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Abstract

Background and aims Healthcare delivery requires the support of new technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Machine Learning to fight and look ahead against the new diseases. We aim to review the role of AI as a decisive technology to analyze, prepare us for prevention and fight with COVID-19 (Coronavirus) and other pandemics. Methods The rapid review of the literature is done on the database of Pubmed, Scopus and Google Scholar using the keyword of COVID-19 or Coronavirus and Artificial Intelligence or AI. Collected the latest information regarding AI for COVID-19, then analyzed the same to identify its possible application for this disease. Results We have identified seven significant applications of AI for COVID-19 pandemic. This technology plays an important role to detect the cluster of cases and to predict where this virus will affect in future by collecting and analyzing all previous data. Conclusions Healthcare organizations are in an urgent need for decision-making technologies to handle this virus and help them in getting proper suggestions in real-time to avoid its spread. AI works in a proficient way to mimic like human intelligence. It may also play a vital role in understanding and suggesting the development of a vaccine for COVID-19. This result-driven technology is used for proper screening, analyzing, prediction and tracking of current patients and likely future patients. The significant applications are applied to tracks data of confirmed, recovered and death cases.

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Arti?cial Intelligence (AI) applications for COVID-19 pandemic
Raju Vaishya
a
, Mohd Javaid
b
,
*
, Ibrahim Haleem Khan
c
, Abid Haleem
b
a
Department of Orthopaedics, Indraprastha Apollo Hospital, SaritaVihar, Mathura Road, 110076, New Delhi, India
b
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
c
Jamia Hamdard, New Delhi, India
article info
Article history:
Received 6 April 2020
Received in revised form
10 April 2020
Accepted 10 April 2020
Keywords:
Arti?cial Intelligence (AI)
AI Applications
COVID-19
Coronavirus
Pandemic
abstract
Background and aims: Healthcare delivery requires the support of new technologies like Arti?cial In-
telligence (AI), Internet of Things (IoT), Big Data and Machine Learning to ?ght and look ahead against the
new diseases. We aim to review the role of AI as a decisive technology to analyze, prepare us for pre-
vention and ?ght with COVID-19 (Coronavirus) and other pandemics.
Methods: The rapid review of the literature is done on the database of Pubmed, Scopus and Google
Scholar using the keyword of COVID-19 or Coronavirus and Arti?cial Intelligence or AI. Collected the
latest information regarding AI for COVID-19, then analyzed the same to identify its possible application
for this disease.
Results: We have identi?ed seven signi?cant applications of AI for COVID-19 pandemic. This technology
plays an important role to detect the cluster of cases and to predict where this virus will affect in future
by collecting and analyzing all previous data.
Conclusions: Healthcare organizations are in an urgent need for decision-making technologies to handle
this virus and help them in getting proper suggestions in real-time to avoid its spread. AI works in a
pro?cient way to mimic like human intelligence. It may also play a vital role in understanding and
suggesting the development of a vaccine for COVID-19. This result-driven technology is used for proper
screening, analyzing, prediction and tracking of current patients and likely future patients. The signi?-
cant applications are applied to tracks data of con?rmed, recovered and death cases.
?2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Background
In this worldwide health crisis, the medical industry is looking
for new technologies to monitor and controls the spread of COVID-
19 (Coronavirus) pandemic. AI is one of such technology which can
easily track the spread of this virus, identi?es the high-risk patients,
and is useful in controlling this infection in real-time. It can also
predict mortality risk by adequately analyzing the previous data of
the patients. AI can help us to ?ght this virus by population
screening, medical help, noti?cation, and suggestions about the
infection control [1e3]. This technology has the potential to
improve the planning, treatment and reported outcomes of the
COVID-19 patient, being an evidence-based medical tool. Fig. 1
shows the general procedure of AI and non-AI based applications
that help general physicians to identify the COVID-19 symptoms.
The above ?ow diagram informs and compares the ?ow of
minimal non-AI treatment versus AI-based treatment. The above
?ow diagram explains the involvement of AI in the signi?cant steps
of treatment of high accuracy and reduces complexity and time
taken. The physician is not only focused on the treatment of the
patient, but also the control of disease with the AI application.
Major symptoms and test analysisare done with the help of AI with
the highest of accuracy. It also shows it reduces the total number of
steps taken in the whole process, making more procurable in
nature.
2. Main applications of AI in COVID-19 pandemic
I) Early detection and diagnosis of the infection
AI can quickly analyze irregular symptom and other red ?ags
*Corresponding author.
E-mail addresses: raju.vaishya@gmail.com (R. Vaishya), mjavaid@jmi.ac.in
(M. Javaid), ibrahimhaleemkhan.ihk@gmail.com (I.H. Khan), haleem.abid@gmail.
com (A. Haleem).
https://scholar.google.co.in/citations?user?2Lu3gQ0AAAAJ&hl?en (R. Vaishya),
https://scholar.google.co.in/citations?user?rfyiwvsAAAAJ&hl?en (M. Javaid),
https://scholar.google.co.in/citations?user?4047148AAAAJ&hl?en (A. Haleem)
Contents lists available at ScienceDirect
Diabetes &Metabolic Syndrome: Clinical Research &Reviews
journal 顺心彩票page: www.elsevier.com/locate/dsx
https://doi.org/10.1016/j.dsx.2020.04.012
1871-4021/?2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 337e339
and thus alarm the patients and the healthcare authorities [4,5]. It
helps to provide faster decision making, which is cost-effective. It
helps to develop a new diagnosis and management system for the
COVID 19 cases, through useful algorithms. AI is helpful in the
diagnosis of the infected cases with the help of medical imaging
technologies like Computed tomography (CT), Magnetic resonance
imaging (MRI) scan of human body parts.
II) Monitoring the treatment
AI can build an intelligent platform for automatic monitoring
and prediction of the spread of this virus. A neural network can also
be developed to extract the visual features of this disease, and this
would help in proper monitoring and treatment of the affected
individuals [6e8]. It has the capability of providing day-to-day
updates of the patients and also to provide solutions to be fol-
lowed in COVID-19 pandemic.
III) Contact tracing of the individuals
AI can help analyze the level of infection by this virus identifying
the clusters and hot spotsand can successfully do the contact
Fig. 1. General procedure of AI and non-AI based applications that help general physicians to identify the COVID-19 symptoms.
R. Vaishya et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 337e339338
tracing of the individuals and also to monitor them. It can predict
the future course of this disease and likely reappearance.
IV) Projection of cases and mortality
This technology can track and forecast the nature of the virus
from the available data, social media and media platforms, about
the risks of the infection and its likely spread. Further, it can predict
the number of positive cases and death in any region. AI can help
identify the most vulnerable regions, people and countries and take
measures accordingly.
V) Development of drugs and vaccines:
AI is used for drug research by analyzing the available data on
COVID-19. It is useful for drug delivery design and development.
This technology is used in speeding up drug testing in real-time,
where standard testing takes plenty of time and hence helps to
accelerate this process signi?cantly, which may not be possible by a
human [6,7]. It can help to identify useful drugs for the treatment of
COVID-19 patients. It has become a powerful tool for diagnostic test
designs and vaccination development [9e11]. AI helps in devel-
oping vaccines and treatments at much of faster rate than usual and
is also helpful for clinical trials during the development of the
vaccine.
VI) Reducing the workload of healthcare workers
Due to a sudden and massive increase in the numbers of pa-
tients during COVID-19 pandemic, healthcare professionals have a
very high workload. Here, AI is used to reduce the workload of
healthcare workers [12e17]. It helps in early diagnosis and
providing treatment at an early stage using digital approaches and
decision science, offers the best training to students and doctors
regarding this new disease [18,19]. AI can impact future patient care
and address more potential challenges which reduce the workload
of the doctors.
VII) Prevention of the disease
With the help of real-time data analysis, AI can provide updated
information which is helpful in the prevention of this disease. It can
be used to predict the probable sites of infection, the in?ux of the
virus, need for beds and healthcare professionals during this crisis.
AI is helpful for the future virus and diseases prevention, with the
help of previous mentored data over data prevalent at different
time. It identi?es traits, causes and reasons for the spread of
infection. In future, this will become an important technology to
?ght against the other epidemics and pandemics. It can provide a
preventive measure and ?ght against many other diseases. In
future, AI will play a vital role in providing more predictive and
preventive healthcare.
3. Conclusion
Arti?cial Intelligence is an upcoming and useful tool to identify
early infections due to coronavirus and also helps in monitoring the
condition of the infected patients. It can signi?cantly improve
treatment consistency and decision making by developing useful
algorithms. AI is not only helpful in the treatment of COVID-19
infected patients but also for their proper health monitoring. It
can track the crisis of COVID-19 at different scales such as medical,
molecular and epidemiological applications. It is also helpful to
facilitate the research on this virus using analyzing the available
data. AI can help in developing proper treatment regimens, pre-
vention strategies, drug and vaccine development.
Declaration of competing interest
None.
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Background Despite its high sensitivity in diagnosing COVID-19 in a screening population, chest CT appearances of COVID 19 pneumonia are thought to be non-specific. Purpose To assess the performance of United States (U.S.) and Chinese radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Methods A total of 219 patients with both positive COVID-19 by RT-PCR and abnormal chest CT findings were retrospectively identified from 7 Chinese hospitals in Hunan Providence, China from January 6 to February 20, 2020. A total of 205 patients with positive Respiratory Pathogen Panel for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia by original radiology interpretation within 7 days of each other were identified from Rhode Island Hospital in Providence, RI. Three Chinese radiologists blindly reviewed all chest CTs (n=424) to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched cases was randomly selected and evaluated by 4 U.S. radiologists in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CTs, three Chinese radiologists correctly differentiated COVID-19 from non-COVID-19 pneumonia 83% (350/424), 80% (338/424), and 60% (255/424) of the time, respectively. The seven radiologists had sensitivities of 80%, 67%, 97%, 93%, 83%, 73% and 70% and specificities of 100%, 93%, 7%, 100%, 93%, 93%, 100%. Compared to non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs. 57%, p<0.001), ground-glass opacity (91% vs. 68%, p<0.001), fine reticular opacity (56% vs. 22%, p<0.001), and vascular thickening (59% vs. 22%, p<0.001), but less likely to have a central+peripheral distribution (14.% vs. 35%, p<0.001), pleural effusion (4.1 vs. 39%, p<0.001) and lymphadenopathy (2.7% vs. 10.2%, p<0.001). Conclusion Radiologists in China and the United States distinguished COVID-19 from viral pneumonia on chest CT with high specificity but moderate sensitivity. A translation of this abstract in Farsi is available in the supplement. - ????? ????? ??? ????? ?? ?????? ?? ????? ????? ???.
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