Raman spectroscopy applied to clinical samples—including human tissues, biofluids, and extracellular vesicles—has demonstrated increasing potential for diagnostic and prognostic applications. However, the reuse and large-scale integration of Raman datasets across studies and institutions remain limited by heterogeneous data structures, incomplete metadata, and poor linkage between spectroscopic measurements and clinical context. This fragmentation represents a major bottleneck for the development of robust, transferable artificial intelligence (AI) models for clinical Raman data analysis [1]. In this work, we propose a FAIR-ification framework for clinical Raman spectroscopy data built on the ISA (Investigation–Study–Assay) model as a unifying organizational structure. Within this framework, the Study level is described using MIABIS (Minimum Information About BIobank data Sharing), enabling standardized, and privacy aware, description of clinical sample, donor-related metadata, and sample processing [2]. The Assay level is implemented using NXRaman, a domain-specific extension of the NeXus data format that provides a machine-readable reporting of spectroscopic data, acquisition parameters, and instrument metadata [3]. This structured separation between clinical context and spectroscopic measurements enables interoperable data aggregation and supports the training, validation, and reuse of AI models across heterogeneous datasets. The framework defines core and optional metadata elements designed to preserve data quality while remaining compatible with real clinical workflows. Two representative test cases—fresh breast tissue analysis and dried blood plasma samples—are presented to demonstrate applicability across different clinical scenarios. This approach represents the first step towards the transition from the current situation dominated by isolated clinical Raman datasets to AI-ready, reusable spectroscopic data resources for biomedical research.