Recent progresses of AI-assisted SERS in biomedicine

Surface-Enhanced Raman Spectroscopy (SERS) offers powerful molecular sensitivity, but conventional spectral analysis struggles with nonlinear relationships and high-dimensional datasets, limiting SERS adoption in biomedical applications. Artificial intelligence (AI), ranging from classical machine learning methods like support vector machines (SVM) to deep learning architectures such as convolutional neural networks (CNN), is well suited to extract discriminative features and model complex patterns in large-scale spectral data. This review surveys recent advances in AI-assisted SERS for biomedical use, covering spectral preprocessing, pathogen identification, pharmaceutical analysis, and biomarker screening in body fluids. We summarize algorithmic strategies, representative applications, and performance outcomes, and we highlight opportunities where AI enhances SERS sensitivity, robustness, and interpretability across laboratory and emerging clinical contexts. Finally, we critically examine current challenges, such as data scarcity, reproducibility, and clinical translation, and we propose actionable research directions focused on methodological refinement, multimodal integration, and pathways toward clinical implementation of AI-SERS systems. Together, these developments point to an emerging, interdisciplinary framework for deploying SERS in biomedical research and diagnostics.