L'État
de
la
situation
sur
les
impacts
sociétaux
l'intelligence
artificielle
et
du
numérique
fait
état
des
connaissances
actuelles
l'IA
numérique,
structurées
autour
sept
axes
recherche
l'Obvia
:
santé,
éducation,
travail
emploi,
éthique
gouvernance,
droit,
arts
médias,
transition
socio-écologique.
Hypertrucages,
désinformation,
empreinte
environnementale,
droit
d'auteur,
évolution
conditions
travail…
Le
document
recense
grandes
questions
soulevées
par
le
déploiement
progressif
ces
nouvelles
technologies,
auxquelles
viennent
s'ajouter
cas
d'usages
pistes
d'action.
Il
s'impose
ainsi
comme
un
outil
complet
indispensable
pour
accompagner
prise
décision
dans
tous
secteurs
bouleversés
changements.
Journal of Advanced Nursing,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 4, 2025
ABSTRACT
Aim
To
explore
nursing
students'
perceptions
and
experiences
of
using
large
language
models
identify
the
facilitators
barriers
by
applying
Theory
Planned
Behaviour.
Design
A
qualitative
descriptive
design.
Method
Between
January
June
2024,
we
conducted
individual
semi‐structured
online
interviews
with
24
students
from
13
medical
universities
across
China.
Participants
were
recruited
purposive
snowball
sampling
methods.
Interviews
in
Mandarin.
Data
analysed
through
directed
content
analysis.
Results
Analysis
revealed
10
themes
according
to
3
constructs
Behaviour:
(a)
attitude:
perceived
value
expectations
facilitators,
while
caution
posed
barriers;
(b)
subjective
norm:
media
effects
role
model
effectiveness
described
as
whereas
organisational
pressure
exerted
universities,
research
institutions
hospitals
acted
a
barrier
usage;
(c)
behavioural
control:
design
free
access
strong
incentives
for
use,
geographic
restrictions
digital
literacy
deficiencies
key
factors
hindering
adoption.
Conclusion
This
study
explored
attitudes,
norms
control
regarding
use
models.
The
findings
provided
valuable
insights
into
that
hindered
or
facilitated
Implications
Profession
Through
lens
this
study,
have
enhanced
knowledge
journey
models,
which
contributes
implementation
management
these
tools
education.
Impact
There
is
gap
literature
views
influence
their
usage,
addresses.
These
could
provide
evidence‐based
support
nurse
educators
formulate
strategies
guidelines.
Reporting
adheres
consolidated
criteria
reporting
(COREQ)
checklist.
Public
Contribution
No
patient
public
contribution.
Management Decision,
Год журнала:
2024,
Номер
unknown
Опубликована: Май 7, 2024
Purpose
The
aim
of
this
paper
is
to
explore
how
multi-national
corporations
(MNCs)
can
effectively
adopt
artificial
intelligence
(AI)
into
their
talent
acquisition
(TA)
practices.
While
the
potential
AI
address
emerging
challenges,
such
as
shortages
and
applicant
surges
in
specific
regions,
has
been
anecdotally
highlighted,
there
limited
empirical
evidence
regarding
its
effective
deployment
adoption
TA.
As
a
result,
endeavors
develop
theoretical
model
that
delineates
motives,
barriers,
procedural
steps
critical
factors
aid
TA
within
MNCs.
Design/methodology/approach
Given
scant
literature
on
our
research
objective,
we
utilized
qualitative
methodology,
encompassing
multiple-case
study
(consisting
19
cases
across
seven
industries)
grounded
theory
approach.
Findings
Our
proposed
framework,
termed
Framework
Effective
Adoption
,
contextualizes
success
essential
for
Research
limitations/
implications
This
contributes
theory.
Practical
Additionally,
it
provides
guidance
managers
seeking
implementation
strategies,
especially
face
challenges.
Originality/value
To
best
authors'
knowledge,
unparalleled,
being
both
based
an
expansive
dataset
spans
firms
from
various
regions
industries.
delves
deeply
corporations'
underlying
motives
processes
concerning
Abstract
Machine
learning
(ML)
and
nanotechnology
interfacing
are
exploring
opportunities
for
cancer
treatment
strategies.
To
improve
therapy,
this
article
investigates
the
synergistic
combination
of
Graphene
Oxide
(GO)‐based
devices
with
ML
techniques.
The
production
techniques
functionalization
tactics
used
to
modify
physicochemical
characteristics
GO
specific
drug
delivery
explained
at
outset
investigation.
is
a
great
option
treating
because
its
natural
biocompatibility
capacity
absorb
medicinal
chemicals.
Then,
complicated
biological
data
analyzed
using
algorithms,
which
make
it
possible
identify
best
medicine
formulations
individualized
plans
depending
on
each
patient's
particular
characteristics.
study
also
looks
optimizing
predicting
interactions
between
carriers
cells
ML.
Predictive
modeling
helps
ensure
effective
payload
release
therapeutic
efficacy
in
design
customized
systems.
Furthermore,
tracking
outcomes
real
time
made
by
permit
adaptive
modifications
therapy
regimens.
By
medication
doses
settings,
not
only
decreases
adverse
effects
but
enhances
accuracy.
BMC Anesthesiology,
Год журнала:
2025,
Номер
25(1)
Опубликована: Янв. 3, 2025
In
medicine,
Artificial
intelligence
has
begun
to
be
utilized
in
nearly
every
domain,
from
medical
devices
the
interpretation
of
imaging
studies.
There
is
still
a
need
for
more
experience
and
studies
related
comprehensive
use
AI
medicine.
The
aim
present
study
evaluate
ability
make
decisions
regarding
anesthesia
methods
compare
most
popular
programs
this
perspective.
included
orthopedic
patients
over
18
years
age
scheduled
limb
surgery
within
1-month
period.
Patients
classified
as
ASA
I-III
who
were
evaluated
clinic
during
preoperative
period
study.
method
preferred
by
anesthesiologist
operation
patient's
demographic
data,
comorbidities,
medications,
surgical
history
recorded.
obtained
patient
data
discussed
if
presenting
scenario
using
free
versions
ChatGPT,
Copilot,
Gemini
applications
different
did
not
perform
operation.
Over
course
1
month,
total
72
enrolled
It
was
observed
that
both
specialists
application
chose
spinal
same
68.5%
cases.
This
rate
higher
compared
other
applications.
For
taking
medication,
it
presented
choices
highly
compatible
(85.7%)
with
anesthesiologists'
preferences.
cannot
fully
master
guidelines
exceptional
specific
cases
arrive
treatment.
Thus,
we
believe
can
serve
valuable
assistant
rather
than
replacing
doctors.
Medical
students,
as
future
healthcare
professionals,
are
pivotal
in
the
adoption
and
application
of
artificial
intelligence
(AI)
clinical
settings.
Their
ability
to
effectively
engage
with
AI
technologies
is
shaped
by
their
understanding,
attitudes,
perceived
significance
medicine.
Given
growing
prominence
medical
field,
it
crucial
evaluate
how
well-prepared
students
integrate
use
these
proficiently.
The
cross-sectional
study
was
conducted
among
482
undergraduate
at
a
college
Central
India
objective
readiness
for
integration
into
practice,
utilizing
Artificial
Intelligence
Readiness
Scale
Students
(MAIRS-MS)
questionnaire.
mean
age
respondents
21.39
±
1.770
years
282
(58.5%)
male
participants.
were
almost
equally
distributed
all
Bachelor
Medicine
Surgery
(MBBS)
batch
students.
average
MAIRS-MS
score
came
out
be
74.61
10.137
maximum
110,
whereas
values
various
subscales
follows:
Cognition
Factor,
26.23
4.417;
Ability
27.62
4.372;
Vision
10.37
1.803;
Ethics
10.39
1.789.
Although
there
overall
respondents,
significant
variation
exists
individuals,
especially
areas
Ability.
data
highlights
necessity
focused
educational
programs
improve
knowledge,
skills,
ethical
ensuring
that
every
respondent
well-equipped
handle
advancing
field
Medical Care,
Год журнала:
2025,
Номер
63(3), С. 227 - 233
Опубликована: Янв. 3, 2025
Objective:
To
understand
the
variation
in
artificial
intelligence/machine
learning
(AI/ML)
adoption
across
different
hospital
characteristics
and
explore
how
AI/ML
is
utilized,
particularly
relation
to
neighborhood
deprivation.
Background:
AI/ML-assisted
care
coordination
has
potential
reduce
health
disparities,
but
there
a
lack
of
empirical
evidence
on
AI’s
impact
equity.
Methods:
We
used
linked
datasets
from
2022
American
Hospital
Association
Annual
Survey
2023
Information
Technology
Supplement.
The
data
were
further
Area
Deprivation
Index
(ADI)
for
each
hospital’s
service
area.
State
fixed-effect
regressions
employed.
A
decomposition
model
was
also
quantify
predictors
implementation,
comparing
hospitals
higher
versus
lower
ADI
areas.
Results:
Hospitals
serving
most
vulnerable
areas
(ADI
Q4)
significantly
less
likely
apply
ML
or
other
predictive
models
(coef
=
−0.10,
P
0.01)
provided
fewer
AI/ML-related
workforce
applications
-0.40,
0.01),
compared
with
those
least
Decomposition
results
showed
that
our
specifications
explained
79%
between
Q4
Q1–Q3.
In
addition,
Accountable
Care
Organization
affiliation
accounted
12%–25%
differences
utilization
various
measures.
Conclusions:
underuse
economically
disadvantaged
rural
areas,
management
electronic
record
suggests
these
communities
may
not
fully
benefit
advancements
AI-enabled
care.
Our
indicate
value-based
payment
could
be
strategically
support
AI
integration.
