Will AI save us from COVI D?
Early this spring as the pandemic began accelerating, A. J. Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford- trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies.
He was awed when the program reported back that the new virus appeared to have a snippet of DNA code — “RRARSAS” — distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance.
Venkatakrishnan, director of scientific research and partnerships at AI startup Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates? And perhaps even why people with cardiovascular disease were experiencing more- severe cases, since sodium can impact blood pressure?
“It was a surprise, completely accidental,” he recalled. “The machine just spotted that.”
Millions of gigabytes of data — the equivalent of a modest library — are being generated by the pandemic each day in medical records and other information on infected patients. Blood results. Age, race. Genetic testing. Interventions attempted. Outcomes. Now, nearly 10 months into the outbreak, scientists are starting to make connections in this jumble of letters and numbers with the help of artificial intelligence, leading to new theories about the virus and how to stop it.
While the human brain can process only so much information at a time, machines are whizzes at finding subtle patterns in huge amounts of data, and they are being deployed against COVID- 19 — the disease caused by the coronavirus — in ways only imagined in the past. Data scientists are aiming AI at some of the coronavirus’s biggest mysteries — why the disease looks so different in children versus adults, what makes some people “superspreaders” while others don’t transmit the virus at all — and other, lesser questions we have made little headway in understanding.
At Northwestern University, a modelling lab is running large- scale simulations on the effects of travel restrictions and social distancing on infection rates. The U. S. Energy Department’s Argonne National Laboratory is using AI to home in on the most promising molecules to test in the lab as possible treatments. In Egypt, AI is helping counter coronavirus misinformation in Arabic.
Jason Moore, director of the Penn Institute for Biomedical Informatics at the University of Pennsylvania, who is helping put together an international COVID-19 data consortium, said that if the virus had hit 20 years ago, the world might have been doomed.
“But I think we have a fighting chance today because of AI and machine learning,” he said.
In April, a computer sorting through medical records confirmed that a lack of smell and taste, which had been reported mostly anecdotally, was one of the earliest symptoms of infection — a discovery that influenced the Centers for Disease Control and Prevention to add anosmia to its list of symptoms. In June, a deep dive into the records of nearly 8,000 patients found that while only a small fraction had obvious and catastrophic blood clots, nearly all had worrisome changes in their blood coagulation.
Other researchers piggybacked on Venkatakrishnan’s finding of the aberrant genetic sequence to understand how the virus binds to cells, and to use that knowledge to develop drugs that aim to reduce transmission.
In a followup paper published in September, Venkatakrishnan and his colleagues reported that a computer analysis showed this “evolutionary tinkering” by coronavirus, which appears to have made it appear like a friend instead of a foe to the human immune system, mostly target the lungs and blood vessels — a finding that provides new insights about clinical symptoms seen by doctors at hospitals.
The early progress in AI has been promising, but critics worry that efforts to harness COVID-19 data have been disjointed and frustratingly slow. Others are concerned that analyses based on faulty or biased algorithms could exacerbate existing racial gaps and other disparities in health care.
One of the biggest challenges has been that much data remains siloed inside incompatible computer systems, hoarded by business interests and tangled in geopolitics. Academic researchers, medical societies and private companies have launched a number of efforts to try to overcome those barriers by creating their own giant databases of health records and other data — but the efforts are slow-going.
The largest—a US$ 20- million, four- year project by the National In
It was a surprise, completely accidental.
stitutes of Health led by scientist Bill Kapogiannis — is not expected to yield results until December at the earliest. But Kapogiannis said he is optimistic the pace of science will accelerate with computing power behind it.
“The human brain becomes pretty quickly overwhelmed by the permutations and combinations of these things,” he said. “But when you put AI into it, it can run countless simulations and can home in on important stuff very quickly and effectively.”
Yet with the stakes so enormous, Isaac Kohane, a Harvard bioinformatics researcher, said the world is not moving fast enough to tap into the power of electronic medical records and other data. He argues that “parochial interests have slowed our national response.”