AI as the Therapist: Student Insights on the Challenges of Using Generative AI for School Mental Health Frameworks
Behavioral Sciences,
Год журнала:
2025,
Номер
15(3), С. 287 - 287
Опубликована: Фев. 28, 2025
The
integration
of
generative
AI
(GenAI)
in
school-based
mental
health
services
presents
new
opportunities
and
challenges.
This
study
focuses
on
the
challenges
using
GenAI
chatbots
as
therapeutic
tools
by
exploring
secondary
school
students’
perceptions
such
applications.
data
were
collected
from
students
who
had
both
theoretical
practical
experience
with
GenAI.
Based
Grodniewicz
Hohol’s
framework
highlighting
“Problem
a
Confused
Therapist”,
Non-human
Narrowly
Intelligent
qualitative
student
reflections
examined
thematic
analysis.
findings
revealed
that
while
acknowledged
AI’s
benefits,
accessibility
non-judgemental
feedback,
they
expressed
significant
concerns
about
lack
empathy,
trust,
adaptability.
implications
underscore
need
for
chatbot
use
to
be
complemented
in-person
counselling,
emphasising
importance
human
oversight
AI-augmented
care.
contributes
deeper
understanding
how
advanced
can
ethically
effectively
incorporated
into
frameworks,
balancing
technological
potential
essential
interaction.
Язык: Английский
Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy
Digital Health,
Год журнала:
2025,
Номер
11
Опубликована: Март 1, 2025
Artificial
Intelligence
(AI)
has
demonstrated
significant
potential
in
transforming
psychiatric
care
by
enhancing
diagnostic
accuracy
and
therapeutic
interventions.
Psychiatry
faces
challenges
like
overlapping
symptoms,
subjective
methods,
personalized
treatment
requirements.
AI,
with
its
advanced
data-processing
capabilities,
offers
innovative
solutions
to
these
complexities.
This
study
systematically
reviewed
meta-analyzed
the
existing
literature
evaluate
AI's
efficacy
care,
focusing
on
various
disorders
AI
technologies.
Adhering
PRISMA
guidelines,
included
a
comprehensive
search
across
multiple
databases.
Empirical
studies
investigating
applications
psychiatry,
such
as
machine
learning
(ML),
deep
(DL),
hybrid
models,
were
selected
based
predefined
inclusion
criteria.
The
outcomes
of
interest
efficacy.
Statistical
analysis
employed
fixed-
random-effects
subgroup
sensitivity
analyses
exploring
impact
methodologies
designs.
A
total
14
met
criteria,
representing
diverse
diagnosing
treating
disorders.
pooled
was
85%
(95%
CI:
80%-87%),
ML
models
achieving
highest
accuracy,
followed
DL
models.
For
efficacy,
effect
size
84%
82%-86%),
excelling
plans
symptom
tracking.
Moderate
heterogeneity
observed,
reflecting
variability
designs
populations.
risk
bias
assessment
indicated
high
methodological
rigor
most
studies,
though
algorithmic
biases
data
quality
remain.
demonstrates
robust
capabilities
offering
data-driven
approach
mental
healthcare.
Future
research
should
address
ethical
concerns,
standardize
methodologies,
explore
underrepresented
populations
maximize
transformative
health.
Язык: Английский
Digital Companionship or Psychological Risk? The Role of AI Characters in Shaping Youth Mental Health
Asian Journal of Psychiatry,
Год журнала:
2025,
Номер
104, С. 104356 - 104356
Опубликована: Янв. 1, 2025
Язык: Английский
Digital Well-Being and AI
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 111 - 132
Опубликована: Фев. 12, 2025
Artificial
intelligence
(AI)
has
become
a
transformative
force
in
mental
health
and
digital
well-being,
offering
personalized
wellness
programs
AI-driven
applications.
However,
these
advancements
present
significant
ethical
challenges,
particularly
regarding
algorithmic
bias
privacy.
This
chapter
discusses
the
implications
of
bias,
where
underrepresentation
diverse
demographics
AI
models
can
lead
to
inequitable
outcomes
privacy
risks
stemming
from
misuse
or
mishandling
sensitive
user
data.
Through
real-world
case
studies,
illustrates
challenges
their
impact
on
equitable
trustworthy
health.
It
also
proposes
strategies
mitigate
risks,
emphasizing
fairness,
transparency,
design.
The
underscores
role
responsible
citizenship,
advocating
for
informed
engagement
with
tools
by
users,
developers,
policymakers
creating
inclusive,
ethical,
privacy-conscious
systems
advance
wellness.
Язык: Английский
The Role of Artificial Intelligence in Managing Bipolar Disorder: A New Frontier in Patient Care
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(7), С. 2515 - 2515
Опубликована: Апрель 7, 2025
Background/Objectives:
Bipolar
disorder
(BD)
is
a
complex
and
chronic
mental
health
condition
that
poses
significant
challenges
for
both
patients
healthcare
providers.
Traditional
treatment
methods,
including
medication
therapy,
remain
vital,
but
there
increasing
interest
in
the
application
of
artificial
intelligence
(AI)
to
enhance
BD
management.
AI
has
potential
improve
mood
episode
prediction,
personalize
plans,
provide
real-time
support,
offering
new
opportunities
managing
more
effectively.
Our
primary
objective
was
explore
role
transforming
management
BD,
specifically
tracking,
personalized
regimens.
Methods:
To
management,
we
conducted
review
recent
literature
using
key
search
terms.
We
included
studies
discussed
applications
personalization.
The
were
selected
based
on
their
relevance
AI's
with
attention
PICO
criteria:
Population-individuals
diagnosed
BD;
Intervention-AI
tools
personalization,
support;
Comparison-traditional
methods
(when
available);
Outcome-measures
effectiveness,
improvements
patient
care.
Results:
findings
from
research
reveal
promising
developments
use
Studies
suggest
AI-powered
can
enable
proactive
care,
improving
outcomes
reducing
burden
professionals.
ability
analyze
data
wearable
devices,
smartphones,
even
social
media
platforms
provides
valuable
insights
early
detection
dynamic
adjustments.
Conclusions:
While
still
its
stages,
it
presents
transformative
However,
further
development
are
crucial
fully
realize
supporting
optimizing
efficacy.
Язык: Английский
AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry
Issues in Mental Health Nursing,
Год журнала:
2025,
Номер
unknown, С. 1 - 9
Опубликована: Май 16, 2025
Artificial
intelligence
(AI)
is
transforming
digital
health,
its
influence
expanding
across
multiple
sectors,
with
mental
health
and
psychiatric
care
emerging
as
key
areas
of
transformation.
While
significant
advancements
have
been
made
in
medical
AI,
there
remains
a
need
to
better
understand
how
these
technologies
are
integrated
into
clinical
practice
what
challenges
they
introduce.
We
examine
the
use
AI
identifying
treating
disorders,
highlighting
impact
on
screening,
diagnosis,
intervention
strategies.
Technologies
such
natural
language
processing
(NLP),
machine
learning
(ML),
computer-delivered
cognitive
behavioral
therapy
(CBT)
discussed
context
enhancing
Clinical
Decision
Support
Systems
(CDSS).
innovations
promise
increased
efficiency
accessibility
care,
also
introduce
ethical
challenges,
including
concerns
over
privacy,
bias,
reduced
human
interaction.
Through
critical
evaluation,
we
find
that
greater
transparency,
unbiased
model
development
systems
work
hand
human-led
should
be
encouraged.
Our
findings
underscore
importance
continued
research
regulation
ensure
responsible
effective
deployment
services.
Язык: Английский
Examining a brief web and longitudinal app-based intervention [Wysa] for mental health support in Singapore during the COVID-19 pandemic: mixed-methods retrospective observational study
Frontiers in Digital Health,
Год журнала:
2024,
Номер
6
Опубликована: Дек. 23, 2024
The
COVID-19
pandemic
in
Singapore
led
to
limited
access
mental
health
services,
resulting
increased
distress
among
the
population.
This
study
explores
potential
benefits
of
offering
a
digital
intervention
(DMHI),
Wysa,
as
brief
and
longitudinal
part
mindline.sg
initiative
launched
by
MOH
Office
for
Healthcare
Transformation
Singapore.
paper
aims
(i)
Evaluate
engagement
retention
Singaporean
users
across
on
website
app
version
Wysa;
(ii)
Examine
types
negative
thoughts
challenges
managed
during
pandemic;
(iii)
Assess
impact
conversational
agent
(CA)
supporting
cognitive
restructuring
attributional
styles
patterns.
A
retrospective
observational
design
with
mixed-methods
approach
was
utilized.
Website
(
N
=
69,055)
4,103)
from
September
1,
2020,
July
25,
2022,
were
included
study.
Engagement
evaluated
through
usage
data,
T-tests
used
compare
between
website.
thematic
analysis
assessed
success
restructuring.
Logistic
regression
estimate
likelihood
based
thought
type
style.
Users
who
after
first
using
demonstrated
significantly
higher
P
<
0.001).
In
user
ratings
received
n
8,959),
83.03%
rated
3
or
(out
5)
helpfulness.
91.6%
862)
attempted
790)
successfully
reframed
thought.
single
conversation
Wysa
also
associated
ability
restructure
future-oriented
0.001)
internal,
stable
global
thoughts,
while
other
required
more
intervention.
Psychosocial
documented
mentioned
within
CA.
findings
demonstrate
that
interventions
can
facilitate
enhanced
DMHIs
improve
outcomes.
provides
useful
inputs
guide
development
their
effectiveness.
Язык: Английский