AI-assisted Raman spectra technique for biomedical sensing: Combine new generation of discriminative spectral analysis with generative inverse design of SERS substrate

Surface-enhanced Raman scattering (SERS) combines molecular fingerprint specificity with high sensitivity and has been widely used in biomedical diagnostics, environmental monitoring, and chemical analysis. Because SERS enhancement depends on the geometry of noble metal nanostructures and the distribution of electromagnetic hotspots, small fabrication tolerances can cause large signal fluctuations that limit measurement repeatability. Discriminative machine learning methods have improved spectral interpretation but cannot directly guide substrate design. Deep generative models, together with physics-informed surrogate methods, now enable inverse design of nanostructures from target optical specifications, opening the prospect of a closed-loop between discriminative analysis and generative design for SERS substrate development. This review covers both discriminative spectral analysis and generative inverse design for SERS. We examine discriminative methods and their limitations, describe how explainable AI connects spectral analysis with structural design, and survey generative design strategies. Translational issues and future directions are also discussed.