Frontiers in Medicine,
Journal Year:
2024,
Volume and Issue:
11
Published: Nov. 29, 2024
Facing
unprecedented
challenges
due
to
global
population
aging
and
the
prevalence
of
chronic
diseases,
healthcare
sector
is
increasingly
relying
on
innovative
solutions.
Internet
Things
(IoT)
technology,
by
integrating
sensing,
network
communication,
data
processing,
security
technologies,
offers
promising
approaches
address
issues
such
as
nursing
personnel
shortages
rising
costs.
This
paper
reviews
current
state
IoT
applications
in
healthcare,
including
key
frameworks
for
smart
platforms,
case
studies.
Findings
indicate
that
significantly
enhances
efficiency
quality
care,
particularly
real-time
health
monitoring,
disease
management,
remote
patient
supervision.
However,
related
quality,
user
acceptance,
economic
viability
also
arise.
Future
trends
development
will
likely
focus
increased
intelligence,
precision,
personalization,
while
international
cooperation
policy
support
are
critical
adoption
healthcare.
review
provides
valuable
insights
policymakers,
researchers,
practitioners
suggests
directions
future
research
technological
advancements.
International Journal of Computational and Experimental Science and Engineering,
Journal Year:
2025,
Volume and Issue:
11(2)
Published: April 9, 2025
The
Mogao
Grottoes
murals
have
deteriorated
over
centuries
due
to
environmental
exposure,
pigment
degradation,
and
natural
ageing,
making
cultural
heritage
preservation
difficult.
AI
computer
vision
can
identify,
classify,
reconstruct
faded
pigments,
revolutionizing
color
restoration.
This
reconstructs
mural
sections
using
deep
learning,
image
processing,
data
implemented
through
TensorFlow,
PyTorch
OpenCV.
study
uses
high-resolution
Digital
Dunhuang
database
images
of
50
pigments
categorized
by
color,
stability,
chemical
composition.
CNNs
learning-based
mapping
algorithms
detect
fading
suggest
restorations
pigments.
reconstructions
along
with
history
accuracy
expert
evaluations
records.
Artificial
intelligence-driven
conservation
detects
precisely
missing
sections,
matches
restored
colors
historical
authenticity,
improving
accuracy,
efficiency,
scalability.
Scientifically,
AI-based
digital
outperforms
manual
preserves
faithfully
sites
artworks
global
learning-driven
restoration
models.
first
reproducible
scientific
model
(CNN,
GAN
algorithms)
analysis
in
was
created.
Heca Journal of Applied Sciences,
Journal Year:
2024,
Volume and Issue:
2(2), P. 54 - 63
Published: Sept. 19, 2024
Mpox
is
a
viral
zoonotic
disease
that
presents
with
skin
lesions
similar
to
other
conditions
like
chickenpox,
measles,
and
hand-foot-mouth
disease,
making
accurate
diagnosis
challenging.
Early
precise
detection
of
mpox
critical
for
effective
treatment
outbreak
control,
particularly
in
resource-limited
settings
where
traditional
diagnostic
methods
are
often
unavailable.
While
deep
learning
models
have
been
applied
successfully
medical
imaging,
their
use
remains
underexplored.
To
address
this
gap,
we
developed
learning-based
approach
using
the
ResNet50v2
model
classify
alongside
five
conditions.
We
also
incorporated
Grad-CAM
(Gradient-weighted
Class
Activation
Mapping)
enhance
interpretability.
The
results
show
achieved
an
accuracy
99.33%,
precision
99.34%,
sensitivity
F1-score
99.32%
on
dataset
1,594
images.
visualizations
confirmed
focused
relevant
lesion
areas
its
predictions.
performed
exceptionally
well
overall,
it
struggled
misclassifications
between
visually
diseases,
such
as
chickenpox
mpox.
These
demonstrate
AI-based
tools
can
provide
reliable,
interpretable
support
clinicians,
limited
access
specialized
diagnostics.
However,
future
work
should
focus
expanding
datasets
improving
model's
capacity
distinguish
BMC Medical Imaging,
Journal Year:
2025,
Volume and Issue:
25(1)
Published: April 18, 2025
Medical
imaging
has
been
essential
and
provided
clinicians
with
useful
information
about
the
human
body
to
diagnose
various
health
issues.
Early
diagnosis
of
diseases
based
on
medical
can
mitigate
risk
severe
consequences
enhance
long-term
outcomes.
Nevertheless,
task
diagnosing
be
challenging
due
exclusive
ability
interpret
outcomes
imaging,
which
is
time-consuming
susceptible
fallibility.
The
ensemble
model
potential
accuracy
diagnoses
by
analyzing
vast
volumes
data
identifying
trends
that
may
not
immediately
apparent
doctors.
However,
it
takes
a
lot
memory
processing
resources
train
maintain
several
models.
These
challenges
highlight
necessity
effective
scalable
models
manage
intricacies
assignments.
This
study
employed
an
SLR
technique
explore
latest
advancements
approaches.
By
conducting
thorough
systematic
search
Scopus
Web
Science
databases
in
accordance
principles
outlined
PRISMA,
employing
keywords
namely
imaging.
included
total
75
papers
were
published
between
2019
2024.
categorization,
methodologies,
use
key
factors
examined
analysis
30
cited
this
study,
focus
diseases.
Researchers
have
observed
emergence
for
disease
using
since
demonstrated
improved
guide
future
studies
highlighting
limitations
model.
Healthcare,
Journal Year:
2024,
Volume and Issue:
12(23), P. 2330 - 2330
Published: Nov. 21, 2024
Artificial
Intelligence
(AI)
is
poised
to
revolutionize
numerous
aspects
of
human
life,
with
healthcare
among
the
most
critical
fields
set
benefit
from
this
transformation.
Medicine
remains
one
challenging,
expensive,
and
impactful
sectors,
challenges
such
as
information
retrieval,
data
organization,
diagnostic
accuracy,
cost
reduction.
AI
uniquely
suited
address
these
challenges,
ultimately
improving
quality
life
reducing
costs
for
patients
worldwide.
Despite
its
potential,
adoption
in
has
been
slower
compared
other
industries,
highlighting
need
understand
specific
obstacles
hindering
progress.
This
review
identifies
current
shortcomings
explores
possibilities,
realities,
frontiers
provide
a
roadmap
future
advancements.
