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.
The
integration
of
artificial
intelligence
in
mental
health
care
represents
a
transformative
shift
the
identification,
treatment,
and
management
disorders.
This
systematic
review
explores
diverse
applications
intelligence,
emphasizing
both
its
benefits
associated
challenges.
A
comprehensive
literature
search
was
conducted
across
multiple
databases
based
on
Preferred
Reporting
Items
for
Systematic
Reviews
Meta-Analyses,
including
ProQuest,
PubMed,
Scopus,
Persian
databases,
resulting
2,638
initial
records.
After
removing
duplicates
applying
strict
selection
criteria,
15
articles
were
included
analysis.
findings
indicate
that
AI
enhances
early
detection
intervention
conditions.
Various
studies
highlighted
effectiveness
AI-driven
tools,
such
as
chatbots
predictive
modeling,
improving
patient
engagement
tailoring
interventions.
Notably,
tools
like
Wysa
app
demonstrated
significant
improvements
user-reported
symptoms.
However,
ethical
considerations
regarding
data
privacy
algorithm
transparency
emerged
critical
While
reviewed
generally
positive
trend
applications,
some
methodologies
exhibited
moderate
quality,
suggesting
room
improvement.
Involving
stakeholders
creation
technologies
is
essential
building
trust
tackling
issues.
Future
should
aim
to
enhance
methods
investigate
their
applicability
various
populations.
underscores
potential
revolutionize
through
enhanced
accessibility
personalized
careful
consideration
implications
methodological
rigor
ensure
responsible
deployment
this
sensitive
field.
Journal of Cancer Education,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 18, 2025
Abstract
The
rapid
integration
of
AI-driven
chatbots
into
oncology
education
represents
both
a
transformative
opportunity
and
critical
challenge.
These
systems,
powered
by
advanced
language
models,
can
deliver
personalized,
real-time
cancer
information
to
patients,
caregivers,
clinicians,
bridging
gaps
in
access
availability.
However,
their
ability
convincingly
mimic
human-like
conversation
raises
pressing
concerns
regarding
misinformation,
trust,
overall
effectiveness
digital
health
communication.
This
review
examines
the
dual-edged
role
AI
chatbots,
exploring
capacity
support
patient
alleviate
clinical
burdens,
while
highlighting
risks
lack
or
inadequate
algorithmic
opacity
(i.e.,
inability
see
data
reasoning
used
make
decision,
which
hinders
appropriate
future
action),
false
information,
ethical
dilemmas
posed
human-seeming
entities.
Strategies
mitigate
these
include
robust
oversight,
transparent
development,
alignment
with
evidence-based
protocols.
Ultimately,
responsible
deployment
requires
commitment
safeguarding
core
values
practice,
human-centered
care.
Family Relations,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 27, 2025
Abstract
Objective
We
aim
to
describe
the
development
of
a
conversational
agent
(CA)
for
parenting,
termed
PAT
(Parenting
Assistant
platform),
demonstrate
how
artificial
intelligence
(AI)
can
enhance
parenting
skills.
Background
Behavioral
problems
are
most
common
issues
in
childhood
mental
health.
Developing
and
disseminating
scalable
interventions
address
early‐stage
behavioral
high
priority.
Artificial
(AI)‐based
CAs
offer
innovative
methods
deliver
reduce
problems.
have
capability
interact
through
text
or
voice
conversations
undergo
training
using
evidence‐based
programs.
However,
research
on
is
limited.
Experience
The
consisted
three
phases:
Phase
1
was
purely
rule‐based,
2
hybrid
(rule‐based
format
plus
large
language
models),
3
featured
an
agentic
architecture.
latest
version
includes
prompt
engineering,
guardrails,
retrieval‐augmented
generation,
few‐shots
learning,
context,
memory
management
Although
comprehensive
empirical
results
pending,
iterative
enhancement
indicate
potential
effective
digital
intervention.
architecture
aims
provide
robust,
context‐aware
interactions
support
challenges.
Implications
reach
broader
population
parents
personalized
tailored
their
specific
needs.
Moreover,
structured
timely
support,
which
family
dynamics
contribute
improved
long‐term
outcomes
both
children.
Conclusion
AI‐based
be
used
as
alternatives
waitlists;
cotherapists;
implemented
health
care,
health,
school
settings.
benefits
risks
different
types
CA
features
discussed.
Journal of Computer-Mediated Communication,
Год журнала:
2024,
Номер
29(5)
Опубликована: Авг. 6, 2024
Abstract
AI
chatbots
are
permeating
the
socio-emotional
realms
of
human
life,
presenting
both
benefits
and
challenges
to
interpersonal
dynamics
well-being.
Despite
burgeoning
interest
in
human–AI
relationships,
conversational
emotional
nuances
real-world,
situ
social
interactions
remain
underexplored.
Through
computational
analysis
a
multimodal
dataset
with
over
35,000
screenshots
posts
from
r/replika,
we
identified
seven
prevalent
types
interactions:
intimate
behavior,
mundane
interaction,
self-disclosure,
play
fantasy,
customization,
transgression,
communication
breakdown,
examined
their
associations
six
basic
emotions.
Our
findings
suggest
paradox
connection
AI,
indicated
by
bittersweet
emotion
encounters
chatbots,
elevated
fear
uncanny
valley
moments
when
exhibits
semblances
mind
deep
self-disclosure.
Customization
characterizes
distinctiveness
companionship,
positively
elevating
user
experiences,
whereas
transgression
breakdown
elicit
or
sadness.
Personal Relationships,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 10, 2024
Abstract
With
the
increasingly
emerging
human–artificial
intelligence
(AI)
romantic
relationships
throughout
world,
it
is
important
to
understand
its
meaning
from
perspective
of
users
who
are
dating
virtual
lovers.
This
study
uses
relational
dialectics
theory
2.0
and
corresponding
method
contrapuntal
analysis
examine
discursive
tensions
what
means
have
an
AI
partner.
Specifically,
this
focused
on
social
chatbot
Replika
analyzed
posts
shared
by
in
online
community.
Findings
revealed
two
discourses:
discourse
idealization
(DI)
realism
(DR)
that
interplayed
through
both
contractive
expansive
practices.
contributes
field
introducing
DI
DR
framework,
which
lays
groundwork
for
future
research
human–AI
relationships.
Additionally,
pivotal
role
communication
highlighted,
serves
as
cornerstone
constructing,
framing,
negotiating
E-Learning and Digital Media,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 3, 2024
In
the
digital
era,
Artificial
Intelligence
(AI)
has
arisen
as
a
revolutionary
influence
with
potential
to
transform
multiple
spheres
of
human
life.
Chatbots,
particularly
OpenAI's
Chat
Generative
Pre-trained
Transformer
(ChatGPT),
are
increasingly
recognised
promising
tools
in
diverse
aspects,
including
mental
health.
This
study
delves
into
ChatGPT's
effectiveness
an
emotional
resilience
support
tool
specifically
for
Generation
Z
(Gen
Z),
demographic
deeply
engaged
interactions.
Employing
sequential
explanatory
design
that
integrates
quantitative
and
qualitative
analyses,
research
investigates
Gen
users'
perceptions
effectiveness,
barriers
its
utilisation,
impact
on
resilience.
The
findings
reveal
significant
acknowledgement
role
enhancing
well-being
notable
concerns
regarding
privacy
security.
Further,
insights
underscore
significance
personalised
interactions,
nonjudgmental
space,
active
listening
characteristics
ChatGPT
fostering
Moreover,
identifies
key
areas
improvement,
such
expanded
topic
coverage
cultural
representation.
Educational
stakeholders
health
professionals
encouraged
utilise
these
integrate
other
AI
tailored
frameworks
Z.
JMIR Mental Health,
Год журнала:
2024,
Номер
11, С. e59560 - e59560
Опубликована: Июль 2, 2024
Background
The
introduction
of
natural
language
processing
(NLP)
technologies
has
significantly
enhanced
the
potential
self-administered
interventions
for
treating
anxiety
and
depression
by
improving
human-computer
interactions.
Although
these
advances,
particularly
in
complex
models
such
as
generative
artificial
intelligence
(AI),
are
highly
promising,
robust
evidence
validating
effectiveness
remains
sparse.
Objective
aim
this
study
was
to
determine
whether
based
on
NLP
can
reduce
depressive
symptoms.
Methods
We
conducted
a
systematic
review
meta-analysis.
searched
Web
Science,
Scopus,
MEDLINE,
PsycINFO,
IEEE
Xplore,
Embase,
Cochrane
Library
from
inception
November
3,
2023.
included
studies
with
participants
any
age
diagnosed
or
through
professional
consultation
validated
psychometric
instruments.
Interventions
had
be
models,
passive
active
comparators.
Outcomes
measured
symptom
scores.
randomized
controlled
trials
quasi-experimental
but
excluded
narrative,
systematic,
scoping
reviews.
Data
extraction
performed
independently
pairs
authors
using
predefined
form.
Meta-analysis
standardized
mean
differences
(SMDs)
random
effects
account
heterogeneity.
Results
In
all,
21
articles
were
selected
review,
which
76%
(16/21)
meta-analysis
each
outcome.
Most
(16/21,
76%)
recent
(2020-2023),
being
mostly
AI-based
(11/21,
52%);
most
(19/21,
90%)
delivered
some
form
therapy
(primarily
cognitive
behavioral
therapy:
16/19,
84%).
overall
showed
that
more
effective
reducing
both
(SMD
0.819,
95%
CI
0.389-1.250;
P<.001)
0.272,
0.116-0.428;
P=.001)
symptoms
compared
various
control
conditions.
Subgroup
analysis
indicated
0.821,
0.207-1.436;
pooled
Rule-based
0.854,
0.172-1.537;
P=.01)
0.347,
0.116-0.578;
P=.003)
meta-regression
no
significant
association
between
participants’
treatment
outcomes
(all
P>.05).
findings
positive,
certainty
very
low,
mainly
due
high
risk
bias,
heterogeneity,
publication
bias.
Conclusions
Our
support
NLP-based
alleviating
symptoms,
highlighting
their
increase
accessibility
to,
costs
in,
mental
health
care.
results
encouraging,
underscoring
need
further
high-quality
examining
implementation
usability.
These
could
become
valuable
components
public
strategies
address
issues.
Trial
Registration
PROSPERO
International
Prospective
Register
Systematic
Reviews
CRD42023472120;
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023472120
Neuropsychiatric
disorders
(NPDs)
pose
a
substantial
burden
on
the
healthcare
system.
The
major
challenge
in
diagnosing
NPDs
is
subjective
assessment
by
physician
which
can
lead
to
inaccurate
and
delayed
diagnosis.
Recent
studies
have
depicted
that
integration
of
artificial
intelligence
(AI)
neuropsychiatry
could
potentially
revolutionize
field
precisely
complex
neurological
mental
health
timely
fashion
providing
individualized
management
strategies.
In
this
narrative
review,
authors
examined
current
status
AI
tools
assessing
neuropsychiatric
evaluated
their
validity
reliability
existing
literature.
analysis
various
datasets
including
MRI
scans,
EEG,
facial
expressions,
social
media
posts,
texts,
laboratory
samples
accurate
diagnosis
conditions
using
machine
learning
has
been
profoundly
explored
article.
recent
trials
tribulations
encouraging
future
scope
utility
application
discussed.
Overall
proved
be
feasible
applicable
it
about
time
research
translates
clinical
settings
for
favorable
patient
outcomes.
Future
should
focus
presenting
higher
quality
evidence
superior
adaptability
establish
guidelines
providers
maintain
standards.