Science is moving at warp speed to understand COVID-19, and artificial intelligence will be a valuable tool to make sense of it all — up to a point — Axios reported.
The coronavirus pandemic has led to an unprecedented wave of scientific publications, but without better tools to pick out meaningful research, it is easy to miss the science that matters — or be misled by findings that turn out to be wrong, author Bryan Walsh wrote for Axios.
According to the COVID-19 Primer, a public dashboard created by the machine-learning company Primer AI, researchers had published 27,569 papers about COVID-19 as of June 17, Walsh reported.
Of those, 21,000 went through the scientific peer review process, meaning experts in the field have examined the publications; 6,569 are so-called preprint papers — put out before the peer review process, he reported.
"The volume is so great that what's being published on COVID is equal to all the other research that is usually being put on infectious disease as a whole," Uri Blackman, CEO of Gideon Informatics, told Axios.
That is where machine-learning tools come in, Walsh reported.
Current AI cannot understand a scientific paper in the way a trained human being can, but can categorize and order it in a way that reveals useful patterns much quicker than humans, he wrote.
The COVID-19 Primer takes in news articles and social media interactions that reference papers and their authors, which allows a user to quickly see which papers are getting the most attention from experts and average people alike, Walsh noted.
But the COVID-19 Primer and similar tools cannot tell you the value of any individual paper, just how it is being received in the news and on social media, Walsh wrote.
"The most shared papers are the most controversial ones," according to Amy Heineike of Prime AI, Axios reported.
She told Wash the second-most-shared paper since the start of the outbreak was a preprint publication from the end of January arguing the novel coronavirus appeared to contain genes from the HIV virus, with the implication the virus was deliberately engineered.
The paper — which was not peer reviewed — was refuted by experts and withdrawn by its authors a few days after it was posted. But 20% of the tweets about it were posted in the days and weeks after its withdrawal, Walsh reported.
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