A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Exosomes, nanoscale extracellular vesicles, emerge as critical biomarkers for disease diagnostics and therapeutic monitoring due to their role in intercellular communication and their cargo of proteins, lipids, and nucleic acids. Surface‐enhanced Raman spectroscopy (SERS) has gained significant attention as a highly sensitive and label‐free technique for exosome characterization, leveraging the enhancement of Raman signals by plasmonic metallic nanostructures. The application of SERS‐based exosome analysis enables rapid, non‐destructive detection of disease‐specific molecular signatures, offering a powerful tool for liquid biopsy applications. Despite its advantages, SERS faces challenges such as similar spectral profiles of biological samples, substrate reproducibility, and data complexity, which limit its clinical translation. Artificial intelligence (AI) integration has shown potential in addressing these limitations by enhancing spectral analysis, improving biomarker classification, and data interpretation. SERS spectra of exosomes from distinct disease states might be distinguished with great accuracy using AI‐driven methods, especially deep learning algorithms. Recent developments in SERS‐based exosome detection are examined in this review, along with the significance of AI‐driven data processing and potential possibilities for enhancing the reproducibility of SERS spectra and clinical suitability. With novel opportunities for early disease identification and real‐time monitoring of therapeutic responses, the combination of SERS, exosome analysis, and AI holds promise for noninvasive diagnostics.