Medical hyperspectral imaging (MHSI) is an emerging imaging modality with potential for noninvasive disease diagnosis and surgical guidance. It acquires a three-dimensional hypercube dataset with two spatial and one spectral dimension, providing diagnostic information about tissue physiology, morphology, and composition. This review summarizes the literature on MHSI technology, hardware, software, and applications from 1988 to 2013. MHSI systems can operate in reflectance, fluorescence, and transmission modes across UV, VIS, and NIR regions. Key components include light sources, dispersive devices, and detector arrays. Common dispersive devices include prisms, gratings, and tunable filters. Detector arrays such as CCDs, InGaAs, and HgCdTe are used for spectral detection. MHSI systems are often combined with other techniques like microscopes and Raman scattering to enhance diagnostic capabilities. Image analysis involves preprocessing, feature extraction, and classification. Preprocessing includes normalization and registration. Feature extraction reduces dimensionality and extracts relevant spectral information. Classification methods include SVMs, neural networks, and spectral angle mapping. These methods help distinguish between healthy and malignant tissues and identify biomarkers. MHSI has shown promise in cancer detection, tissue diagnosis, and surgical guidance. Challenges include high data redundancy, variability in spectral signatures, and the need for efficient analysis algorithms. Future developments aim to improve accuracy, speed, and integration with clinical workflows.Medical hyperspectral imaging (MHSI) is an emerging imaging modality with potential for noninvasive disease diagnosis and surgical guidance. It acquires a three-dimensional hypercube dataset with two spatial and one spectral dimension, providing diagnostic information about tissue physiology, morphology, and composition. This review summarizes the literature on MHSI technology, hardware, software, and applications from 1988 to 2013. MHSI systems can operate in reflectance, fluorescence, and transmission modes across UV, VIS, and NIR regions. Key components include light sources, dispersive devices, and detector arrays. Common dispersive devices include prisms, gratings, and tunable filters. Detector arrays such as CCDs, InGaAs, and HgCdTe are used for spectral detection. MHSI systems are often combined with other techniques like microscopes and Raman scattering to enhance diagnostic capabilities. Image analysis involves preprocessing, feature extraction, and classification. Preprocessing includes normalization and registration. Feature extraction reduces dimensionality and extracts relevant spectral information. Classification methods include SVMs, neural networks, and spectral angle mapping. These methods help distinguish between healthy and malignant tissues and identify biomarkers. MHSI has shown promise in cancer detection, tissue diagnosis, and surgical guidance. Challenges include high data redundancy, variability in spectral signatures, and the need for efficient analysis algorithms. Future developments aim to improve accuracy, speed, and integration with clinical workflows.