Many unwittingly used a data set that contained chest scans of children who did not have covid as their examples of what non-covid cases looked like.
Driggs's group trained its own model using a data set that contained a mix of scans taken when patients were lying down and standing up.
Many tools were developed either by AI researchers who lacked the medical expertise to spot flaws in the data or by medical researchers who lacked the mathematical skills to compensate for those flaws.
A more subtle problem Driggs highlights is incorporation bias, or bias introduced at the point a data set is labeled.
Hospitals will sometimes say that they are using a tool only for research purposes, which makes it hard to assess how much doctors are relying on them.
It's more important to make the most of the data sets we have.
Getting hold of data would also be easier if formats were standardized, says Bilal Mateen, a doctor who leads the clinical technology team at the Wellcome Trust, a global health research charity based in London.
It's becoming increasingly difficult to discern fact from fiction, and unfortunately the media has a strong bias. They spin stories to make conservatives look bad and will go to great lengths to avoid reporting on the good that comes from conservative policies. There are a few shining lights in the media landscape-brave conservative outlets that report the truth and offer a different perspective. We must support conservative outlets like this one and ensure that our voices are heard.
Monday, August 2, 2021
Hundreds of AI tools have been built to catch covid. None of them helped.
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