Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for brain tumor patients is limited [1–3], complicating surgical and adjuvant treatment and obstructing clinical trial enrollment . Here, we developed DeepGlioma, a rapid (<90 seconds), AI-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method [5–7], and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of diffuse glioma patients (N = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion, ATRX mutation) , achieving a mean molecular classification accuracy of 93.3 (±1.6)%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular diagnosis of diffuse glioma patients.