Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews
Health Services Research and Managerial Epidemiology,
Год журнала:
2024,
Номер
11
Опубликована: Янв. 1, 2024
Introduction
The
use
of
artificial
intelligence
(AI),
which
can
emulate
human
and
enhance
clinical
results,
has
grown
in
healthcare
decision-making
due
to
the
digitalization
effects
COVID-19
pandemic.
purpose
this
study
was
determine
scope
applications
AI
tools
process
service
delivery
networks.
Materials
methods
This
used
a
qualitative
method
conduct
systematic
review
existing
reviews.
Review
articles
published
between
2000
2024
English-language
were
searched
PubMed,
Scopus,
ProQuest,
Cochrane
databases.
CASP
(Critical
Appraisal
Skills
Programme)
Checklist
for
Systematic
Reviews
evaluate
quality
articles.
Based
on
eligibility
criteria,
final
selected
data
extraction
done
independently
by
2
authors.
Finally,
thematic
analysis
approach
analyze
extracted
from
Results
Of
14
219
identified
records,
18
eligible
included
analysis,
covered
findings
669
other
assessment
score
all
reviewed
high.
And,
3
main
themes
including
decision-making,
organizational
shared
decision-making;
originated
8
subthemes.
Conclusions
revealed
that
have
been
applied
various
aspects
decision-making.
improve
quality,
efficiency,
effectiveness
services
providing
accurate,
timely,
personalized
information
support
Further
research
is
needed
explore
best
practices
standards
implementing
Язык: Английский
Technology Readiness Assessment: Case of Clinical Decision Support Systems in Healthcare
Technology in Society,
Год журнала:
2024,
Номер
unknown, С. 102736 - 102736
Опубликована: Окт. 1, 2024
Язык: Английский
Les biais de l’IA : enjeux et précautions pour une prise de décision éthique et fiable en santé
Médecine de Catastrophe - Urgences Collectives,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 1, 2025
Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review
Medical Devices Evidence and Research,
Год журнала:
2024,
Номер
Volume 17, С. 191 - 211
Опубликована: Май 1, 2024
Abstract:
This
study
aimed
to
analyze
the
use
of
generative
artificial
intelligence
in
emergency
trauma
care
setting
through
a
brief
scoping
review
literature
published
between
2014
and
2024.
An
exploration
NCBI
repository
was
performed
using
search
string
selected
keywords
that
returned
N=87
results;
articles
met
inclusion
criteria
(n=28)
were
reviewed
analyzed.
Heterogeneity
sources
explored
identified
by
significance
threshold
P
<
0.10
or
an
I
2
value
exceeding
50%.
If
applicable,
categorized
within
three
primary
domains:
triage,
diagnostics,
treatment.
Findings
suggest
CNNs
demonstrate
strong
diagnostic
performance
for
diverse
traumatic
injuries,
but
generalized
integration
requires
expanded
prospective
multi-center
validation.
Injury
scoring
models
currently
experience
calibration
gaps
mortality
quantification
lesion
localization
can
undermine
clinical
utility
permitting
false
negatives.
Triage
predictive
now
confront
transparency,
explainability,
healthcare
ecosystem
barriers
limiting
real-world
translation.
The
most
significant
gap
centers
on
treatment-oriented
AI
applications
provide
real-time
guidance
urgent
interventions
rather
than
just
analytical
support.
Keywords:
intelligence,
machine-learning,
medicine,
traumatology
Язык: Английский
TriageIntelli: AI-Assisted Multimodal Triage System for Health Centers
Procedia Computer Science,
Год журнала:
2024,
Номер
251, С. 430 - 437
Опубликована: Янв. 1, 2024
Язык: Английский
Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning
Nursing Reports,
Год журнала:
2024,
Номер
14(4), С. 4162 - 4172
Опубликована: Дек. 20, 2024
Background:
Optimum
efficiency
and
responsiveness
to
callers
of
mental
health
helplines
can
only
be
achieved
if
call
priority
is
accurately
identified.
Currently,
operators
making
a
triage
assessment
rely
heavily
on
their
clinical
judgment
experience.
Due
the
significant
morbidity
mortality
associated
with
illness,
there
an
urgent
need
identify
who
have
high
level
distress
seen
by
clinician
offer
interventions
for
treatment.
This
study
delves
into
potential
using
machine
learning
(ML)
estimate
from
properties
callers’
voices
rather
than
evaluating
spoken
words.
Method:
Phone
speech
first
isolated
existing
APIs,
then
features
or
representations
are
extracted
raw
speech.
These
fed
series
deep
neural
networks
classify
audio
representation.
Results:
Development
network
architecture
that
instantly
determines
positive
negative
levels
in
input
segments.
A
total
459
records
helpline
were
investigated.
The
final
ML
model
balanced
accuracy
92%
correct
identification
both
instances
priority.
Conclusions:
provides
voice
quality
terms
demeanor
simultaneously
displayed
web
interface
computer
smartphone.
Язык: Английский