Document Type

Article

Publication Date

2026

DOI

10.3390/nano16130785

Publication Title

Nanomaterials

Volume

16

Issue

13

Pages

785

Abstract

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. This review provides a comprehensive and critical synthesis of AI-assisted SERS platforms for cardiovascular diagnostics, integrating advances in plasmonic materials, biomolecular recognition, and intelligent spectral analysis within a unified framework. It further examines key translational barriers, including data variability, model interpretability, and scalability, and outlines future directions for developing standardized, edge-deployable, and clinically validated SERS-AI systems.

Comments

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data availability statement: "No new data were created or analyzed in this study. Data sharing is not applicable to this article."

Original Publication Citation

Joshi, A., & Slaughter, G. (2026). AI-assisted surface-enhanced raman spectroscopy for cardiovascular diagnostics: From plasmonic materials to clinical translation. Nanomaterials, 16(13), Article 785. https://doi.org/10.3390/nano16130785

ORCID

0000-0002-4307-091X (Slaughter)

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