Research progress, translation-related challenges, and clinical prospects of surface-enhanced Raman spectroscopy in respiratory virus detection: a narrative review.

Background and Objective: The diagnosis of respiratory viral infections has traditionally relied on nucleic acid testing, antigen assays, and viral culture; however, these approaches may be limited by turnaround time, sensitivity, operational complexity, or multiplexing capacity in certain clinical settings. Research interest in surface-enhanced Raman spectroscopy (SERS) as a rapid diagnostic strategy has intensified owing to its high sensitivity, molecular specificity, label-free detection capability, and multiplex analytical potential. This review provides a synthesis of the recently generated evidence related to SERS-based respiratory virus detection and discusses the translation-related challenges and clinical prospects. Methods: A systematic literature search for relevant studies published from January 1, 2005, to April 1, 2026, was conducted in the PubMed, Web of Science, and ScienceDirect databases. The search focused on the application of SERS for respiratory virus detection, specifically label-based and label-free strategies, microfluidics, and integration with machine learning and polymerase chain reaction. Key Content and Findings: Initial studies in the field established the feasibility of SERS in identifying respiratory viruses and distinguishing pathogen-specific molecular signatures. More recent work has expanded applications to encompass label-free detection, immunoassay-based platforms, nucleic acid amplification-coupled SERS, and integrated microfluidic or portable systems. These advances have improved analytical sensitivity, enabled multiplex pathogen discrimination, and broadened potential use across scenarios such as community screening, emergency triage, surveillance, and confirmatory testing. Combinations with deep learning and automated platforms, which may further enhance diagnostic performance and clinical adaptability, have also emerged. However, major barriers remain, including insufficient signal reproducibility, poor substrate uniformity, lack of standardized methodologies, and limited large-scale clinical validation. Conclusions: SERS has demonstrated considerable promise as a complementary diagnostic modality for respiratory virus detection and is progressing from proof-of-concept validation to the assessment of clinical feasibility. The future translation of this technology will depend on reproducible substrates, standardized workflows, robust algorithms, and rigorous clinical validation.