BMC Health Services Research,
Год журнала:
2024,
Номер
24(1)
Опубликована: Окт. 25, 2024
The
use
of
Artificial
Intelligence
(AI)
tools
in
hospital
management
holds
potential
for
enhancing
decision-making
processes.
This
study
investigates
the
current
state
management,
explores
benefits
AI
integration,
and
examines
managers'
perceptions
as
a
decision-support
tool.
A
descriptive
exploratory
was
conducted
using
qualitative
approach.
Data
were
collected
through
semi-structured
interviews
with
15
managers
from
various
departments
institutions.
transcribed,
anonymized,
analyzed
thematic
coding
to
identify
key
themes
patterns
responses.
Hospital
highlighted
inefficiencies
processes,
often
characterized
by
poor
communication,
isolated
decision-making,
limited
data
access.
traditional
like
spreadsheet
applications
business
intelligence
systems
remains
prevalent,
but
there
is
clear
need
more
advanced,
integrated
solutions.
Managers
expressed
both
optimism
skepticism
about
AI,
acknowledging
its
improve
efficiency
while
raising
concerns
privacy,
ethical
issues,
loss
human
empathy.
identified
challenges,
including
variability
technical
skills,
fragmentation,
resistance
change.
emphasized
importance
robust
infrastructure
adequate
training
ensure
successful
integration.
reveals
complex
landscape
where
are
balanced
significant
challenges
concerns.
Effective
integration
requires
addressing
technical,
ethical,
cultural
focus
on
maintaining
elements
decision-making.
seen
powerful
tool
support,
not
replace,
judgment
promising
improvements
efficiency,
accessibility,
analytical
capacity.
Preparing
healthcare
institutions
necessary
providing
specialized
crucial
maximizing
mitigating
associated
risks.
Bioengineering,
Год журнала:
2025,
Номер
12(3), С. 235 - 235
Опубликована: Фев. 26, 2025
AI-powered
medical
imaging
faces
persistent
challenges,
such
as
limited
datasets,
class
imbalances,
and
high
computational
costs.
To
overcome
these
barriers,
we
introduce
PixMed-Enhancer,
a
novel
conditional
GAN
that
integrates
the
ghost
module
into
its
encoder—a
pioneering
approach
achieves
efficient
feature
extraction
while
significantly
reducing
complexity
without
compromising
performance.
Our
method
features
hybrid
loss
function,
uniquely
combining
binary
cross-entropy
(BCE)
Structural
Similarity
Index
Measure
(SSIM),
to
ensure
pixel-level
precision
enhancing
perceptual
realism.
Additionally,
use
of
input
masks
offers
unparalleled
control
over
generation
tumor
features,
marking
breakthrough
in
fine-grained
dataset
augmentation
for
segmentation
diagnostic
tasks.
Rigorous
testing
on
diverse
datasets
establishes
PixMed-Enhancer
state-of-the-art
solution,
excelling
realism,
structural
fidelity,
efficiency.
robust
foundation
real-world
clinical
applications
AI-driven
imaging.
International Journal of Educational Technology in Higher Education,
Год журнала:
2025,
Номер
22(1)
Опубликована: Март 13, 2025
Abstract
Artificial
intelligence
(AI)
is
ushering
in
an
era
of
potential
transformation
various
fields,
especially
educational
communication
technologies,
with
tools
like
ChatGPT
and
other
generative
AI
(GenAI)
applications.
This
rapid
proliferation
adoption
GenAI
have
sparked
significant
interest
concern
among
college
professors,
who
are
dealing
evolving
dynamics
digital
within
the
classroom.
Yet,
effect
implications
education
remain
understudied.
Therefore,
this
study
employs
Technology
Acceptance
Model
(TAM)
Social
Cognitive
Theory
(SCT)
as
theoretical
frameworks
to
explore
higher
faculty’s
perceptions,
attitudes,
usage,
motivations,
underlying
factors
that
influence
their
or
rejection
tools.
A
survey
was
conducted
full-time
faculty
members
(
N
=
294)
recruited
from
two
mid-size
public
universities
US.
Results
found
professors’
perceived
usefulness
predicted
attitudes
intention
use
adopt
technology,
more
than
ease
use.
Trust
social
reinforcement
strongly
influenced
decisions
acted
mediators
better
understand
relationship
between
TAM
SCT.
Findings
emphasized
power
shaping
self-efficacy,
GenAI.
enhances
peer
affects
how
shapes
users’
willingness
whereas
self-efficacy
has
a
minimal
impact.
research
provides
valuable
insights
inform
policies
aimed
at
improving
experience
for
students
AI-driven
workforce.
International Journal of Innovation Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 26, 2025
Purpose
Using
Diffusion
of
Innovation
theory
as
the
theoretical
lens,
this
study
aims
to
explore
how
academics
perceive
about
uses
Generative
Artificial
Intelligence
in
academic
research.
Design/methodology/approach
A
netnographic
qualitative
content
analytic
approach
was
used,
using
public
comments
on
YouTube
tutorial
videos
instructing
artificial
intelligence
(AI)
tools
source
insight.
Findings
The
findings
revealed
themes
and
subthemes
based
key
concepts
theory.
Besides,
perceived
risk
price
value
are
two
emerged
themes,
which
crucial
for
AI
adoption
Research
limitations/implications
This
enriches
technology
literature
by
exploring
more
disruptive
technologies
Originality/value
provides
empirical
evidence
establishes
a
clearer
view
global
community
truly
integrate
into
their
daily
research
practices.
Systems,
Год журнала:
2025,
Номер
13(4), С. 268 - 268
Опубликована: Апрель 8, 2025
This
study
combines
innovation
resistance
theory,
the
stimulus–organism–response
(SOR)
framework,
and
job
demands–resources
model
to
facilitate
an
in-depth
exploration
of
barriers
faced
by
healthcare
professionals
psychological
responses
they
exhibit
when
adopting
AI-supported
technologies.
We
conducted
a
questionnaire
survey
obtained
296
valid
from
examine
relationship
between
technologies
AI
adoption
behavioral
intentions.
Using
SOR
framework
as
basis,
this
validated
serial
mediation
with
moderating
effects,
demonstrating
that
influenced
intentions
through
resource,
demand,
levels
employee
engagement.
Further,
sought
validate
age-moderated
resource
demand
in
employees
exhibiting
their
associated
The
results
indicated
resources,
demands,
engagement
serially
mediated
Additionally,
age
only
exhibited
significant
effects
on
demands
findings
can
aid
promoting
professionals,
generating
new
insights
broadening
scope
vision
existing
literature.
Hospital Topics,
Год журнала:
2025,
Номер
unknown, С. 1 - 14
Опубликована: Апрель 10, 2025
Diabetes
mellitus,
a
non-communicable
metabolic
disorder,
is
significant
global
health
concern,
with
rising
prevalence
rates
resulting
in
increased
economic
burdens
on
healthcare
systems.
Early
detection
and
diagnosis
are
crucial
for
preventing
severe
complications.
Artificial
Intelligence
(AI)
offers
immense
potential
to
revolutionize
diabetes
management
early
detection.
This
study
aims
understand
the
factors
influencing
medical
professionals'
adoption
of
AI-based
tools
intervention,
develop
predictive
models
identify
adopters
propose
Hub-and-Spoke
model
screening
South
India,
particularly
segments
predominantly
rice-based
diet.
By
leveraging
machine
learning
techniques,
identifies
key
demographic
professional
that
predict
AI
intent.
The
proposed
addresses
logistical
challenges
screening,
underserved
regions.
research
contributes
effort
combat
diabetes,
improve
outcomes,
optimize
resource
allocation.
