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