Debate flares over using AI to detect Covid-19 in lung scans
A series of studies, starting as a steady drip and quickening to a deluge, has reported the same core finding amid the global spread of Covid-19: Artificial intelligence could analyze chest images to accurately detect the disease in legions of untested patients.
The results promised a ready solution to the shortage of diagnostic testing in the U.S. and some other countries and triggered splashy press releases and a cascade of hopeful headlines. But in recent days, the initial burst of optimism has given way to an intensifying debate over the plausibility of building AI systems during an unprecedented public health emergency.
On one side are AI developers and researchers who argue that training and testing methods can, and should, be modified to fit the contours of the crisis; on the other are skeptics who point to flaws in study designs and the limited number of lung scans available from coronavirus patients to train AI algorithms. They also argue that imaging should be used sparingly during the pandemic because of the risk of spreading the infection through contaminated equipment. For those and other reasons, many radiological societies have recommended against using such scans, whether supported by AI or not, for Covid-19 screening.
As AI makes increasing inroads into health care
