T he scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations 1,2 . Healthcare providers, researchers and industry have pivoted their focus to address unmet and critical clinical needs created by the crisis, with remarkable results [3][4][5][6][7][8][9] . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit 10-12 . The data analytics and AI disciplines have always fostered open

Chest radiography has potential as a screening tool for revealing previously undiagnosed vertebral fractures, although in this study only half of moderate to severe fractures that we identified were mentioned in official reports.

Magnetic resonance imaging and maps of T1 and T2 values were used to study muscle composition in Duchenne muscular dystrophy (DMD). The mean T2 of anterior tibial muscle was 27 msec in healthy control subjects and 43 msec with increased fatty infiltration in DMD patients. In stronger DMD patients, the distribution of muscle T2 values was narrow, centered at 27 msec as in the controls, with a nonoverlapping fat peak centered at 49 msec. In weaker DMD patients, the width of the muscle T2 peak increased and the peak shifted toward the fat peak. Mean muscle T1 decreased from 1.7 to 0.6 second with increasing fatty infiltration. These results show that quantitative T1 and T2 maps may be used to assess muscle status and monitor DMD progression.

‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study 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. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.