  Diagnosis of COVID-19 at point of care is vital to the containment of the
global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of
lungs to detect COVID-19 in patients in a repeatable and cost effective way.
Previous work has used public datasets of POCUS videos to train an AI model for
diagnosis that obtains high sensitivity. Due to the high stakes application we
propose the use of robust and explainable techniques. We demonstrate
experimentally that robust models have more stable predictions and offer
improved interpretability. A framework of contrastive explanations based on
adversarial perturbations is used to explain model predictions that aligns with
human visual perception.
