Apprehension toward generative artificial intelligence in healthcare: a multinational study among health sciences students
Frontiers in Education,
Год журнала:
2025,
Номер
10
Опубликована: Май 1, 2025
Background
In
the
recent
generative
artificial
intelligence
(genAI)
era,
health
sciences
students
(HSSs)
are
expected
to
face
challenges
regarding
their
future
roles
in
healthcare.
This
multinational
cross-sectional
study
aimed
confirm
validity
of
novel
FAME
scale
examining
themes
Fear,
Anxiety,
Mistrust,
and
Ethical
issues
about
genAI.
The
also
explored
extent
apprehension
among
HSSs
genAI
integration
into
careers.
Methods
was
based
on
a
self-administered
online
questionnaire
distributed
using
convenience
sampling.
survey
instrument
scale,
while
toward
assessed
through
modified
State-Trait
Anxiety
Inventory
(STAI).
Exploratory
confirmatory
factor
analyses
were
used
construct
scale.
Results
final
sample
comprised
587
mostly
from
Jordan
(31.3%),
Egypt
(17.9%),
Iraq
(17.2%),
Kuwait
(14.7%),
Saudi
Arabia
(13.5%).
Participants
included
studying
medicine
(35.8%),
pharmacy
(34.2%),
nursing
(10.7%),
dentistry
(9.5%),
medical
laboratory
(6.3%),
rehabilitation
(3.4%).
Factor
analysis
confirmed
reliability
Of
constructs,
Mistrust
scored
highest,
followed
by
Ethics.
participants
showed
generally
neutral
genAI,
with
mean
score
9.23
±
3.60.
multivariate
analysis,
significant
variations
observed
previous
ChatGPT
use,
faculty,
nationality,
expressing
highest
level
apprehension,
Kuwaiti
lowest.
Previous
use
correlated
lower
levels.
higher
agreement
Ethics
constructs
statistically
associations
apprehension.
Conclusion
revealed
notable
Arab
HSSs,
which
highlights
need
for
educational
curricula
that
blend
technological
proficiency
ethical
awareness.
Educational
strategies
tailored
discipline
culture
needed
ensure
job
security
competitiveness
an
AI-driven
future.
Язык: Английский
Autonomous Resilience: Advancing Data Engineering Through Self-Healing Pipelines and Generative
Lakshmi Srinivasarao Kothamasu
European Journal of Computer Science and Information Technology,
Год журнала:
2025,
Номер
13(28), С. 102 - 113
Опубликована: Апрель 15, 2025
This
article
explores
the
transformative
potential
of
self-healing
data
pipelines
enhanced
by
generative
artificial
intelligence
in
next-generation
engineering
environments.
The
integration
machine
learning
models
capable
predicting,
detecting,
and
autonomously
resolving
anomalies
represents
a
paradigm
shift
how
organizations
manage
their
infrastructure.
By
examining
both
technical
architecture
organizational
implications
these
systems,
demonstrates
can
significantly
reduce
operational
overhead
while
improving
quality
processing
reliability.
investigates
implementation
strategies
across
various
industry
contexts,
addressing
challenges
governance
considerations
that
emerge
when
deploying
such
systems.
suggests
adopting
experience
substantial
improvements
efficiency
integrity,
ultimately
enabling
more
sophisticated
data-driven
decision
making.
contributes
to
evolving
discourse
on
autonomous
systems
provides
framework
for
future
research
field
advanced
engineering.
Язык: Английский