AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing
Seung-Ki Lee, Rowoon Park, Ho Sang Jung
Abstract
Surface‐enhanced Raman scattering (SERS) captures intrinsic vibrational signatures of biomolecules, allowing for label‐free detection with high specificity and sensitivity. This makes SERS particularly advantageous for analyzing biofluids, tissues, and cellular environments. However, spectral complexity, signal variability, and overlapping molecular signatures pose challenges in interpretation. To overcome these challenges, artificial intelligence (AI) enhances SERS analysis by automating spectral processing, improving classification, and refining imaging, enabling highly sensitive target detection, molecular interaction validation, and clinical diagnostics. This review explores AI‐enhanced SERS (AI‐SERS) applications in disease screening, biomolecular interaction analysis, and imaging‐based diagnostics, highlighting its potential for clinical translation. By leveraging AI‐driven spectral interpretation, AI‐SERS extracts molecular signatures, quantifies biomolecular interactions, and visualizes spatial distributions, providing diagnostic insights beyond conventional identification. Furthermore, we discuss key considerations for enhancing analytical reliability, optimizing spectral feature extraction, and integrating AI‐SERS into real‐world biomedical applications, outlining future directions to advance its role in medical diagnostics and therapeutic monitoring. By refining the synergy between AI and SERS, future advancements will drive innovations in next‐generation disease monitoring, drug discovery, and medical assistance.