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Companies are employing AI to respond to the Covid-19 pandemic
in a number of different ways, including diagnosing Covid-19 cases,
identifying which patients would be at highest risk and discovering
potential treatments. But not all of these approaches have been
validated, experts warned.
In a rush to find solutions for the Covid-19 pandemic,
researchers are deploying machine learning algorithms to trawl
through data that might give us more clues about the virus. Some
claim to have identified potential treatments based on the data,
while others are using it to screen patients or identify those at
highest risk.
But, like their vaccine and drug counterparts, many of these
algorithms are still unproven. With hundreds of research articles
describing the use of artificial intelligence or machine learning
— many of them preprints — it can be difficult to sort
out which ones are most effective.
“I’ve heard a lot of hype about machine learning being
applied to battling Covid-19, but I haven’t seen very many
concrete examples where you could imagine in the short- or
medium-term something that is going to have a substantial
effect,†said John Quackenbush, chair of the Department of
Biostatistics at the Harvard T.H. Chan School of Public Health, in
a phone interview.
Any good model requires good data, and that can be a challenge
to find in healthcare. Given that Covid-19 is a new disease, that
limits the amount of information researchers have. On top of that,
most clinical data is locked up in health record systems, which
often have different ways of recording it.
“Everything that we’re doing gets better with a lot
more well-annotated datasets,†said Dr. Eric Topol, director
of the Scripps Research Translational Institute, who published a
book on AI in healthcare. “In the U.S., we don’t have
centralized data. Here we are at the epicenter and all of our
healthcare data is fragmented.â€
On the other hand, as datasets get larger, they become
“noisier.†For example, a model that screens Covid-19
patients for temperature might be reasonably effective. But
expanded to the general population, “it’s a terrible
predictor,†Quackenbush said.
Still, both were cautiously optimistic about using AI in some
settings, such as determining which patients face a higher risk
from Covid-19, opening an opportunity for communication with their
physician.
Searching for a
treatment
In early April, drugmaker Eli Lillyannounced
it would launch a trial of its existing rheumatoid arthritis
treatment, baricitinib, in severely ill Covid-19 patients.
The drug was identified by a British startup, BenevolentAI,
which used
natural language processing to skim through millions of
papers and create a database of biological processes related to
the novel coronavirus. From there, they
identified baricitinib as a potential treatment because of two
key characteristics: its anti-inflammatory properties might help
temper the body’s hyperactive immune response to the virus,
and it seemed like the drug might be able to prevent viral
infection.
As a counterpoint, a group of rheumatologists that had treated
patients in Lombardy, Italy, cautioned about potential adverse
effects from the drug. Its FDA black box warning indicates patients
taking the drug may face an increased risk of developing serious
infections,
Originally published by
Elise Reuter
| May 28, 2020
MedCityNews