Meta-Radiology,
Journal Year:
2023,
Volume and Issue:
1(1), P. 100003 - 100003
Published: June 1, 2023
The
use
of
AI
systems
in
healthcare
for
the
early
screening
diseases
is
great
clinical
importance.
Deep
learning
has
shown
promise
medical
imaging,
but
reliability
and
trustworthiness
limit
their
deployment
real
scenes,
where
patient
safety
at
stake.
Uncertainty
estimation
plays
a
pivotal
role
producing
confidence
evaluation
along
with
prediction
deep
model.
This
particularly
important
uncertainty
model's
predictions
can
be
used
to
identify
areas
concern
or
provide
additional
information
clinician.
In
this
paper,
we
review
various
types
learning,
including
aleatoric
epistemic
uncertainty.
We
further
discuss
how
they
estimated
imaging.
More
importantly,
recent
advances
models
that
incorporate
Finally,
challenges
future
directions
hope
will
ignite
interest
community
researchers
an
up-to-date
reference
regarding
applications
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Journal Year:
2023,
Volume and Issue:
45(10), P. 12113 - 12132
Published: May 11, 2023
Transformer
is
a
promising
neural
network
learner,
and
has
achieved
great
success
in
various
machine
learning
tasks.
Thanks
to
the
recent
prevalence
of
multimodal
applications
Big
Data,
Transformer-based
become
hot
topic
AI
research.
This
paper
presents
comprehensive
survey
techniques
oriented
at
data.
The
main
contents
this
include:
(1)
background
learning,
ecosystem,
Data
era,
(2)
systematic
review
Vanilla
Transformer,
Vision
Transformers,
from
geometrically
topological
perspective,
(3)
applications,
via
two
important
paradigms,
i.e.,
for
pretraining
specific
tasks,
(4)
summary
common
challenges
designs
shared
by
models
(5)
discussion
open
problems
potential
research
directions
community.
Journal of Personalized Medicine,
Journal Year:
2023,
Volume and Issue:
13(6), P. 951 - 951
Published: June 5, 2023
Artificial
intelligence
(AI)
applications
have
transformed
healthcare.
This
study
is
based
on
a
general
literature
review
uncovering
the
role
of
AI
in
healthcare
and
focuses
following
key
aspects:
(i)
medical
imaging
diagnostics,
(ii)
virtual
patient
care,
(iii)
research
drug
discovery,
(iv)
engagement
compliance,
(v)
rehabilitation,
(vi)
other
administrative
applications.
The
impact
observed
detecting
clinical
conditions
diagnostic
services,
controlling
outbreak
coronavirus
disease
2019
(COVID-19)
with
early
diagnosis,
providing
care
using
AI-powered
tools,
managing
electronic
health
records,
augmenting
compliance
treatment
plan,
reducing
workload
professionals
(HCPs),
discovering
new
drugs
vaccines,
spotting
prescription
errors,
extensive
data
storage
analysis,
technology-assisted
rehabilitation.
Nevertheless,
this
science
pitch
meets
several
technical,
ethical,
social
challenges,
including
privacy,
safety,
right
to
decide
try,
costs,
information
consent,
access,
efficacy,
while
integrating
into
governance
crucial
for
safety
accountability
raising
HCPs'
belief
enhancing
acceptance
boosting
significant
consequences.
Effective
prerequisite
precisely
address
regulatory,
trust
issues
advancing
implementation
AI.
Since
COVID-19
hit
global
system,
concept
has
created
revolution
healthcare,
such
an
uprising
could
be
another
step
forward
meet
future
needs.
Cancer Cell,
Journal Year:
2022,
Volume and Issue:
40(10), P. 1095 - 1110
Published: Oct. 1, 2022
In
oncology,
the
patient
state
is
characterized
by
a
whole
spectrum
of
modalities,
ranging
from
radiology,
histology,
and
genomics
to
electronic
health
records.
Current
artificial
intelligence
(AI)
models
operate
mainly
in
realm
single
modality,
neglecting
broader
clinical
context,
which
inevitably
diminishes
their
potential.
Integration
different
data
modalities
provides
opportunities
increase
robustness
accuracy
diagnostic
prognostic
models,
bringing
AI
closer
practice.
are
also
capable
discovering
novel
patterns
within
across
suitable
for
explaining
differences
outcomes
or
treatment
resistance.
The
insights
gleaned
such
can
guide
exploration
studies
contribute
discovery
biomarkers
therapeutic
targets.
To
support
these
advances,
here
we
present
synopsis
methods
strategies
multimodal
fusion
association
discovery.
We
outline
approaches
interpretability
directions
AI-driven
through
interconnections.
examine
challenges
adoption
discuss
emerging
solutions.
Bioengineering,
Journal Year:
2023,
Volume and Issue:
10(12), P. 1435 - 1435
Published: Dec. 18, 2023
The
integration
of
artificial
intelligence
(AI)
into
medical
imaging
has
guided
in
an
era
transformation
healthcare.
This
literature
review
explores
the
latest
innovations
and
applications
AI
field,
highlighting
its
profound
impact
on
diagnosis
patient
care.
innovation
segment
cutting-edge
developments
AI,
such
as
deep
learning
algorithms,
convolutional
neural
networks,
generative
adversarial
which
have
significantly
improved
accuracy
efficiency
image
analysis.
These
enabled
rapid
accurate
detection
abnormalities,
from
identifying
tumors
during
radiological
examinations
to
detecting
early
signs
eye
disease
retinal
images.
article
also
highlights
various
imaging,
including
radiology,
pathology,
cardiology,
more.
AI-based
diagnostic
tools
not
only
speed
up
interpretation
complex
images
but
improve
disease,
ultimately
delivering
better
outcomes
for
patients.
Additionally,
processing
facilitates
personalized
treatment
plans,
thereby
optimizing
healthcare
delivery.
paradigm
shift
that
brought
role
revolutionizing
By
combining
techniques
their
practical
applications,
it
is
clear
will
continue
shaping
future
positive
ways.
IEEE Access,
Journal Year:
2022,
Volume and Issue:
10, P. 28642 - 28655
Published: Jan. 1, 2022
Diabetic
Retinopathy
(DR)
is
a
degenerative
disease
that
impacts
the
eyes
and
consequence
of
Diabetes
mellitus,
where
high
blood
glucose
levels
induce
lesions
on
eye
retina.Diabetic
regarded
as
leading
cause
blindness
for
diabetic
patients,
especially
working-age
population
in
developing
nations.Treatment
involves
sustaining
patient's
current
grade
vision
since
irreversible.Early
detection
crucial
order
to
sustain
effectively.The
main
issue
involved
with
DR
manual
diagnosis
process
very
time,
money,
effort
consuming
an
ophthalmologist's
examination
retinal
fundus
images.The
latter
also
proves
be
more
difficult,
particularly
early
stages
when
features
are
less
prominent
images.Machine
learning-based
medical
image
analysis
has
proven
competency
assessing
images,
utilization
deep
learning
algorithms
aided
(DR).This
paper
reviews
analyzes
state-of-the-art
methods
supervised,
self-supervised,
Vision
Transformer
setups,
proposing
classification
detection.For
instance,
referable,
non-referable,
proliferative
classifications
reviewed
summarized.Moreover,
discusses
available
datasets
used
tasks
such
detection,
classification,
segmentation.The
assesses
research
gaps
area
detection/classification
addresses
various
challenges
need
further
study
investigation.
Current Oncology,
Journal Year:
2022,
Volume and Issue:
29(10), P. 7498 - 7511
Published: Oct. 7, 2022
The
automated
classification
of
brain
tumors
plays
an
important
role
in
supporting
radiologists
decision
making.
Recently,
vision
transformer
(ViT)-based
deep
neural
network
architectures
have
gained
attention
the
computer
research
domain
owing
to
tremendous
success
models
natural
language
processing.
Hence,
this
study,
ability
ensemble
standard
ViT
for
diagnosis
from
T1-weighted
(T1w)
magnetic
resonance
imaging
(MRI)
is
investigated.
Pretrained
and
finetuned
(B/16,
B/32,
L/16,
L/32)
on
ImageNet
were
adopted
task.
A
tumor
dataset
figshare,
consisting
3064
T1w
contrast-enhanced
(CE)
MRI
slices
with
meningiomas,
gliomas,
pituitary
tumors,
was
used
cross-validation
testing
model's
perform
a
three-class
best
individual
model
L/32,
overall
test
accuracy
98.2%
at
384
×
resolution.
all
four
demonstrated
98.7%
same
resolution,
outperforming
both
resolutions
their
ensembling
224
In
conclusion,
could
be
deployed
computer-aided
based
CE
MRI,
leading
radiologist
relief.
