Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface-Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms

Abstract

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) has further elevated its capabilities, refining data interpretation, pattern prediction, and bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML and innovative nanostructures, substrates, and nanoprobes for SERS enhancement. The breakthroughs are highlighted in SERS-based point-of-care techniques and the nuanced detection of key biomarkers, from nucleic acids to proteins and metabolites. The article also addresses prevailing challenges, such as the need for standardized SERS methodologies and optimized platforms. Moreover, the potential of portable SERS systems is discussed for clinical deployment, as well as current efforts and challenges in clinical trials. In essence, this review positions the fusion of nanoengineering, AI, ML, and SERS as the frontier for next-generation biomedical diagnostics.