For all the frantic effort to coordinate life-saving work around the globe during the COVID-19 pandemic, the digital age finds itself hampered in one very specific respect: information. Teams of artificial intelligence researchers are trying to bring decades of technology to bear on the problem of diagnosing and treating the disease, but the data they need to develop their software programs is scattered around the globe, making it practically inaccessible.
The painful lack of data is evident in one particular use case for AI, the development of diagnostic tests for COVID-19 based on X-rays or on "computed tomography" scans of the lungs.
While definitive tests for the disease are genetic tests, called "RT-PCR," those tests have been in notoriously short supply in many parts of the world including the U.S. An alternative is an X-ray or CT scan. X-rays in particular are widely available throughout the world, and the results come back much quicker than RT-PCR. There's a common belief that CT scans are more "sensitive" than RT-PCR, a potential advantage of using them.
Analyzing X-rays and CTs takes time, so numerous scholars around the world have put together so-called deep learning neural networks that can compute whether there are anomalies in the scans. The idea is to ease the burden of radiologists suddenly inundated with COVID-19 patients. Triaging the scans as a kind of first