The effect of machine learning explanations on user trust for automated diagnosis of COVID-19.

Recent years have seen deep neural networks (DNN) gain widespread acceptance for a range of computer vision tasks that include medical imaging. Motivated by their performance, multiple studies have focused on designing deep convolutional neural network architectures tailored to detect COVID-19 cases from chest computerized tomography (CT) images. However, a fundamental challenge of DNN models is their inability to explain the reasoning for a diagnosis.

A low-cost solution for converting existing stethoscope into tele-stethoscope in resource-constrained setting for COVID-19 pandemic.

In the present scenario of COVID-19, there has been a sudden surge in demand for tele- consultancy. Such measures require resources to support the system. Replacement of conventional equipment used in a clinic with digital equipment is not feasible due to their cost and scalability issues.