Journal of Clinical Medicine,
Год журнала:
2024,
Номер
13(24), С. 7833 - 7833
Опубликована: Дек. 22, 2024
The
integration
of
artificial
intelligence
(AI)
into
hepatology
is
revolutionizing
the
diagnosis
and
management
liver
diseases
amidst
a
rising
global
burden
conditions
like
metabolic-associated
steatotic
disease
(MASLD).
AI
harnesses
vast
datasets
complex
algorithms
to
enhance
clinical
decision
making
patient
outcomes.
AI’s
applications
in
span
variety
conditions,
including
autoimmune
hepatitis,
primary
biliary
cholangitis,
sclerosing
MASLD,
hepatitis
B,
hepatocellular
carcinoma.
It
enables
early
detection,
predicts
progression,
supports
more
precise
treatment
strategies.
Despite
its
transformative
potential,
challenges
remain,
data
integration,
algorithm
transparency,
computational
demands.
This
review
examines
current
state
hepatology,
exploring
applications,
limitations,
opportunities
it
presents
health
care
delivery.
International Orthopaedics,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 18, 2025
Abstract
Purpose
The
purpose
of
this
scoping
review
is
to
analyze
the
application
artificial
intelligence
(AI)
in
ultrasound
(US)
imaging
for
diagnosing
carpal
tunnel
syndrome
(CTS),
with
an
aim
explore
potential
AI
enhancing
diagnostic
accuracy,
efficiency,
and
patient
outcomes
by
automating
tasks,
providing
objective
measurements,
facilitating
earlier
detection
CTS.
Methods
We
systematically
searched
multiple
electronic
databases,
including
Embase,
PubMed,
IEEE
Xplore,
Scopus,
identify
relevant
studies
published
up
January
1,
2025.
Studies
were
included
if
they
focused
on
US
CTS
diagnosis.
Editorials,
expert
opinions,
conference
papers,
dataset
publications,
that
did
not
have
a
clear
clinical
algorithm
excluded.
Results
345
articles
identified,
following
abstract
full-text
two
independent
reviewers,
18
manuscripts
included.
Of
these,
thirteen
experimental
studies,
three
comparative
one
was
feasibility
study.
All
eighteen
shared
common
improving
diagnosis
and/or
initial
assessment
using
AI,
aims
ranging
from
median
nerve
segmentation
(
n
=
12)
automated
9)
severity
classification
2).
majority
utilized
deep
learning
approaches,
particularly
CNNs
15),
some
radiomics
features
5)
traditional
machine
techniques.
Conclusion
integration
holds
significant
promise
transforming
practice.
has
improve
streamline
process,
reduce
variability,
ultimately
lead
better
outcomes.
Further
research
needed
address
challenges
related
limitations,
variability
imaging,
ethical
considerations.
BMJ Open,
Год журнала:
2025,
Номер
15(2), С. e094908 - e094908
Опубликована: Фев. 1, 2025
Introduction
Empirical
data
on
the
barriers
limiting
artificial
intelligence
(AI)’s
impact
healthcare
are
scarce,
particularly
within
Canadian
context.
This
study
aims
to
address
this
gap
by
conducting
a
scoping
review
identify
and
evaluate
AI
algorithms
developed
researchers
affiliated
with
institutions
for
patient
triage,
diagnosis
care
management.
The
goal
is
characteristics
in
that
can
be
leveraged
better
impact.
Methods
analysis
A
will
conducted
following
JBI
Methodology
Scoping
Reviews
reported
Preferred
Reporting
Items
Systematic
Meta-Analyses
extension
guidelines.
Relevant
literature
identified
through
comprehensive
searches
of
MEDLINE
(PubMed),
CINAHL
(EBSCO)
Web
Science
(Clarivate)
databases,
combining
keywords
related
AI,
clinical
management
Studies
published
after
2014,
English
or
French,
discuss
included.
Data
from
selected
articles
extracted
analysed
descriptively,
findings
presented
tabular
form
accompanied
narrative
summary.
Ethics
dissemination
Ethical
approval
not
required
as
it
involves
publicly
available
literature.
expected
completed
November
2025.
disseminated
publications
peer-reviewed
journals
presentations
at
conferences
focused
practice.
