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Just like your phone can detect what song is playing in crowded spaces, weshow that Artificial Intelligence transfer learning algorithms trained on coughphone recordings results in diagnostic tests for COVID-19. To gain adoption bythe health care community, we plan to validate our results in a clinical trialand three other venues in Mexico, Spain and the USA . However, if we had datafrom other on-going clinical trials and volunteers, we may do much more. Forexample, for confirmed stay-at-home COVID-19 patients, a longitudinal audiotest could be developed to determine contact-with-hospital recommendations, andfor the most critical COVID-19 patients a success ratio forecast test,including patient clinical data, to prioritize ICU allocation. As a challengeto the engineering community and in the context of our clinical trial, theauthors suggest distributing cough recordings daily, hoping other trials andcrowdsourcing users will contribute more data. Previous approaches to complexAI tasks have either used a static dataset or were private efforts led by largecorporations. All existing COVID-19 trials published also follow this paradigm.Instead, we suggest a novel open collective approach to large-scale real-timehealth care AI. We will be posting updates at https://opensigma.mit.edu. Ourpersonal view is that our approach is the right one for large scale pandemics,and therefore is here to stay - will you join?

Hi Sigma, do I have the Coronavirus?: Call for a New Artificial  Intelligence Approach to Support Health Care Professionals Dealing With The  COVID-19 Pandemic