PRILOZI,
Год журнала:
2024,
Номер
45(2), С. 5 - 13
Опубликована: Июнь 1, 2024
Over
the
past
period
different
reports
related
to
artificial
intelligence
(AI)
and
machine
learning
used
in
everyday
life
have
been
growing
intensely.
However,
AI
our
country
is
still
very
limited,
especially
field
of
medicine.
The
aim
this
article
give
some
review
about
medicine
fields
based
on
published
articles
PubMed
Psych
Net.
A
research
showed
more
than
9
thousand
available
at
mentioned
databases.
After
providing
historical
data,
applications
are
discussed.
Finally,
limitations
ethical
implications
Journal of Medical Internet Research,
Год журнала:
2024,
Номер
26, С. e56780 - e56780
Опубликована: Май 31, 2024
Large
language
models
(LLMs)
such
as
ChatGPT
have
become
widely
applied
in
the
field
of
medical
research.
In
process
conducting
systematic
reviews,
similar
tools
can
be
used
to
expedite
various
steps,
including
defining
clinical
questions,
performing
literature
search,
document
screening,
information
extraction,
and
refinement,
thereby
conserving
resources
enhancing
efficiency.
However,
when
using
LLMs,
attention
should
paid
transparent
reporting,
distinguishing
between
genuine
false
content,
avoiding
academic
misconduct.
this
viewpoint,
we
highlight
potential
roles
LLMs
creation
reviews
meta-analyses,
elucidating
their
advantages,
limitations,
future
research
directions,
aiming
provide
insights
guidance
for
authors
planning
meta-analyses.
PLoS ONE,
Год журнала:
2025,
Номер
20(1), С. e0313401 - e0313401
Опубликована: Янв. 7, 2025
Background
Systematic
reviews
provide
clarity
of
a
bulk
evidence
and
support
the
transfer
knowledge
from
clinical
trials
to
guidelines.
Yet,
they
are
time-consuming.
Artificial
intelligence
(AI),
like
ChatGPT-4o,
may
streamline
processes
data
extraction,
but
its
efficacy
requires
validation.
Objective
This
study
aims
(1)
evaluate
validity
ChatGPT-4o
for
extraction
compared
human
reviewers,
(2)
test
reproducibility
ChatGPT-4o’s
extraction.
Methods
We
conducted
comparative
using
papers
an
ongoing
systematic
review
on
exercise
reduce
fall
risk.
Data
extracted
by
were
reference
standard:
two
independent
reviewers.
The
was
assessed
categorizing
into
five
categories
ranging
completely
correct
false
data.
Reproducibility
evaluated
comparing
in
separate
sessions
different
accounts.
Results
total
484
points
across
11
papers.
AI’s
92.4%
accurate
(95%
CI:
89.5%
94.5%)
produced
5.2%
cases
3.4%
7.4%).
between
high,
with
overall
agreement
94.1%.
decreased
when
information
not
reported
papers,
77.2%.
Conclusion
Validity
high
reviews.
qualified
as
second
reviewer
showed
potential
future
advancements
summarizing
JAMA Otolaryngology–Head & Neck Surgery,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 30, 2025
This
year,
JAMA
Otolaryngology–Head
&
Neck
Surgery
celebrates
its
100th
year
of
continuous
publication.
As
we
celebrate
100
years
publication,
look
back
on
our
journey,
from
modest
beginnings
to
being
a
valued
contributor
medical
knowledge.
JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 13, 2025
Abstract
Almost
every
facet
of
modern
biomedical
research
involves
artificial
intelligence
(AI).
This
ACCP
commentary
forecasts
the
role
AI
in
clinical
pharmacy
and
scholarship.
The
potential
benefits/opportunities
together
with
limitations/challenges
are
reviewed
for
stages
scientific
method
including
(1)
developing
question(s),
study
design,
execution;
(2)
data
analysis;
(3)
reporting
dissemination
research.
Benefits
opportunities
include
streamlining
hypothesis
generation
facilitating
overcoming
limitations
traditional
statistical
analysis
techniques,
manuscript
development
dissemination,
expediting
peer
review.
Limitations
challenges
introduction
biases
subject
recruitment;
false
information,
also
known
as
“AI
hallucinations”;
concern
“black
box”
analyses
that
difficult
to
validate;
legal
liabilities;
lack
accountability;
need
investigators
ensure
accuracy
integrity
AI‐generated
content.
In
summary,
rapid
progress
capabilities
has
great
revolutionize
accelerate
scholarship;
however,
it
is
imperative
recognize
mitigate
introduced
by
AI.
Journal of the Society for Cardiovascular Angiography & Interventions,
Год журнала:
2025,
Номер
4(3), С. 102562 - 102562
Опубликована: Март 1, 2025
Artificial
intelligence
(AI)
serves
as
a
powerful
tool
that
can
revolutionize
how
personalized,
patient-focused
care
is
provided
within
interventional
cardiology.
Specifically,
AI
augment
clinical
across
the
spectrum
for
acute
coronary
syndrome,
artery
disease,
and
valvular
heart
with
applications
in
structural
interventions.
This
has
been
enabled
by
potential
of
to
harness
various
types
health
data.
We
review
AI-driven
technologies
advance
diagnosis,
preprocedural
planning,
intraprocedural
guidance,
prognostication
automates
tasks,
increases
efficiency,
improves
reliability
accuracy,
individualizes
care,
establishing
its
transform
care.
Furthermore,
AI-enabled,
community-based
screening
programs
are
yet
be
implemented
leverage
full
improve
patient
outcomes.
However,
practice,
tools
require
robust
transparent
development
processes,
consistent
performance
settings
populations,
positive
impact
on
quality
outcomes,
seamless
integration
into
workflows.
Once
these
established,
reshape
cardiology,
improving
precision,
Abstract
Coronary
artery
disease
(CAD)
is
a
major
cause
of
ill
health
and
death
worldwide.
computed
tomographic
angiography
(CCTA)
the
first-line
investigation
to
detect
CAD
in
symptomatic
patients.
This
diagnostic
approach
risks
greater
second-line
heart
tests
treatments
at
cost
patient
system.
The
National
Health
Service
funded
use
an
artificial
intelligence
(AI)
tool,
tomography
(CT)-derived
fractional
flow
reserve
(FFR-CT),
patients
with
chest
pain
improve
physician
decision-making
reduce
downstream
tests.
observational
cohort
study
assessed
impact
FFR-CT
on
cardiovascular
outcomes
by
including
all
investigated
CCTA
during
national
AI
implementation
program
27
hospitals
(CCTA
n
=
90,553
7,863).
was
safe,
no
difference
all-cause
(
1,134
(3.2%)
versus
1,612
(2.9%),
adjusted-hazard
ratio
(aHR)
1.00
(0.93–1.08),
P
0.97)
or
mortality
465
(1.3%)
617
(1.1%),
aHR
0.96
(0.85–1.08),
0.48),
while
reducing
invasive
coronary
angiograms
5,720
(16%)
8,183
(14.9%),
0.93
(0.90–0.97),
<
0.001)
noninvasive
cardiac
(189/1,000
167/1,000),
0.001).
Implementation
AI-diagnostic
tool
as
part
intervention
safe
beneficial
pathway
system
fewer
2
years.
Stroke,
Год журнала:
2024,
Номер
55(10), С. 2573 - 2578
Опубликована: Сен. 3, 2024
Artificial
intelligence
(AI)
large
language
models
(LLMs)
now
produce
human-like
general
text
and
images.
LLMs'
ability
to
generate
persuasive
scientific
essays
that
undergo
evaluation
under
traditional
peer
review
has
not
been
systematically
studied.
To
measure
perceptions
of
quality
the
nature
authorship,
we
conducted
a
competitive
essay
contest
in
2024
with
both
human
AI
participants.
Human
authors
4
distinct
LLMs
generated
on
controversial
topics
stroke
care
outcomes
research.
A
panel
This
Viewpoint
discusses
the
role
of
generative
artificial
intelligence
in
surgical
publishing,
including
idea
generation,
study
conduct,
manuscript
preparation,
and
review.
