AI‐Assisted SERS in Bacterial Diagnosis and Antimicrobial Susceptibility Testing: Advances, Gaps, and Translational Pathways

The escalating global crisis of antimicrobial resistance (AMR) demands rapid, accurate, and accessible methods for bacterial identification and antimicrobial susceptibility testing (AST). Surface‐enhanced Raman spectroscopy (SERS) offers high sensitivity, molecular fingerprinting capability, and non‐destructive detection, positioning it as a promising tool for microbiological diagnostics. The integration of artificial intelligence (AI), particularly machine learning and deep learning, enables automated extraction of latent features from complex SERS spectra, transforming raw data into actionable diagnostic insights. This review surveys recent advances in SERS substrate engineering, AI‐driven bacterial identification and metabolite‐based AST strategies, and the emergence of interpretable AI frameworks that bridge spectral patterns with biochemical mechanisms. We further examine the challenges impeding clinical translation, including substrate batch variability, cross‐device spectral inconsistency, and the lack of orthogonal biochemical validation for model interpretability, and outline a pragmatic roadmap toward standardized, point‐of‐care deployment of AI‐SERS in bacterial diagnostics.