International Journal of Electronics and Communication Engineering,
Год журнала:
2024,
Номер
11(12), С. 107 - 122
Опубликована: Дек. 31, 2024
Artificial
Intelligence
(AI)
is
rapidly
transforming
sectors
such
as
healthcare,
education,
and
public
services,
contributing
new
solutions
that
advance
efficiency,
management,
overall
outcomes.
However,
despite
its
vast
potential,
AI
adoption
faces
numerous
challenges,
including
ethical
concerns
(e.g.,
algorithmic
bias),
data
privacy
issues,
integration
difficulties
with
legacy
systems.
This
paper
provides
a
comprehensive
survey
of
applications
across
these
sectors,
analyzing
over
60
recent
studies
from
2019
to
2024
after
the
PRISMA
methodology.
The
study
identifies
key
factors
influencing
successful
implementation
by
highlighting
sector-specific
challenges
shared
barriers.
framework
was
applied
for
systematic
selection,
inclusion
exclusion
criteria,
screening,
extraction,
ensuring
only
relevant,
high-quality
were
reviewed.
These
experimental
results
reveal
models
consistently
outperform
state-of-the-art
techniques
in
critical
domains,
medical
diagnosis,
personalised
service
optimisation.
hybrid
approach,
which
combines
Convolutional
Neural
Networks
(CNNs)
Recurrent
(RNNs),
outperforms
existing
addressing
preprocessing,
model
architecture,
hyperparameter
Additionally,
explores
future
up-and-coming
technologies
quantum
computing,
blockchain,
metaverse
while
providing
strategies
overcome
legal,
cultural,
infrastructural
barriers
adoption.
findings
offer
actionable
insights
researchers,
practitioners,
policymakers,
emphasising
need
both
technical
innovation
considerations
growth
execution.
Background
and
aim
Medical
students
are
expected
to
be
familiar
with
artificial
intelligence
(AI)
applications
in
healthcare.
This
cross-sectional
study
looked
at
the
attitudes,
thoughts,
understanding
of
healthcare
toward
AI.
Materials
methods
During
academic
year
2023-2024,
medical
enrolled
College
Medicine
Health
Sciences
(CMHS)
Arabian
Gulf
University
(AGU)
were
included
this
study.
A
questionnaire
was
developed
evaluate
their
opinions
regarding
use
AI
training.
These
data
gathered,
categorized,
analyzed
using
Statistical
Package
for
Social
(SPSS)
version
29
(IBM
Corp.,
Armonk,
NY,
US).
Categorical
variables
shown
form
frequencies
percentages,
whereas
continuous
presented
as
mean
standard
deviation
(SD).
Chi-square
tests
utilized
comparing
categorical
variables.
p-value
<0.05
considered
statistically
significant.
Results
The
found
that
n=41
(27%)
very
while
n=92
(60.5%)
somewhat
familiar.
Familiarity
increases
progress
education,
senior
clinical
phase
more
than
juniors.
There
no
significant
difference
perceptions
application
among
phases.
research
methodology
studies
familiarity
applications.
Most
believe
will
have
a
positive
impact
on
but
vary
by
educational
phase.
Many
support
integrating
into
curricula
67
(44.1%)
applications,
higher
percentage
pre-clinical
phases,
likely
due
projects
Concerns
raised
about
impacting
human
touch
practice
doctor-patient
communication,
well
technical
challenges
faced
when
applying
Conclusion
Arab
show
attitudes
education.
Tailored
strategies
needed
optimize
integration
address
concerns
effectively.
Advances in educational technologies and instructional design book series,
Год журнала:
2024,
Номер
unknown, С. 251 - 276
Опубликована: Окт. 24, 2024
In
order
to
provide
readers
with
an
overview
of,
and
summarize,
the
content
of
chapter,
purpose
research
discussed
is
stated
as
gain
understanding
technology
integration
in
inquiry-based
learning
(IBL)
classroom,
concerning
how
South
African
Grade
12
Life
Sciences
teachers
use
relevant
technologies
accommodate
their
learners'
needs,
difficulties,
cognitive
capabilities.
Against
background
power
persuasive
educational
enhancing
learning,
chapter
will
discuss
e.g.,
games
gamification,
higher
education,
early
childhood
education
Artificial
Intelligence
(AI).
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 63 - 90
Опубликована: Сен. 20, 2024
The
medical
profession
is
at
a
crossroads
with
technology
and
innovation
disrupting
traditional
care
delivery,
posing
challenges
for
training
future
current
workforces.
Traditional
memorization-based
education
may
become
impractical
due
to
information
overload,
exemplified
by
the
rapid
growth
of
research.
AI's
ability
democratize
knowledge
reshape
clinical
workflows
will
be
crucial
in
this
context.
AI-powered
platforms
have
improved
students'
assessment
scores
providing
real-time
feedback,
personalized
study
resources,
virtual
patient
simulations.
These
tools
enhance
reasoning,
diagnostic
skills,
practical
knowledge,
while
automated
assessments
offer
timely,
objective
evaluations.
Despite
these
benefits,
lack
understanding
AI
among
learners
educators,
disparities
access
tools,
hinder
its
full
potential.
This
chapter
explores
transformative
impact
on
education,
addressing
applications,
challenges,
integration.
International Journal of Electronics and Communication Engineering,
Год журнала:
2024,
Номер
11(12), С. 107 - 122
Опубликована: Дек. 31, 2024
Artificial
Intelligence
(AI)
is
rapidly
transforming
sectors
such
as
healthcare,
education,
and
public
services,
contributing
new
solutions
that
advance
efficiency,
management,
overall
outcomes.
However,
despite
its
vast
potential,
AI
adoption
faces
numerous
challenges,
including
ethical
concerns
(e.g.,
algorithmic
bias),
data
privacy
issues,
integration
difficulties
with
legacy
systems.
This
paper
provides
a
comprehensive
survey
of
applications
across
these
sectors,
analyzing
over
60
recent
studies
from
2019
to
2024
after
the
PRISMA
methodology.
The
study
identifies
key
factors
influencing
successful
implementation
by
highlighting
sector-specific
challenges
shared
barriers.
framework
was
applied
for
systematic
selection,
inclusion
exclusion
criteria,
screening,
extraction,
ensuring
only
relevant,
high-quality
were
reviewed.
These
experimental
results
reveal
models
consistently
outperform
state-of-the-art
techniques
in
critical
domains,
medical
diagnosis,
personalised
service
optimisation.
hybrid
approach,
which
combines
Convolutional
Neural
Networks
(CNNs)
Recurrent
(RNNs),
outperforms
existing
addressing
preprocessing,
model
architecture,
hyperparameter
Additionally,
explores
future
up-and-coming
technologies
quantum
computing,
blockchain,
metaverse
while
providing
strategies
overcome
legal,
cultural,
infrastructural
barriers
adoption.
findings
offer
actionable
insights
researchers,
practitioners,
policymakers,
emphasising
need
both
technical
innovation
considerations
growth
execution.
