近年来，运用表面增强拉曼光谱（SERS）技术对生物样本检测的研究发展十分火热，尤其是结合机器学习方法的SERS技术在临床样本诊断方面的运用日趋成熟。机器学习方法是基于无监督和有监督的算法，可以解决复杂的样本和大量、高维的数据信息，在生物分析领域受到高度关注。本文介绍了SERS技术结合机器学习方法的相关应用，尤其是在生物医学领域。SERS既可以使用免标记的策略检测生物样本的指纹信息，又可以使用标记方法跟踪蛋白质等生物标志物实现间接检测。本文总结了SERS技术结合机器学习方法用于血液、尿液、组织等临床样本进行疾病诊断的最新进展。此外，本文还汇总了细胞样本及其他复杂样本SERS分析中机器学习方法运用的实例。该文综述了这一领域的最新进展，为从事SERS生物分析领域的研究人员提供可借鉴的方案。

In recent years, the use of surface-enhanced Raman spectroscopy (SERS) technology to detect biological samples has become a hot topic. Significantly, the application of SERS technology combined with machine learning methods in clinical sample diagnosis has become increasingly mature. Machine learning methods based on unsupervised and supervised algorithms to solve complex samples and large, high-dimensional data, have received high attention. This review describes the relevant applications of SERS technology combined with machine learning methods, especially in the biomedical field. SERS can detect fingerprint information of biological samples using label-free strategies, or indirect SERS detections for tracking biomarkers such as proteins. This review summarizes SERS technology combined with machine learning for disease diagnosis in clinical samples such as blood, urine, and biotissues. In addition, we also summarize their applications on many cellular samples and other complex samples. An overview of the latest advances in this field is provided and this study offers a reference that can be followed by researchers working in SERS bioanalysis.