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Machine learning methods offer great promise for fast and accurate detectionand prognostication of COVID-19 from standard-of-care chest radiographs (CXR)and computed tomography (CT) images. Many articles have been published in 2020describing new machine learning-based models for both of these tasks, but it isunclear which are of potential clinical utility. In this systematic review, wesearch EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv forpublished papers and preprints uploaded from January 1, 2020 to October 3, 2020which describe new machine learning models for the diagnosis or prognosis ofCOVID-19 from CXR or CT images. Our search identified 2,212 studies, of which415 were included after initial screening and, after quality screening, 61studies were included in this systematic review. Our review finds that none ofthe models identified are of potential clinical use due to methodological flawsand/or underlying biases. This is a major weakness, given the urgency withwhich validated COVID-19 models are needed. To address this, we give manyrecommendations which, if followed, will solve these issues and lead to higherquality model development and well documented manuscripts.

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans