How AI might be the most effective weapon to detect COVID-19

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How AI might be the most effective weapon to
detect COVID-19
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(No Transcript)
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The COVID-19 pandemic raises many problems in the
domain of Artificial Intelligence (AI). The major
challenges are often raised by way of so many
pertinent questions such as, can AI help to keep
an eye on the outbreak and predict the quantum of
spread? Can AI apply to assist with diagnosis and
prognosis as well? Could it be utilized in a
quest for proper treatment and subsequent hunt
for the exact vaccine? Will it be used to govern
the social factors? There are a plethora of
questions expected to be answered, and people are
anxious to know everything. This blog is an
effort to facilitate an honest analysis of how AI
has progressed in this respect so far, and to
note down limitations, weaknesses, and concerned
drawbacks. The relevant research is aptly
affected by the dearth of proper data and
increasing conflict between imperatives on public
health and data privacy issues.
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To Keep tabs on predicting the future
In theory, AI can be used for monitoring and
predicting how the COVID-19 disease would spread
its wings over time and space. However, in
December 2019, an AI-driven Health Map module at
the venue of Boston Childrens Hospital (USA)
triggered one of the first alarm bells that were
a warning given by a scientist at the site of
PMED (Program for Monitoring Emerging
Diseases). Even so, AI has not been very
effective up to now for any further tracking and
predictive analytics of how COVID-19 will spread.
That is because of a variety of reasons. The
first and foremost is that for AI to desperately
need data on COVID-19 for a proper drill down.
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To Keep tabs on predicting the future
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To Keep tabs on predicting the future
However, a variety of exciting projects have
commenced collecting and exchanging data, both
existing and fresh, to train new AI applications
and machine learning. Now, these projects entail
the global work on the Coronavirus Disease
database initiated by the World Health
Organization (WHO) in terms of giving access to
other related enterprises. There is also a second
opinion as to why AI has not been quite helpful
in monitoring and foreseeing disease spread thus
far. Not only is this a paucity of piling
historical training data, but it is also an
account of issues exploiting big data extracted
from social media. In the recent fiasco of Google
Flu Patterns, it highlighted the limitations of
AI and big data in the scope of infectious
diseases.
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To Keep tabs on predicting the future
Owing to a lack of evidence, chaotic social media
big data hubris, algorithmic dynamics, and
outlier data, AI predictions of the outbreak of
COVID-19 are still not very accurate or robust.
The majority of frameworks used for monitoring
and making predictions thus far do not use AI
technologies. Rather, a majority of forecast
modules prefer conventional epidemiological
patterns. To chase down the mushrooming of
COVID-19 in real-time, a true application for the
data creation dashboard has surfaced to visualize
the existence of the virus. The first, and most
customarily used, is that of the Center for
Systems Science and Engineering (CSSE) at Johns
Hopkins. The data created and made accessible
through this dashboard can be found in a GitHub
repository, so to say.
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Attempts for diagnosis and prognosis
Besides potentially monitoring and anticipating
the transmission of COVID-19, AI may also be
deftly used in disease diagnosis as well as its
prognosis. Actually, perhaps it is where most of
the AI initiatives have been given due
recognition. Rapid and precise COVID-19 diagnosis
will save innumerable human lives, restrict
disease contamination, and trigger data to train
AI applications. There is an increasing endeavour
to educate people through AI models to use chest
X-ray images to detect COVID-19.
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Attempts for diagnosis and prognosis
Even though it has been confirmed that a host of
Chinese health care facilities have deployed
AI-driven radiology techniques, the prospects of
AI are not yet converted into practice.
Radiologists elsewhere have voiced alarm that not
sufficient data can teach AI applications, that
many of the available COVID-19 samples come from
Chinese laboratories. Also, they may surely
suffer from self-selection, and that the use of
X-rays and CT scans may infect relevant equipment
and are further prone to the spread of the
disease. It appears that relatively less effort
has been made to use AI for diagnostic processes
at a primary stage. For example, determining
whether anyone is contaminated before it appears
in CT scans or X-rays in making data-driven
diagnoses with less chance of contamination.
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Vaccines and treatments
A third dimension where AI may theoretically
contribute to combating COVID-19 is the detection
of alternative therapies and vaccines. Years
before the COVID-19 outbreak, AI had been
suitably admired for its ability to contribute to
the development of the newer approach of
antidotes.
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Vaccines and treatments
In the scenario of COVID-19, plenty of data
centres and research laboratories have already
confirmed that they are trying to engage AI to
find an opening in terms of the COVID-19 vaccine.
The idea is that AI will speed up both the cycle
of developing new drugs and the savaging existing
drugs. A lot of scientists have claimed to have
found repurposing drugs, to say the least. Such
medications (especially a vaccine) are unlikely
to be available soon, and certainly to be of
great use during the ongoing pandemic situation.
The explanation is that it will take some time
for the scientific and medical tests, tracking,
and controls to be carried out before such
vaccines are released once they are detected and
tested, which might take as long a period as up
to 18 months.
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Social Management
One of the foremost objectives for AI lies in
social control and management in the campaign
against the COVID-19 outbreak. It is asserted
that AI application is imminent to manage the
alarming spread of pandemic via the
implementation of thermal imaging to scan the
open realm for conceivably infected people and by
supporting social distancing and lockdown
metrics.
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Social Management
Infrared cameras are taken into consideration at
airports and railway stations all over to scan
throngs of people for the lookout of high
temperatures. Often, they are shown with a system
of facial recognition that can identify the
person suffering from high temperature and
whether he or she is donning a surgical mask. So
even though using AI Information to identify and
treat COVID-19 is hindered by a lack of critical
historical databases, there are no AI resources
like robots and computer vision. Hence, in the
short term, people are more expected to see this
form of AI to be utilized for social control as
an extra effort.
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Conclusion
To summarize, AI can be a fundamental tool in
fighting COVID-19 and related pandemics. The
assumption is that issues related to public
health will outweigh data privacy issues. There
could be mission creep, with policymakers and
statutory bodies maintaining their exceptional
monitoring of common citizens long past the
pandemic is subsided. Concerns over data privacy
degradation are thus ably founded. AI-powered
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