https://www.sciencedaily.com/releases/2020/04/200409140015.htm

"The solution is to find an optimal tradeoff between doing something now, such as prescribing a drug off-label, or waiting until traditional clinical trials are complete," said Derek Angus, M.D., M.P.H., professor and chair, Department of Critical Care Medicine at Pitt and UPMC. "We've developed a way to do that with an adaptive clinical trial model that relies on a type of artificial intelligence known as reinforcement learning to identify the best, evidence-backed therapy for COVID-19 much faster than using the traditional scientific approach."

Before COVID-19 emerged, Angus and a wide range of international collaborators had developed a platform, called REMAP-Community Acquired Pneumonia (REMAP-CAP), designed to find optimal treatments for severe pneumonia both in non-pandemic and pandemic settings. When COVID-19 began circulating, REMAP-CAP was rapidly adapted, as per its intent, to incorporate additional treatment regimens specifically targeting the SARS-CoV-2 virus. The international team describes the REMAP-CAP platform in a manuscript published today in the Annals of the American Thoracic Society (AnnalsATS).