Yuhong Wen

TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.

TL;DR: This study investigates the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting and shows that despite substantial differences among the datasets from all sites and without centralizing data, it can successfully train AI models in federation.

TL;DR: This paper investigated the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting, and showed that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone.

TL;DR: In this paper, the authors used federated learning to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model.

TL;DR: The key design principles of FLARE are introduced and some use cases are illustrated with customizable FL workflows that implement different privacy-preserving algorithms.