Health Care Management Review,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 27, 2025
Issue
The
digital
transformation
of
the
U.S.
health
care
system
is
underway,
but
role
chief
information
officers
(HCIOs)
in
that
has
been
unclear.
As
landscape
technology
continues
to
expand,
there
an
increasing
need
understand
influence
HCIOs,
who
are
a
unique
position
impact
key
strategic
decisions.
We
seek
demonstrate
importance
HCIOs
meeting
needs
transformation,
by
managing
emergence
and
implementation
technologies
benefit
organization
performance.
also
propose
profession-based
stereotypes
inhibit
as
they
may
be
viewed
behind-the-scenes
technicians
rather
than
leaders.
Critical
Theoretical
Analysis
Upper
echelons
(UE)
theory
demonstrates
how
HCIOs'
perspectives
gained
through
education,
experience,
decision-making
process
can
organizational
build
on
UE
conceptualize
degree
which
moderate
top
management
teams).
Implications
present
two
theoretical
contributions.
First,
we
introduce
stereotype
moderated
model
specific
HCIOs.
Second,
offer
analysis
leaders
era.
Practice
call
upon
scholars
practitioners
examine
their
roles
decision-making,
team
interactions,
outcomes
continues.
Asian Journal of Research in Computer Science,
Год журнала:
2024,
Номер
17(5), С. 85 - 107
Опубликована: Март 8, 2024
The
study
investigates
data
governance
challenges
within
AI-enabled
healthcare
systems,
focusing
on
Project
Nightingale
as
a
case
to
elucidate
the
complexities
of
balancing
technological
advancements
with
patient
privacy
and
trust.
Utilizing
survey
methodology,
were
collected
from
843
service
users
employing
structured
questionnaire
designed
measure
perceptions
AI
in
healthcare,
trust
providers,
concerns
about
privacy,
impact
regulatory
frameworks
adoption
technologies.
reliability
instrument
was
confirmed
Cronbach's
Alpha
0.81,
indicating
high
internal
consistency.
multiple
regression
analysis
revealed
significant
findings:
positive
relationship
between
awareness
projects
countered
by
negative
Additionally,
familiarity
perceived
effectiveness
positively
correlated
data,
while
constraints
issues
identified
barriers
effective
technologies
healthcare.
highlights
critical
need
for
enhanced
transparency,
public
awareness,
robust
navigate
ethical
associated
recommends
adopting
flexible,
principle-based
approaches
fostering
multi-stakeholder
collaboration
ensure
deployment
that
prioritize
welfare
BMC Medical Education,
Год журнала:
2024,
Номер
24(1)
Опубликована: Май 7, 2024
Implementing
digital
transformation
and
artificial
intelligence
(AI)
in
education
practice
necessitates
understanding
nursing
students'
attitudes
behaviors
as
end-users
toward
current
future
AI
applications.
Journal of Imaging,
Год журнала:
2024,
Номер
10(4), С. 81 - 81
Опубликована: Март 28, 2024
Computer
vision
(CV),
a
type
of
artificial
intelligence
(AI)
that
uses
digital
videos
or
sequence
images
to
recognize
content,
has
been
used
extensively
across
industries
in
recent
years.
However,
the
healthcare
industry,
its
applications
are
limited
by
factors
like
privacy,
safety,
and
ethical
concerns.
Despite
this,
CV
potential
improve
patient
monitoring,
system
efficiencies,
while
reducing
workload.
In
contrast
previous
reviews,
we
focus
on
end-user
CV.
First,
briefly
review
categorize
other
(job
enhancement,
surveillance
automation,
augmented
reality).
We
then
developments
hospital
setting,
outpatient,
community
settings.
The
advances
monitoring
delirium,
pain
sedation,
deterioration,
mechanical
ventilation,
mobility,
surgical
applications,
quantification
workload
hospital,
for
events
outside
highlighted.
To
identify
opportunities
future
also
completed
journey
mapping
at
different
levels.
Lastly,
discuss
considerations
associated
with
outline
processes
algorithm
development
testing
limit
expansion
healthcare.
This
comprehensive
highlights
ideas
expanded
use
Computers in Biology and Medicine,
Год журнала:
2025,
Номер
189, С. 109834 - 109834
Опубликована: Март 1, 2025
This
paper
presents
a
comprehensive
systematic
review
of
generative
models
(GANs,
VAEs,
DMs,
and
LLMs)
used
to
synthesize
various
medical
data
types,
including
imaging
(dermoscopic,
mammographic,
ultrasound,
CT,
MRI,
X-ray),
text,
time-series,
tabular
(EHR).
Unlike
previous
narrowly
focused
reviews,
our
study
encompasses
broad
array
modalities
explores
models.
Our
aim
is
offer
insights
into
their
current
future
applications
in
research,
particularly
the
context
synthesis
applications,
generation
techniques,
evaluation
methods,
as
well
providing
GitHub
repository
dynamic
resource
for
ongoing
collaboration
innovation.
search
strategy
queries
databases
such
Scopus,
PubMed,
ArXiv,
focusing
on
recent
works
from
January
2021
November
2023,
excluding
reviews
perspectives.
period
emphasizes
advancements
beyond
GANs,
which
have
been
extensively
covered
reviews.
The
survey
also
aspect
conditional
generation,
not
similar
work.
Key
contributions
include
broad,
multi-modality
scope
that
identifies
cross-modality
opportunities
unavailable
single-modality
surveys.
While
core
techniques
are
transferable,
we
find
methods
often
lack
sufficient
integration
patient-specific
context,
clinical
knowledge,
modality-specific
requirements
tailored
unique
characteristics
data.
Conditional
leveraging
textual
conditioning
multimodal
remain
underexplored
but
promising
directions
findings
structured
around
three
themes:
(1)
Synthesis
highlighting
clinically
valid
significant
gaps
using
synthetic
augmentation,
validation
evaluation;
(2)
Generation
identifying
personalization
innovation;
(3)
Evaluation
revealing
absence
standardized
benchmarks,
need
large-scale
validation,
importance
privacy-aware,
relevant
frameworks.
These
emphasize
benchmarking
comparative
studies
promote
openness
collaboration.
Behavioral Sciences,
Год журнала:
2025,
Номер
15(1), С. 88 - 88
Опубликована: Янв. 18, 2025
This
study
examines
how
the
use
of
artificial
intelligence
(AI)
by
healthcare
professionals
affects
their
work
well-being
through
satisfaction
basic
psychological
needs,
framed
within
Self-Determination
Theory.
Data
from
280
across
various
departments
in
Chinese
hospitals
were
collected,
and
hierarchical
regression
analyzed
to
assess
relationship
between
AI,
needs
(autonomy,
competence,
relatedness),
well-being.
The
results
reveal
that
AI
enhances
indirectly
increasing
these
needs.
Additionally,
job
complexity
serves
as
a
boundary
condition
moderates
Specifically,
weakens
autonomy
while
having
no
significant
effect
on
relatedness.
These
findings
suggest
impact
professionals’
is
contingent
complexity.
highlights
promoting
at
context
adoption
requires
not
only
technological
implementation
but
also
ongoing
adaptation
meet
evolving
insights
provide
theoretical
foundation
practical
guidance
for
integrating
into
support
professionals.
Health care science,
Год журнала:
2024,
Номер
3(5), С. 329 - 349
Опубликована: Окт. 1, 2024
Abstract
The
increasing
integration
of
new
technologies
is
driving
a
fundamental
revolution
in
the
healthcare
sector.
Developments
artificial
intelligence
(AI),
machine
learning,
and
big
data
analytics
have
completely
transformed
diagnosis,
treatment,
care
patients.
AI‐powered
solutions
are
enhancing
efficiency
accuracy
delivery
by
demonstrating
exceptional
skills
personalized
medicine,
early
disease
detection,
predictive
analytics.
Furthermore,
telemedicine
remote
patient
monitoring
systems
overcome
geographical
constraints,
offering
easy
accessible
services,
particularly
underserved
areas.
Wearable
technology,
Internet
Medical
Things,
sensor
empowered
individuals
to
take
an
active
role
tracking
managing
their
health.
These
devices
facilitate
real‐time
collection,
enabling
preventive
care.
Additionally,
development
3D
printing
technology
has
revolutionized
medical
field
production
customized
prosthetics,
implants,
anatomical
models,
significantly
impacting
surgical
planning
treatment
strategies.
Accepting
these
advancements
holds
potential
create
more
patient‐centered,
efficient
system
that
emphasizes
individualized
care,
better
overall
health
outcomes.
This
review's
novelty
lies
exploring
how
radically
transforming
industry,
paving
way
for
effective
all.
It
highlights
capacity
modern
revolutionize
addressing
long‐standing
challenges
improving
Although
approval
use
digital
advanced
analysis
face
scientific
regulatory
obstacles,
they
translational
research.
as
continue
evolve,
poised
alter
environment,
sustainable,
efficient,
ecosystem
future
generations.
Innovation
across
multiple
fronts
will
shape
revolutionizing
provision
healthcare,
outcomes,
equipping
both
patients
professionals
with
tools
make
decisions
receive
treatment.
As
develop
become
integrated
into
standard
practices,
probably
be
accessible,
effective,
than
ever
before.
Applied Sciences,
Год журнала:
2024,
Номер
14(5), С. 2132 - 2132
Опубликована: Март 4, 2024
The
advancement
of
machine
learning
in
healthcare
offers
significant
potential
for
enhancing
disease
prediction
and
management.
This
study
harnesses
the
PyCaret
library—a
Python-based
toolkit—to
construct
refine
predictive
models
diagnosing
diabetes
mellitus
forecasting
hospital
readmission
rates.
By
analyzing
a
rich
dataset
featuring
variety
clinical
demographic
variables,
we
endeavored
to
identify
patients
at
heightened
risk
complications
leading
readmissions.
Our
methodology
incorporates
an
evaluation
numerous
algorithms,
emphasizing
their
accuracy
generalizability
improve
patient
care.
We
scrutinized
strength
each
model
concerning
crucial
metrics
like
accuracy,
precision,
recall,
area
under
curve,
underlining
imperative
eliminate
false
diagnostics
field.
Special
attention
is
given
use
light
gradient
boosting
classifier
among
other
advanced
modeling
techniques,
which
emerge
as
particularly
effective
terms
Kappa
statistic
Matthews
correlation
coefficient,
suggesting
robustness
prediction.
paper
discusses
implications
management,
underscoring
interventions
lifestyle
changes
pharmacological
treatments
avert
long-term
complications.
Through
exploring
intersection
health
informatics,
reveals
pivotal
insights
into
algorithmic
predictions
readmission.
It
also
emphasizes
necessity
further
research
development
fully
incorporate
modern
care
prompt
timely
achieve
better
overall
outcomes.
outcome
this
testament
transformative
impact
automated
realm
analytics.
Journal of Informatics Education and Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
Explainable
AI
(XAI)
is
one
of
the
key
game-changing
features
in
machine
learning
models,
which
contribute
to
making
them
more
transparent,
regulated
and
usable
different
applications.
In
(the)
investigation
this
paper,
we
consider
four
rows
explanation
methods—LIME,
SHAP,
Anchor,
Decision
Tree-based
Explanation—in
disentangling
decision-making
process
black
box
models
within
fields.
our
experiments,
use
datasets
that
cover
domains,
for
example,
health,
finance
image
classification,
compare
accuracy,
fidelity,
coverage,
precision
human
satisfaction
each
method.
Our
work
shows
rule
trees
approach
called
(Decision
explanation)
mostly
superior
comparison
other
non-model-specific
methods
performing
higher
coverage
regardless
classifier.
addition
this,
respondents
who
answered
qualitative
evaluation
indicated
they
were
very
content
with
decision
tree-based
explanations
these
types
are
easy
understandable.
Furthermore,
most
famous
sorts
clarifications
instinctive
significant.
The
over
discoveries
stretch
on
utilize
interpretable
strategies
facilitating
hole
between
understanding
thus
advancing
straightforwardness
responsibility
AI-driven
decision-making.
