Brain Sciences,
Год журнала:
2025,
Номер
15(1), С. 47 - 47
Опубликована: Янв. 6, 2025
Background/Objectives:
The
evolution
of
digital
technology
enhances
the
broadening
a
person's
intellectual
growth.
Research
points
out
that
implementing
innovative
applications
world
improves
human
social,
cognitive,
and
metacognitive
behavior.
Artificial
intelligence
chatbots
are
yet
another
human-made
construct.
These
forms
software
simulate
conversation,
understand
process
user
input,
provide
personalized
responses.
Executive
function
includes
set
higher
mental
processes
necessary
for
formulating,
planning,
achieving
goal.
present
study
aims
to
investigate
executive
reinforcement
through
artificial
chatbots,
outlining
potentials,
limitations,
future
research
suggestions.
Specifically,
examined
three
questions:
use
conversational
in
functioning
training,
their
impact
on
executive-cognitive
skills,
duration
any
improvements.
Methods:
assessment
existing
literature
was
implemented
using
systematic
review
method,
according
PRISMA
2020
Principles.
avalanche
search
method
employed
conduct
source
following
databases:
Scopus,
Web
Science,
PubMed,
complementary
Google
Scholar.
This
included
studies
from
2021
experimental,
observational,
or
mixed
methods.
It
AI-based
conversationalists
support
functions,
such
as
anxiety,
stress,
depression,
memory,
attention,
cognitive
load,
behavioral
changes.
In
addition,
this
general
populations
with
specific
neurological
conditions,
all
peer-reviewed,
written
English,
full-text
access.
However,
excluded
before
2021,
reviews,
non-AI-based
conversationalists,
not
targeting
range
skills
abilities,
without
open
criteria
aligned
objectives,
ensuring
focus
AI
agents
function.
initial
collection
totaled
n
=
115
articles;
however,
eligibility
requirements
led
final
selection
10
studies.
Results:
findings
suggested
positive
effects
enhance
improve
skills.
Although,
several
limitations
were
identified,
making
it
still
difficult
generalize
reproduce
effects.
Conclusions:
an
tool
can
assistant
learning
expanding
contributing
metacognitive,
social
development
individual.
its
training
is
at
primary
stage.
highlighted
need
unified
framework
reference
studies,
better
designs,
diverse
populations,
larger
sample
sizes
participants,
longitudinal
observe
long-term
use.
Generative
Artificial
Intelligence
(GAI)
has
driven
several
advancements
in
healthcare,
with
large
language
models
(LLMs)
such
as
OpenAI's
ChatGPT,
Google's
Gemini,
and
Microsoft's
Copilot
demonstrating
potential
clinical
decision
support,
medical
education,
research
acceleration.
However,
their
closed-source
architecture,
high
computational
costs,
limited
adaptability
to
specialized
contexts
remained
key
barriers
universal
adoption.
Now,
the
rise
of
DeepSeek's
DeepThink
(R1),
an
open-source
LLM,
gaining
prominence
since
mid-January
2025,
new
opportunities
challenges
emerge
for
healthcare
integration
AI-driven
research.
Unlike
proprietary
models,
DeepSeek
fosters
continuous
learning
by
leveraging
publicly
available
datasets,
possibly
enhancing
ever-evolving
knowledge
scientific
reasoning.
Its
transparent,
community-driven
approach
may
enable
greater
customization,
regional
specialization,
collaboration
among
data
researchers
clinicians.
Additionally,
supports
offline
deployment,
addressing
some
privacy
concerns.
Despite
these
promising
advantages,
presents
ethical
regulatory
challenges.
Users'
worries
have
emerged,
concerns
about
user
retention
policies
developer
access
user-generated
content
without
opt-out
options.
when
used
applications,
its
compliance
China's
data-sharing
regulations
highlights
urgent
need
clear
international
governance.
Furthermore,
like
other
LLMs,
face
limitations
related
inherent
biases,
hallucinations,
output
reliability,
which
warrants
rigorous
validation
human
oversight
before
application.
This
editorial
explores
role
workflows,
while
also
highlighting
security,
accuracy,
responsible
AI
With
careful
implementation,
considerations,
collaboration,
similar
LLMs
could
enhance
innovation,
providing
cost-effective,
scalable
solutions
ensuring
expertise
remains
at
forefront
patient
care.
JMIR Medical Education,
Год журнала:
2025,
Номер
11, С. e63400 - e63400
Опубликована: Фев. 20, 2025
Background
With
the
rapid
development
of
artificial
intelligence
technologies,
there
is
a
growing
interest
in
potential
use
intelligence–based
tools
like
ChatGPT
medical
education.
However,
limited
research
on
initial
perceptions
and
experiences
faculty
students
with
ChatGPT,
particularly
Saudi
Arabia.
Objective
This
study
aimed
to
explore
earliest
knowledge,
perceived
benefits,
concerns,
limitations
using
education
among
at
leading
Arabian
university.
Methods
A
qualitative
exploratory
was
conducted
April
2023,
involving
focused
meetings
varying
levels
experience.
thematic
analysis
used
identify
key
themes
subthemes
emerging
from
discussions.
Results
Participants
demonstrated
good
knowledge
its
functions.
The
main
were
use,
concerns
about
benefits
included
collecting
summarizing
information
saving
time
effort.
centered
around
lack
critical
thinking
provided,
ambiguity
references,
access,
trust
output
ethical
concerns.
Conclusions
provides
valuable
insights
into
regarding
newly
introduced
large
language
models
While
recognized,
participants
also
expressed
requiring
further
studies
for
effective
integration
education,
exploring
impact
learning
outcomes,
student
satisfaction,
skills.
This
editorial
explores
the
recent
advancements
in
generative
artificial
intelligence
with
newly-released
OpenAI
o1-Preview,
comparing
its
capabilities
to
traditional
ChatGPT
(GPT-4)
model,
particularly
context
of
healthcare.
While
has
shown
many
applications
for
general
medical
advice
and
patient
interactions,
o1-Preview
introduces
new
features
advanced
reasoning
skills
using
a
The
rapid
evolution
of
generative
artificial
intelligence
(AI)
has
introduced
transformative
technologies
across
various
domains,
with
text-to-video
(T2V)
generation
models
emerging
as
innovations
in
the
field.
This
narrative
review
explores
potential
T2V
AI
used
healthcare,
focusing
on
their
applications,
challenges,
and
future
directions.
Advanced
platforms,
such
Sora
Turbo
(OpenAI,
Inc.,
San
Francisco,
California,
United
States)
Veo
2
(Google
LLC,
Mountain
View,
States),
both
announced
December
2024,
offer
capability
to
generate
high-fidelity
video
contents.
Such
could
revolutionize
healthcare
by
providing
tailored
videos
for
patient
education,
enhancing
medical
training,
possibly
optimizing
telemedicine.
We
conducted
a
comprehensive
literature
search
databases
including
PubMed
Google
Scholar,
identified
41
relevant
studies
published
between
2020
2024.
Our
findings
reveal
significant
possible
benefits
improving
standardizing
customized
remote
consultations.
However,
critical
challenges
persist,
risks
misinformation
(or
deepfake),
privacy
breaches,
ethical
concerns,
limitations
authenticity.
Detection
mechanisms
deepfakes
regulatory
frameworks
remain
underdeveloped,
necessitating
further
interdisciplinary
research
vigilant
policy
development.
Future
advancements
enable
real-time
visualizations
augmented
reality
training.
achieving
these
will
require
addressing
accessibility
ensure
equitable
implementation
prevent
disparities.
By
fostering
collaboration
among
stakeholders,
systems
technologists,
transform
global
into
more
effective,
universal,
innovative
system
while
safeguarding
against
its
misuse.
JMIR Nursing,
Год журнала:
2025,
Номер
8, С. e63058 - e63058
Опубликована: Фев. 27, 2025
Abstract
Background
The
health
care
sector
faces
a
projected
shortfall
of
10
million
workers
by
2030.
Artificial
intelligence
(AI)
automation
in
areas
such
as
patient
education
and
initial
therapy
screening
presents
strategic
response
to
mitigate
this
shortage
reallocate
medical
staff
higher-priority
tasks.
However,
current
methods
evaluating
early-stage
AI
chatbots
are
highly
limited
due
safety
concerns
the
amount
time
effort
that
goes
into
them.
Objective
This
study
introduces
novel
3-bot
method
for
efficiently
testing
validating
provider
chatbots.
To
extensively
test
without
involving
real
patients
or
researchers,
various
bots
an
evaluator
bot
were
developed.
Methods
Provider
interacted
with
embodying
frustrated,
anxious,
depressed
personas.
An
reviewed
interaction
transcripts
based
on
specific
criteria.
Human
experts
then
each
transcript,
bot’s
results
compared
human
evaluation
ensure
accuracy.
Results
patient-education
evaluations
nearly
identical,
minimal
variance,
limiting
opportunity
further
analysis.
also
yielded
similar
between
evaluator.
Statistical
analysis
confirmed
reliability
accuracy
evaluations.
Conclusions
innovative
ensures
safe,
adaptable,
effective
means
refine
early
versions
risking
investing
excessive
researcher
effort.
Our
could
have
benefitted
from
larger
criteria,
we
had
extremely
evaluators,
which
arisen
because
small
number
We
prompting
input
practical
consideration
increases
prompts.
In
future,
using
techniques
retrieval
augmented
generation
will
allow
system
receive
more
information
become
accurate
rapid
validation
automate
basic
tasks,
freeing
providers
address
complex
BMC Medical Education,
Год журнала:
2025,
Номер
25(1)
Опубликована: Март 19, 2025
To
assess
the
ability
of
General
Practice
(GP)
Trainees
to
detect
AI-generated
hallucinations
in
simulated
clinical
practice,
ChatGPT-4o
was
utilized.
The
were
categorized
into
three
types
based
on
accuracy
answers
and
explanations:
(1)
correct
with
incorrect
or
flawed
explanations,
(2)
explanations
that
contradict
factual
evidence,
(3)
explanations.
This
multi-center,
cross-sectional
survey
study
involved
142
GP
Trainees,
all
whom
undergoing
Specialist
Training
volunteered
participate.
evaluated
consistency
ChatGPT-4o,
as
well
Trainees'
response
time,
accuracy,
sensitivity
(d'),
tendencies
(β).
Binary
regression
analysis
used
explore
factors
affecting
identify
errors
generated
by
ChatGPT-4o.
A
total
137
participants
included,
a
mean
age
25.93
years.
Half
unfamiliar
AI,
35.0%
had
never
it.
ChatGPT-4o's
overall
80.8%,
which
slightly
decreased
80.1%
after
human
verification.
However,
for
professional
practice
(Subject
4)
only
57.0%,
verification,
it
dropped
further
44.2%.
87
identified,
primarily
occurring
at
application
evaluation
levels.
detecting
these
55.0%,
(d')
0.39.
Regression
revealed
shorter
times
(OR
=
0.92,
P
0.02),
higher
self-assessed
AI
understanding
0.16,
0.04),
more
frequent
use
10.43,
0.01)
associated
stricter
error
detection
criteria.
concluded
trainees
faced
challenges
identifying
errors,
particularly
scenarios.
highlights
importance
improving
literacy
critical
thinking
skills
ensure
effective
integration
medical
education.
Current Opinion in Urology,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 19, 2025
Purpose
of
review
The
integration
artificial
intelligence
(AI)
into
healthcare
has
significantly
impacted
the
way
is
delivered,
particularly
with
generative
AI-powered
chatbots.
This
aims
to
provide
an
analysis
application,
benefits,
challenges
and
future
chatbots
in
Urology.
Recent
findings
advancements
AI
have
led
significant
improvements
chatbot
performance
applicability
healthcare.
Generative
shown
promise
patient
education,
symptom
assessment,
administrative
tasks,
clinical
decision-making
urology.
Studies
demonstrate
their
ability
reduce
clinic
burden,
improve
satisfaction,
enhance
accessibility.
However,
concerns
remain
about
accuracy,
data
privacy,
workflows.
Summary
Increasing
number
studies
urological
practice.
As
technology
advances,
likely
integrate
multiple
aspects
Concerns
will
need
be
examined
before
safe
implementation.
