets are extracted from image analysis. Further, subtle variations of SERS signatures from thousands of clinical samples impose a major challenge in identifying analytes for accurate diagnosis. To address these issues, machine learning (ML) algorithms and multivariate statistical analysis have been combined with SERS for extracting and predicting the better outcome. Advancements in artificial intelligence (AI) and ML have shown promising potential to enhance the capabilities of SERS through rapid analysis and automated data processing. By leveraging AI/ML, SERS can transition from merely sensing to a more comprehensive sense, where the algorithms not only detect but also interpret complex patterns in the data. This review delves into the integration of ML with SERS, exploring how ML algorithms can improve these techniques by providing more accurate and insightful analyses. We discuss the overall process of merging ML with SERS, emphasize their applications in molecular diagnostics and screening, and offer insights into the future of ML-enhanced SERS sensor technologies, highlighting the transformative potential of AI/ML in moving from simple sensing to intelligent sensing.