Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions
The Lancet Public Health,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 1, 2025
Artificial
intelligence
(AI)
can
rapidly
analyse
large
and
complex
datasets,
extract
tailored
recommendations,
support
decision
making,
improve
the
efficiency
of
many
tasks
that
involve
processing
data,
text,
or
images.
As
such,
AI
has
potential
to
revolutionise
public
health
practice
research,
but
accompanying
challenges
need
be
addressed.
used
surveillance,
epidemiological
communication,
allocation
resources,
other
forms
making.
It
also
productivity
in
daily
work.
Core
its
widespread
adoption
span
equity,
accountability,
data
privacy,
for
robust
digital
infrastructures,
workforce
skills.
Policy
makers
must
acknowledge
regulatory
frameworks
covering
lifecycle
relevant
technologies
are
needed,
alongside
sustained
investment
infrastructure
development.
Public
institutions
play
a
key
part
advancing
meaningful
use
by
ensuring
their
staff
up
date
regarding
existing
provisions
ethical
principles
development
technologies,
thinking
about
how
prioritise
equity
design
implementation,
investing
systems
securely
process
volumes
needed
applications
governance
cybersecurity,
promoting
through
clear
guidelines
align
with
human
rights
good,
considering
AI's
environmental
impact.
Язык: Английский
Psychological Research Contributions to Urgent Global Health Challenges for the Next Decade
Опубликована: Янв. 1, 2025
Язык: Английский
Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents’ perspectives
BMJ Health & Care Informatics,
Год журнала:
2025,
Номер
32(1), С. e101456 - e101456
Опубликована: Май 1, 2025
Objectives
The
aim
of
this
study
was
to
compare
the
performances
residents
and
ChatGPT
in
answering
validated
questions
assess
paediatric
surgery
residents’
acceptance,
perceptions
readiness
integrate
artificial
intelligence
(AI)
into
clinical
practice.
Methods
We
conducted
a
cross-sectional
using
randomly
selected
cases
on
topics.
examined
acceptance
AI
before
after
comparing
their
results
ChatGPT’s
Unified
Theory
Acceptance
Use
Technology
2
(UTAUT2)
model.
Data
analysis
performed
Jamovi
V.2.4.12.0.
Results
30
participated.
ChatGPT-4.0’s
median
score
13.75,
while
ChatGPT-3.5’s
8.75.
among
8.13.
Differences
appeared
statistically
significant.
outperformed
specifically
definition
(ChatGPT-4.0
vs
residents,
p<0.0001;
ChatGPT-3.5
p=0.03).
In
UTAUT2
Questionnaire,
respondents
expressed
more
positive
evaluation
with
higher
mean
values
for
each
construct
lower
fear
technology
learning
about
test
scores.
Discussion
better
than
knowledge-based
simple
cases.
accuracy
declined
when
confronted
complex
questions.
UTAUT
questionnaire
showed
that
potential
could
lead
shift
perception,
resulting
attitude
towards
AI.
Conclusion
Our
reveals
receptivity
AI,
especially
being
its
efficacy.
These
highlight
importance
integrating
AI-related
topics
medical
curricula
residency
help
future
physicians
surgeons
understand
advantages
limitations
Язык: Английский