Perspective review highlights surface-enhanced Raman scattering for biomarker detection in early diagnostics, suggesting artificial intelligence integration enables clinical translation.
Biosensing plays a crucial role in precision biomarker detection, enabling real-time monitoring and physiological health analysis, particularly in early diagnosis. Traditional biosensing techniques, such as electrochemical, fluorescence, and colorimetric methods, have shown remarkable performance. However, their reliability is often susceptible to nonspecific interference, may produce false-positive results, and is typically limited to the detection of known targets, restricting their applicability in addressing ambiguous or unknown biological problems. Surface-enhanced Raman scattering (SERS) offers a powerful alternative, combining ultra-high sensitivity and molecular specificity. By supporting both label-free and label-based strategies, SERS broadens the detection scope, making it a promising platform for next-generation biosensing. In this Perspective, we aim at specific problems, introduce why SERS can do biosensing, how to advance SERS-based biosensing, and what SERS can do for early diagnosis. Finally, we discuss strategies to bridge the gap between laboratory research and clinical applications, emphasizing the important role of standardization and artificial intelligence in driving the practical transformation of SERS-based biosensing. Through these advancements, we aim to address existing limitations and further propel the evolution of SERS in biosensing and early diagnosis.
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Li et al. (2025) studied this question.
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