Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support. Biomedical Raman spectra are, however, high-dimensional, noisy, and affected by fluorescence background, acquisition variability, and biological heterogeneity, making robust computational analysis essential. This review examines the role of machine learning across the biomedical Raman spectroscopy pipeline, from preprocessing and signal correction to unsupervised structure discovery, supervised diagnosis and molecular stratification, representation and transfer learning, explainability, biomarker discovery, and multimodal integration with imaging, pathology, and molecular profiling. Emphasis is placed on the use of machine learning not only for diagnostic classification, but also for biologically interpretable and clinically actionable analysis. We also discuss the main barriers to clinical translation, including limited dataset sizes, inter-instrument variability, inconsistent preprocessing, insufficient external validation, reproducibility concerns, and limited sharing of software, data, and metadata. We argue that progress will require methodological advances together with standardization, robust validation, explainability, and deployment-ready analytical frameworks. By integrating methodological, biomedical, and translational perspectives, this review outlines key directions for developing reliable and clinically deployable Raman-AI systems.

翻译：拉曼光谱能够提供无标记、化学特异性的生物系统表征，已成为癌症诊断、分子分型、微生物鉴定及术中决策支持的重要工具。然而，生物医学拉曼光谱具有高维度、噪声大、受荧光背景、采集变异及生物异质性影响的特点，因此稳健的计算分析至关重要。本综述审视了机器学习在生物医学拉曼光谱全流程中的作用，涵盖从预处理与信号校正到无监督结构发现、监督诊断与分子分层、表征与迁移学习、可解释性、生物标志物发现，以及与影像、病理和分子谱分析的多模态融合。重点不仅在于机器学习用于诊断分类，更在于实现生物学可解释且临床可操作的分析。我们还讨论了临床转化的主要障碍，包括数据集规模有限、仪器间变异性、预处理不一致、外部验证不足、可重复性问题以及软件、数据和元数据共享有限。我们认为，进展需要方法论上的进步，同时结合标准化、稳健验证、可解释性以及可部署的分析框架。通过整合方法论、生物医学和转化视角，本综述概述了开发可靠且可临床部署的拉曼-人工智能系统的关键方向。