Artificial Intelligence Review,
Journal Year:
2025,
Volume and Issue:
58(3)
Published: Jan. 17, 2025
Edge
deep
learning,
a
paradigm
change
reconciling
edge
computing
and
facilitates
real-time
decision
making
attuned
to
environmental
factors
through
the
close
integration
of
computational
resources
data
sources.
Here
we
provide
comprehensive
review
current
state
art
in
focusing
on
computer
vision
applications,
particular
medical
diagnostics.
An
overview
foundational
principles
technical
advantages
learning
is
presented,
emphasising
capacity
this
technology
revolutionise
wide
range
domains.
Furthermore,
present
novel
categorisation
hardware
platforms
based
performance
usage
scenarios,
facilitating
platform
selection
operational
effectiveness.
Following
this,
dive
into
approaches
effectively
implement
neural
networks
devices,
encompassing
methods
such
as
lightweight
design
model
compression.
Reviewing
practical
applications
fields
general
diagnostics
particular,
demonstrate
profound
impact
edge-deployed
models
can
have
real-life
situations.
Finally,
an
analysis
potential
future
directions
obstacles
adoption
with
intention
stimulate
further
investigations
advancements
intelligent
solutions.
This
survey
provides
researchers
practitioners
reference
shedding
light
critical
role
plays
advancement
applications.
Journal of Imaging,
Journal Year:
2024,
Volume and Issue:
10(4), P. 81 - 81
Published: March 28, 2024
Computer
vision
(CV),
a
type
of
artificial
intelligence
(AI)
that
uses
digital
videos
or
sequence
images
to
recognize
content,
has
been
used
extensively
across
industries
in
recent
years.
However,
the
healthcare
industry,
its
applications
are
limited
by
factors
like
privacy,
safety,
and
ethical
concerns.
Despite
this,
CV
potential
improve
patient
monitoring,
system
efficiencies,
while
reducing
workload.
In
contrast
previous
reviews,
we
focus
on
end-user
CV.
First,
briefly
review
categorize
other
(job
enhancement,
surveillance
automation,
augmented
reality).
We
then
developments
hospital
setting,
outpatient,
community
settings.
The
advances
monitoring
delirium,
pain
sedation,
deterioration,
mechanical
ventilation,
mobility,
surgical
applications,
quantification
workload
hospital,
for
events
outside
highlighted.
To
identify
opportunities
future
also
completed
journey
mapping
at
different
levels.
Lastly,
discuss
considerations
associated
with
outline
processes
algorithm
development
testing
limit
expansion
healthcare.
This
comprehensive
highlights
ideas
expanded
use
International Journal of Computational and Experimental Science and Engineering,
Journal Year:
2025,
Volume and Issue:
11(1)
Published: Feb. 12, 2025
Bone
cancer,
especially
osteosarcoma,
is
an
aggressive
tumor
with
a
highly
complex
histopathologic
appearance
that
imposes
considerable
diagnostic
difficulties.
Although
practical
and
efficient,
traditional
methods
current
deep
learning
models
have
class
imbalance,
fused
pixel
intensity
distributions,
tissue
heterogeneity
hinder
efficiency.
These
problems
emphasize
the
demand
of
more
sophisticated
frameworks
specifically
address
distinct
properties
bone
cancer
histopathology
images.
To
overcome
these
shortcomings,
in
this
study
proposes
framework,
IBCDNet,
to
alleviate
limitations.
Inspired
by
cutting-edge
improvements
architecture
(e.g.,
like
attention,
residual
connections,
proposed
Intelligent
Learning-Based
Cancer
Detection
(ILB-BCD)
algorithm),
framework
combines
different
features
from
both
public
private
datasets
efficient
way.
This
allows
for
strong
feature
extraction,
better
imbalanced
data,
thus
precise
classification.
The
model
obtains
state-of-the-art
results
98.39%
on
Osteosarcoma
Tumor
Assessment
Dataset,
outperforming
powerful
baseline
ResNet50,
DenseNet121,
InceptionV3.
further
affirms
its
robustness
respective
precision
(97.8%),
recall
(98.1%),
F1-score
(98.0%)
which
shows
remarkable
improvement
We
present
cost-effective
scalable
real-world
clinical
applications
assist
pathologists
early
detection
accurate
diagnosis
cancer.
Those
important
gaps
identified
addressed
research
contribute
progress
towards
AI-driven
healthcare
global
goals
medicine
enhanced
patient
outcomes.
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 2, 2024
In
addition
to
enhancing
diagnostic
accuracy,
deep
learning
techniques
offer
the
potential
streamline
workflows,
reduce
interpretation
time,
and
ultimately
improve
patient
outcomes.
The
scalability
adaptability
of
algorithms
enable
their
deployment
across
diverse
clinical
settings,
ranging
from
radiology
departments
point-of-care
facilities.
Furthermore,
ongoing
research
efforts
focus
on
addressing
challenges
data
heterogeneity,
model
interpretability,
regulatory
compliance,
paving
way
for
seamless
integration
solutions
into
routine
practice.
As
field
continues
evolve,
collaborations
between
clinicians,
scientists,
industry
stakeholders
will
be
paramount
in
harnessing
full
advancing
medical
image
analysis
diagnosis.
with
other
technologies,
including
natural
language
processing
computer
vision,
may
foster
multimodal
decision
support
systems
care.
future
diagnosis
is
promising.
With
each
success
advancement,
this
technology
getting
closer
being
leveraged
purposes.
Beyond
analysis,
care
pathways
like
imaging,
imaging
genomics,
intelligent
operating
rooms
or
intensive
units
can
benefit
models.
Cancers,
Journal Year:
2024,
Volume and Issue:
16(13), P. 2448 - 2448
Published: July 3, 2024
Cancer
is
one
of
the
leading
causes
death,
making
timely
diagnosis
and
prognosis
very
important.
Utilization
AI
(artificial
intelligence)
enables
providers
to
organize
process
patient
data
in
a
way
that
can
lead
better
overall
outcomes.
This
review
paper
aims
look
at
varying
uses
for
clinical
utility.
PubMed
EBSCO
databases
were
utilized
finding
publications
from
1
January
2020
22
December
2023.
Articles
collected
using
key
search
terms
such
as
“artificial
intelligence”
“machine
learning.”
Included
collection
studies
application
determining
cancer
multi-omics
data,
radiomics,
pathomics,
laboratory
data.
The
resulting
89
categorized
into
eight
sections
based
on
type
then
further
subdivided
two
subsections
focusing
prognosis,
respectively.
Eight
integrated
more
than
form
omics,
namely
genomics,
transcriptomics,
epigenomics,
proteomics.
Incorporating
alongside
omics
represents
significant
advancement.
Given
considerable
potential
this
domain,
ongoing
prospective
are
essential
enhance
algorithm
interpretability
ensure
safe
integration.
Electronics,
Journal Year:
2024,
Volume and Issue:
13(4), P. 746 - 746
Published: Feb. 13, 2024
Recently,
artificial
intelligence
(AI)-based
algorithms
have
revolutionized
the
medical
image
segmentation
processes.
Thus,
precise
of
organs
and
their
lesions
may
contribute
to
an
efficient
diagnostics
process
a
more
effective
selection
targeted
therapies,
as
well
increasing
effectiveness
training
process.
In
this
context,
AI
automatization
scan
increase
quality
resulting
3D
objects,
which
lead
generation
realistic
virtual
objects.
paper,
we
focus
on
AI-based
solutions
applied
in
intelligent
visual
content
generation,
i.e.,
computer-generated
three-dimensional
(3D)
images
context
extended
reality
(XR).
We
consider
different
types
neural
networks
used
with
special
emphasis
learning
rules
applied,
taking
into
account
algorithm
accuracy
performance,
open
data
availability.
This
paper
attempts
summarize
current
development
methods
imaging
that
are
XR.
It
concludes
possible
developments
challenges
applications
reality-based
solutions.
Finally,
future
lines
research
directions
applications,
both
solutions,
discussed.
