Throughout
the
COVID-19
pandemic,
a
variety
of
digital
technologies
have
been
leveraged
for
public
health
surveillance
worldwide.
However,
concerns
remain
around
rapid
development
and
deployment
technologies,
how
these
used,
their
efficacy
in
supporting
goals.
Following
five-stage
scoping
review
framework,
we
conducted
peer-reviewed
grey
literature
to
identify
types
nature
used
during
pandemic
success
measures.
We
search
published
between
1
December
2019
31
2020
provide
snapshot
questions,
concerns,
discussions,
findings
emerging
at
this
pivotal
time.
A
total
147
79
publications
reporting
on
technology
use
across
90
countries
regions
were
retained
analysis.
The
most
frequently
included
mobile
phone
devices
applications,
location
tracking
drones,
temperature
scanning
wearable
devices.
utility
was
impacted
by
factors
including
uptake
targeted
populations,
technological
capacity
errors,
scope,
validity
accuracy
data,
guiding
legal
frameworks,
infrastructure
support
use.
Our
raise
important
questions
value
ensure
successful
while
mitigating
potential
harms
not
only
context
but
also
other
infectious
disease
outbreaks,
epidemics,
pandemics.
British Medical Bulletin,
Год журнала:
2021,
Номер
139(1), С. 4 - 15
Опубликована: Авг. 14, 2021
Artificial
intelligence
(AI)
and
machine
learning
(ML)
are
rapidly
evolving
fields
in
various
sectors,
including
healthcare.
This
article
reviews
AI's
present
applications
healthcare,
its
benefits,
limitations
future
scope.A
review
of
the
English
literature
was
conducted
with
search
terms
'AI'
or
'ML'
'deep
learning'
'healthcare'
'medicine'
using
PubMED
Google
Scholar
from
2000-2021.AI
could
transform
physician
workflow
patient
care
through
applications,
assisting
physicians
replacing
administrative
tasks
to
augmenting
medical
knowledge.From
challenges
training
ML
systems
unclear
accountability,
implementation
is
difficult
incremental
at
best.
Physicians
also
lack
understanding
what
AI
represent.AI
can
ultimately
prove
beneficial
but
requires
meticulous
governance
similar
conduct.Regulatory
guidelines
needed
on
how
safely
implement
assess
technology,
alongside
further
research
into
specific
capabilities
use.
Background:
Recently,
Coronavirus
Disease
2019
(COVID-19),
caused
by
severe
acute
respiratory
syndrome
virus
2
(SARS-CoV-2),
has
affected
more
than
200
countries
and
lead
to
enormous
losses.
This
study
systematically
reviews
the
application
of
Artificial
Intelligence
(AI)
techniques
in
COVID-19,
especially
for
diagnosis,
estimation
epidemic
trends,
prognosis,
exploration
effective
safe
drugs
vaccines;
discusses
potential
limitations.
Methods:
We
report
this
systematic
review
following
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
(PRISMA)
guidelines.
searched
PubMed,
Embase
Cochrane
Library
from
inception
19
September
2020
published
studies
AI
applications
COVID-19.
used
PROBAST
(prediction
model
risk
bias
assessment
tool)
assess
quality
literature
related
diagnosis
prognosis
registered
protocol
(PROSPERO
CRD42020211555).
Results:
included
78
studies:
46
articles
discussed
AI-assisted
COVID-19
with
total
accuracy
70.00
99.92%,
sensitivity
73.00
100.00%,
specificity
25
area
under
curve
0.732
1.000.
Fourteen
evaluated
based
on
clinical
characteristics
at
hospital
admission,
such
as
clinical,
laboratory
radiological
characteristics,
reaching
74.4
95.20%,
72.8
98.00%,
55
96.87%
AUC
0.66
0.997
predicting
critical
Nine
models
predict
peak,
infection
rate,
number
infected
cases,
transmission
laws,
development
trend.
Eight
explore
drugs,
primarily
through
drug
repurposing
development.
Finally,
1
article
predicted
vaccine
targets
that
have
develop
vaccines.
Conclusions:
In
review,
we
shown
achieved
high
performance
evaluation,
prediction
discovery
enhance
significantly
existing
medical
healthcare
system
efficiency
during
pandemic.
International Journal of Biological Sciences,
Год журнала:
2021,
Номер
17(6), С. 1581 - 1587
Опубликована: Янв. 1, 2021
Artificial
intelligence
(AI)
is
being
used
to
aid
in
various
aspects
of
the
COVID-19
crisis,
including
epidemiology,
molecular
research
and
drug
development,
medical
diagnosis
treatment,
socioeconomics.The
association
AI
can
accelerate
rapidly
diagnose
positive
patients.To
learn
dynamics
a
pandemic
with
relevance
AI,
we
search
literature
using
different
academic
databases
(PubMed,
PubMed
Central,
Scopus,
Google
Scholar)
preprint
servers
(bioRxiv,
medRxiv,
arXiv).In
present
review,
address
clinical
applications
machine
learning
deep
learning,
characteristics,
electronic
records,
images
(CT,
X-ray,
ultrasound
images,
etc.)
diagnosis.The
current
challenges
future
perspectives
provided
this
review
be
direct
an
ideal
deployment
technology
pandemic.
Artificial
intelligence
(AI)
is
expected
to
improve
healthcare
outcomes
by
facilitating
early
diagnosis,
reducing
the
medical
administrative
burden,
aiding
drug
development,
personalising
and
oncological
management,
monitoring
parameters
on
an
individual
basis,
allowing
clinicians
spend
more
time
with
their
patients.
In
post-pandemic
world
where
there
a
drive
for
efficient
delivery
of
manage
long
waiting
times
patients
access
care,
AI
has
important
role
in
supporting
systems
streamline
care
pathways
provide
timely
high-quality
Despite
technologies
being
used
some
decades,
all
theoretical
potential
AI,
uptake
been
uneven
slower
than
anticipated
remain
number
barriers,
both
overt
covert,
which
have
limited
its
incorporation.
This
literature
review
highlighted
barriers
six
key
areas:
ethical,
technological,
liability
regulatory,
workforce,
social,
patient
safety
barriers.
Defining
understanding
preventing
acceptance
implementation
setting
will
enable
clinical
staff
leaders
overcome
identified
hurdles
incorporate
benefit
staff.
Frontiers in Public Health,
Год журнала:
2023,
Номер
11
Опубликована: Окт. 26, 2023
Artificial
intelligence
(AI)
is
a
rapidly
evolving
tool
revolutionizing
many
aspects
of
healthcare.
AI
has
been
predominantly
employed
in
medicine
and
healthcare
administration.
However,
public
health,
the
widespread
employment
only
began
recently,
with
advent
COVID-19.
This
review
examines
advances
health
potential
challenges
that
lie
ahead.
Some
ways
aided
delivery
are
via
spatial
modeling,
risk
prediction,
misinformation
control,
surveillance,
disease
forecasting,
pandemic/epidemic
diagnosis.
implementation
not
universal
due
to
factors
including
limited
infrastructure,
lack
technical
understanding,
data
paucity,
ethical/privacy
issues.
Healthcare,
Год журнала:
2023,
Номер
11(2), С. 207 - 207
Опубликована: Янв. 10, 2023
People
in
the
life
sciences
who
work
with
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
are
under
increased
pressure
to
develop
algorithms
faster
than
ever.
The
possibility
of
revealing
innovative
insights
speeding
breakthroughs
lies
using
large
datasets
integrated
on
several
levels.
However,
even
if
there
is
more
data
at
our
disposal
ever,
only
a
meager
portion
being
filtered,
interpreted,
integrated,
analyzed.
subject
this
technology
study
how
computers
may
learn
from
imitate
human
mental
processes.
Both
an
increase
learning
capacity
provision
decision
support
system
size
that
redefining
future
healthcare
enabled
by
AI
ML.
This
article
offers
survey
uses
ML
industry,
particular
emphasis
clinical,
developmental,
administrative,
global
health
implementations
infrastructure
as
whole,
along
impact
expectations
each
component
healthcare.
Additionally,
possible
trends
scopes
utilization
medical
have
also
been
discussed.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 112891 - 112928
Опубликована: Янв. 1, 2023
Big
Data
Analytics
(BDA)
has
garnered
significant
attention
in
both
academia
and
industries,
particularly
sectors
such
as
healthcare,
owing
to
the
exponential
growth
of
data
advancements
technology.
The
integration
from
diverse
sources
utilization
advanced
analytical
techniques
potential
revolutionize
healthcare
by
improving
diagnostic
accuracy,
enabling
personalized
medicine,
enhancing
patient
outcomes.
In
this
paper,
we
aim
provide
a
comprehensive
literature
review
on
application
big
analytics
focusing
its
ecosystem,
applications,
sources.
To
achieve
this,
an
extensive
analysis
scientific
studies
published
between
2013
2023
was
conducted
overall
180
were
thoroughly
evaluated,
establishing
strong
foundation
for
future
research
identifying
collaboration
opportunities
domain.
study
delves
into
various
areas
BDA
highlights
successful
implementations,
explores
their
enhance
outcomes
while
reducing
costs.
Additionally,
it
outlines
challenges
limitations
associated
with
discusses
modelling
tools
techniques,
showcases
deployed
solutions,
presents
advantages
through
real-world
use
cases.
Furthermore,
identifies
key
open
field
aiming
push
boundaries
contribute
enhanced
decision-making
processes.
Applying
artificial
intelligence
(AI)
to
nursing
practice
has
dramatically
enhanced
healthcare
delivery
in
Arab
countries.
However,
AI
application
also
raises
complex
moral
issues,
including
patient
privacy,
data
security,
responsibility,
transparency,
and
equity
decision-making.
A
systematic
analysis
of
the
ethical
issues
surrounding
nations
is
carried
out
this
review,
highlighting
most
important
recommending
responsible
integration.
comprehensive
literature
search
was
conducted
across
major
databases.
Following
initial
identification
150
articles,
120
were
selected
for
full-text
review
based
on
title
abstract
screening.
Subsequently,
50
pertinent
studies
incorporated
into
review.
Numerous
significant
concerns
regarding
decision-making
processes
identified.
The
assessment
highlighted
possible
effects
nurse-patient
interaction
critical
role
played
by
ethics
committees
regulatory
frameworks
resolving
these
issues.
Ethical
must
be
established
guarantee
integration
practice,
safeguard
patients'
welfare,
strengthen
trust
between
providers
patients.
No
clinical
Trial.
