Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
Stanford researchers have developed a contrastive learning approach that can significantly reduce the amount of labeled electrocardiogram (ECG) data required for downstream healthcare tasks, such as arrhythmia identification.