Tas Zehra, Ciftci Fatih, Icoz Kutay, Unal Mustafa
Karaman Provincial Health Directorate, Karaman, 70100, Türkiye.
Department of Biomedical Engineering, Faculty of Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34445, Türkiye; BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Türkiye; Department of Technology Transfer Office, Fatih Sultan Mehmet Vakıf University, Istanbul, 34445, Türkiye.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Oct 15;339:126285. doi: 10.1016/j.saa.2025.126285. Epub 2025 Apr 23.
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.
表面增强拉曼光谱（SERS）、人工智能（AI）和微流控技术的整合代表了一种用于生物医学应用的变革性方法。通过结合SERS的分子敏感性、人工智能驱动的光谱分析以及微流控技术精确的样品处理，这些新型集成系统能够以最少的样品处理实现超灵敏、无标记诊断。便携式、经济高效平台的开发可为资源有限的环境普及先进诊断技术。然而，诸如重现性、临床验证和系统集成等挑战阻碍了其广泛应用。本综述探讨了这些新型集成平台，首先讨论了SERS原理、其生物医学应用以及人工智能和微流控技术在提高分析性能方面的关键作用。我们评估了这些集成系统应用的最新进展，同时解决诸如基底可扩展性、生物相容性和即时检测转化等关键挑战，重点关注纳米材料、人工智能模型和芯片实验室设计。最后，我们概述了未来的发展方向，包括多模态传感、可持续材料以及用于实时诊断的嵌入式人工智能，以弥合技术创新与临床应用之间的差距。
