Tuberculosis & respiratory diseases,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 17, 2024
Thoracic
radiology
is
a
primary
field
where
artificial
intelligence
(AI)
has
been
extensively
researched.Recent
advancements
in
AI
demonstrate
potential
improvements
radiologists'
performance.AI
facilitates
the
detection
and
classification
of
abnormalities,
as
well
quantification
both
normal
abnormal
anatomical
structures.Furthermore,
it
enables
prognostication
based
on
these
quantitative
values.In
this
review
article,
recent
achievements
thoracic
will
be
reviewed,
mainly
focused
deep
learning,
current
limitations
future
directions
cutting-edge
technique
discussed.
Korean Journal of Radiology,
Год журнала:
2024,
Номер
25(11), С. 959 - 959
Опубликована: Янв. 1, 2024
Generative
artificial
intelligence
(AI)
has
been
applied
to
images
for
image
quality
enhancement,
domain
transfer,
and
augmentation
of
training
data
AI
modeling
in
various
medical
fields.
Image-generative
can
produce
large
amounts
unannotated
imaging
data,
which
facilitates
multiple
downstream
deep-learning
tasks.
However,
their
evaluation
methods
clinical
utility
have
not
thoroughly
reviewed.
This
article
summarizes
commonly
used
generative
adversarial
networks
diffusion
models.
In
addition,
it
tasks
the
field
radiology,
such
as
direct
utilization,
lesion
detection,
segmentation,
diagnosis.
aims
guide
readers
regarding
radiology
practice
research
using
image-generative
by
1)
reviewing
basic
theories
AI,
2)
discussing
evaluate
generated
images,
3)
outlining
4)
issue
hallucinations.
Apollo Medicine,
Год журнала:
2024,
Номер
21(4), С. 386 - 392
Опубликована: Июль 25, 2024
Background
and
Aim:
Chatbots
are
computer
programs,
which
devised
to
simulate
conversations
through
voice
or
textual
interactive
forms.
These
applications
offer
multiple
benefits
in
patient
education,
clinical
decision-making,
interpersonal
communication,
research
activities,
data
analysis
administrative
affairs.
The
present
scoping
review
aims
analyse
the
current
role,
pitfalls,
challenges
future
scope
of
these
modalities
diverse
fields
medical
science.
Methods:
Literature
search
was
made
on
9
April
2024,
five
databases
(PubMed,
Scopus,
Web
Science,
Embase
Google
Scholar).
A
narrative
approach
used
for
synthesis
results.
Results:
Our
literature
yielded
1024
studies.
After
de-duplication
manuscripts
using
Endnote,
342
articles
were
identified.
title
abstract
screening,
74
included
next
round
screening.
Finally,
14
selected
this
review.Diverse
chatbot
have
been
developed
at
a
growing
rate
use
There
is
gradual
shift
towards
employing
machine
learning-based
strategies
develop
programs.
significant
potential
revolutionise
aspects
medicine
including
care,
academic
endeavours.
Conclusion:
Artificial
Intelligence
can
be
highly
effective
streamlining
routine
functions
activities
care.
meliorate
accessibility,
efficiency
standard
However,
need
further
validation
high-quality
studies
ensure
privacy,
conform
patients’
security,
accuracy
precision
technology
mitigating
pitfalls
its
utilisation
globally
warranted.
We
extend
existing
techniques
by
using
generative
adversarial
network
(GAN)
models
to
reduce
the
appearance
of
cast
shadows
in
radiographs
across
various
age
groups.
retrospectively
collected
11,500
adult
and
paediatric
wrist
radiographs,
evenly
divided
between
those
with
without
casts.
The
test
subset
consisted
750
cast.
extended
results
from
a
previous
study
that
employed
CycleGAN
enhancing
model
perceptual
loss
function
self-attention
layer.
which
incorporates
layer
delivered
similar
quantitative
performance
as
original
model.
This
was
applied
images
20
cases
where
reports
recommended
CT
scanning
or
repeat
cast,
were
then
evaluated
radiologists
for
qualitative
assessment.
demonstrated
generated
could
improve
radiologists'
diagnostic
confidence,
some
leading
more
decisive
reports.
Where
available,
follow-up
imaging
compared
produced
reading
AI-generated
images.
Every
report,
except
two,
provided
identical
diagnoses
associated
imaging.
ability
perform
robust
reporting
downsampled
AI-enhanced
is
clinically
meaningful
warrants
further
investigation.
Additionally,
unable
distinguish
unenhanced
These
findings
suggest
suppression
technique
be
integrated
tool
augment
clinical
workflows,
potential
benefits
reducing
patient
doses,
improving
operational
efficiencies,
delays
diagnoses,
number
visits.
HomeRadiologyVol.
314,
No.
3
PreviousNext
Reviews
and
CommentaryEditorialHarnessing
the
Power
of
Generative
AI
to
Enhance
Radiologist
Efficiency
AccuracyPaul
S.
Babyn1
,
Scott
J.
Adams1Paul
Adams1Author
Affiliations1Department
Medical
Imaging,
Royal
University
Hospital,
Saskatchewan,
103
Hospital
Dr,
Saskatoon,
SK,
Canada
S7N
0W8Address
correspondence
to:
P.S.B.
(email:
[email
protected])Paul
Adams1Published
Online:Mar
11
2025https://doi.org/10.1148/radiol.250339See
also
article
by
Hong
et
al
in
this
issue.MoreSectionsFull
textPDF
ToolsAdd
favoritesCiteTrack
CitationsPermissionsReprints
ShareShare
onFacebookXLinked
In
References1.
Shen
Y,
Xu
Ma
J,
al.
Multi-modal
large
language
models
radiology:
principles,
applications,
potential.
Abdom
Radiol
(NY)
2024.
10.1007/s00261-024-04708-8.
Published
online
December
2,
Google
Scholar2.
Kim
K,
Cho
Jang
R,
Updated
primer
on
generative
artificial
intelligence
medical
imaging
for
professionals.
Korean
J
2024;25(3):224–242.
Medline
Scholar3.
EK,
Roh
B,
Park
Value
using
a
model
chest
radiography
reporting:
reader
study.
Radiology
2025;314(3):e241646.
Scholar4.
Sacoransky
E,
Kwan
BYM,
Soboleski
D.
ChatGPT
assistive
structured
radiology
systematic
review.
Curr
Probl
Diagn
2024;53(6):728–737.
Scholar5.
Zhang
L,
Liu
M,
Wang
Constructing
generate
impressions
from
findings
reports.
2024;312(3):e240885.
Scholar6.
Amin
KS,
Davis
MA,
Doshi
Haims
AH,
Khosla
P,
Forman
HP.
Accuracy
ChatGPT,
Bard,
Microsoft
Bing
simplifying
2023;309(2):e232561.
Scholar7.
Lee
S,
Youn
H,
Yoon
SH.
CXR-LLaVA:
multimodal
interpreting
x-ray
images.
Eur
2025.
10.1007/s00330-024-11339-6.
January
15,
Scholar8.
Shin
HJ,
Han
Ryu
EK.
The
impact
reading
times
radiologists
radiographs.
NPJ
Digit
Med
2023;6(1):82.
Scholar9.
Yu
F,
Endo
Krishnan
Evaluating
progress
automatic
report
generation.
Patterns
2023;4(9):100802.
Scholar10.
Gefter
WB,
Prokop
Seo
JB,
Raoof
Langlotz
CP,
Hatabu
H.
Human-AI
symbiosis:
path
forward
improve
role
patient
care.
2024;310(1):e232778.
ScholarArticle
HistoryReceived:
Jan
29
2025Revision
requested:
Feb
received:
12
2025Accepted:
13
2025Published
online:
Mar
2025
FiguresReferencesRelatedDetailsAccompanying
This
ArticleValue
Using
Model
Chest
Radiography
Reporting:
A
Reader
StudyMar
2025Radiology
Vol.
Metrics
Altmetric
Score
PDF
download
Journal of Advanced Nursing,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
ABSTRACT
Aim
This
study
aimed
to
assess
the
performance
of
Visual
ChatGPT
in
staging
pressure
injuries
using
real
patient
images,
compare
it
manual
by
expert
nurses,
and
evaluate
its
applicability
as
a
supportive
tool
wound
care
management.
Design
used
descriptive
comparative
cross‐sectional
design.
Methods
The
analysed
155
injury
images
from
hospital
database,
staged
nurses
National
Pressure
Injury
Advisory
Panel
guidelines.
ChatGPT's
was
tested
two
scenarios:
with
only
plus
characteristics.
Diagnostic
evaluated,
including
sensitivity,
specificity,
accuracy,
inter‐rater
agreement
(Kappa).
Results
Expert
demonstrated
superior
accuracy
specificity
across
most
stages.
performed
comparably
early‐stage
injuries,
especially
when
characteristics
were
included,
but
struggled
unstageable
deep‐tissue
injuries.
Conclusion
shows
potential
an
artificial
intelligence
for
management
nursing.
However,
improvements
are
necessary
complex
cases,
ensuring
that
complements
clinical
judgement.
Implications
Profession
and/or
Patient
Care
can
serve
innovative
settings,
assisting
less
experienced
those
areas
limited
specialists
managing
Reporting
Method
STROBE
checklist
followed
reporting
studies
line
relevant
EQUATOR
Contribution
No
or
public
contribution.
