<p dir="ltr">Advancements in machine learning are narrowing the gap in visual capabilities between machines and healthcare professionals, resulting in a transformation of the way we understand and address health challenges. Despite these advances, underlying limitations persist in addressing real-world problems, particularly in the precise capture of biological and physiological information. This is primarily because traditional trichromatic cameras fall short of representing reflectance spectra due to their limited spectral information. To overcome these limitations, hyperspectral imaging has emerged as a powerful tool for biomedical applications. By collecting a wealth of information at different wavelengths, hyperspectral imaging provides a comprehensive view of electromagnetic spectra, allowing non-invasive clinical analysis for accurate diagnostics. Snapshot hyperspectral imaging, in particular, is a competitive alternative to traditional cameras as it can capture a hyperspectral image in a single shot without the need for scanning individual wavelengths. Here, we introduce a computational snapshot hyperspectral imaging method, achieved through the integration of a machine learning approach with a streamlined optical system. We design an explainable machine learning algorithm by incorporating optical and biological knowledge into the algorithm. Therefore, the algorithm can reconstruct hyperspectral images with high spectralspatial resolution comparable to those of scientific spectrometers, despite the use of sparse information captured from the optical system. To demonstrate its versatility in biomedical applications, we extract hemodynamic parameters of peripheral microcirculation from embryonic model systems, tissue phantom samples, and human conjunctivas. Furthermore, we validate high accuracy of the results using conventional hyperspectral imaging and functional near-infrared spectroscopy. This learning-powered imaging method, characterized by high resolution and simplified hardware requirements, has the potential to offer solutions for various biomedical challenges by surpassing the constraints of conventional cameras and hyperspectral imaging.</p>