Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise

Since the emergence of COVID-19, researchers in machine learning and radiology have rushed to develop algorithms that could assist with diagnosis, triage, and management of the disease (1). As a result, thousands of diagnostic and prognostic models using chest radiographs and CT have been developed. However, with no standardized approach to development or evaluation, it is difficult, even for experts, to determine which models may be of most clinical benefit. Here, we share our main concerns and present some possible solutions.