Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 339; p. 126285
• Main Authors: Tas, Zehra, Ciftci, Fatih, Icoz, Kutay, Unal, Mustafa
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.10.2025
• Subjects: Animals; Artificial Intelligence; Biomedical applications; Deep learning; Humans; Machine learning; Microfluidic Analytical Techniques - methods; Microfluidic technologies; Microfluidics - methods; Spectrum Analysis, Raman - instrumentation; Spectrum Analysis, Raman - methods; Surface-Enhanced Raman Spectroscopy; Deep learning; Surface-Enhanced Raman Spectroscopy; Biomedical applications; Microfluidic technologies; Artificial intelligence; Machine learning
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2025.126285

[Display omitted] •SERS, AI, and microfluidics integration enables ultrasensitive, label-free diagnostics with minimal sample preparation.•Advances in nanomaterials, AI algorithms, and lab-on-chip designs enhance system performance.•Challenges remain in reproducibility, clinical validation, and system integration for real-world applications.•Future trends include multimodal sensing, sustainable materials, and embedded AI for real-time, point-of-care diagnostics. The integration of surface-enhanced Raman spectroscopy (SERS), artificial intelligence (AI), and microfluidics represent a transformative approach for biomedical applications. By combining the molecular sensitivity of SERS, AI-driven spectral analysis, and the precise sample handling of microfluidics, these novel integrated systems enable ultrasensitive, label-free diagnostics with minimal sample processing. The development of portable, cost-effective platforms could democratize advanced diagnostics for resource-limited settings. However, challenges such as reproducibility, clinical validation, and system integration hinder widespread adoption. This review explores these new integrated platforms, beginning with a discussion of SERS principles, their biomedical applications, and the critical roles of AI and microfluidics in enhancing analytical performance. We evaluate recent advances in the application of these integrated systems, while addressing key challenges such as substrate scalability, biocompatibility, and point-of-care translation, with a focus on nanomaterials, AI models, and lab-on-chip designs. Finally, we outline future directions, including multimodal sensing, sustainable materials, and embedded AI for real-time diagnostics, to bridge the gap between technological innovation and clinical implementation.