Debates em Psiquiatria,
Год журнала:
2024,
Номер
14, С. 1 - 5
Опубликована: Сен. 13, 2024
A
inteligência
artificial
(IA)
vem
representando
uma
revolução
na
assistência
médica,
especificamente
ao
que
se
refere
à
saúde
mental,
cujo
potencial
leva
em
conta
algoritmos
de
diagnóstico,
análise
dados
diversas
fontes
e
monitoramento
pacientes
tempo
real,
no
entanto,
questões
associadas
privacidade,
preconceito
risco
desta
ferramenta
substituir
o
atendimento
humano
também
são
evidentes;
modo
a
regulamentação
envolvimento
do
médico
fundamentais
para
sua
implantação
equitativa;
não
obstante
potencializar
tomada
decisões
clínicas
eficiência,
contrapartida,
pode
secundar
os
dilemas
morais,
perda
autonomia
relacionadas
escopo
da
prática,
alcance
um
equilíbrio
entre
pontos
fortes
as
limitações
implica
utiliza-la
como
suplemento
clínico
validado
sob
supervisão
médica;
trajetória
deve
estar
alinhada
otimização
tratamento
mental
manutenção
cuidado
compassivo;
mas
negar
integração
psiquiatria
psicoterapia
é
realidade.
Social Sciences,
Год журнала:
2024,
Номер
13(7), С. 381 - 381
Опубликована: Июль 22, 2024
AI
has
the
potential
to
revolutionize
mental
health
services
by
providing
personalized
support
and
improving
accessibility.
However,
it
is
crucial
address
ethical
concerns
ensure
responsible
beneficial
outcomes
for
individuals.
This
systematic
review
examines
considerations
surrounding
implementation
impact
of
artificial
intelligence
(AI)
interventions
in
field
well-being.
To
a
comprehensive
analysis,
we
employed
structured
search
strategy
across
top
academic
databases,
including
PubMed,
PsycINFO,
Web
Science,
Scopus.
The
scope
encompassed
articles
published
from
2014
2024,
resulting
51
relevant
articles.
identifies
18
key
considerations,
6
associated
with
using
wellbeing
(privacy
confidentiality,
informed
consent,
bias
fairness,
transparency
accountability,
autonomy
human
agency,
safety
efficacy);
5
principles
development
technologies
settings
practice
positive
(ethical
framework,
stakeholder
engagement,
review,
mitigation,
continuous
evaluation
improvement);
7
practices,
guidelines,
recommendations
promoting
use
(adhere
transparency,
prioritize
data
privacy
security,
mitigate
involve
stakeholders,
conduct
regular
reviews,
monitor
evaluate
outcomes).
highlights
importance
By
addressing
privacy,
bias,
oversight,
evaluation,
can
that
like
chatbots
AI-enabled
medical
devices
are
developed
deployed
an
ethically
sound
manner,
respecting
individual
rights,
maximizing
benefits
while
minimizing
harm.
The
increasing
demand
for
psychotherapy
and
limited
access
to
specialists
underscore
the
potential
of
artificial
intelligence
(AI)
in
mental
health
care.
This
study
evaluates
effectiveness
AI-powered
Friend
chatbot
providing
psychological
support
during
crisis
situations,
compared
traditional
psychotherapy.
A
randomized
controlled
trial
was
conducted
with
104
women
diagnosed
anxiety
disorders
active
war
zones.
Participants
were
randomly
assigned
two
groups:
experimental
group
used
daily
support,
while
control
received
60-minute
sessions
three
times
a
week.
Anxiety
levels
assessed
using
Hamilton
Rating
Scale
Beck
Inventory.
T-tests
analyze
results.
Both
groups
showed
significant
reductions
levels.
receiving
therapy
had
45%
reduction
on
scale
50%
scale,
30%
35%
group.
While
provided
accessible,
immediate
proved
more
effective
due
emotional
depth
adaptability
by
human
therapists.
particularly
beneficial
settings
where
therapists
limited,
proving
its
value
scalability
availability.
However,
engagement
notably
lower
in-person
therapy.
offers
scalable,
cost-effective
solution
situations
may
not
be
accessible.
Although
remains
reducing
anxiety,
hybrid
model
combining
AI
interaction
could
optimize
care,
especially
underserved
areas
or
emergencies.
Further
research
is
needed
improve
AI's
responsiveness
adaptability.
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.
Psychological Medicine,
Год журнала:
2025,
Номер
55
Опубликована: Янв. 1, 2025
Abstract
Artificial
intelligence
(AI)
has
been
recently
applied
to
different
mental
health
illnesses
and
healthcare
domains.
This
systematic
review
presents
the
application
of
AI
in
domains
diagnosis,
monitoring,
intervention.
A
database
search
(CCTR,
CINAHL,
PsycINFO,
PubMed,
Scopus)
was
conducted
from
inception
February
2024,
a
total
85
relevant
studies
were
included
according
preestablished
inclusion
criteria.
The
methods
most
frequently
used
support
vector
machine
random
forest
for
learning
chatbot
tools
appeared
be
accurate
detecting,
classifying,
predicting
risk
conditions
as
well
treatment
response
monitoring
ongoing
prognosis
disorders.
Future
directions
should
focus
on
developing
more
diverse
robust
datasets
enhancing
transparency
interpretability
models
improve
clinical
practice.
European Journal of Investigation in Health Psychology and Education,
Год журнала:
2025,
Номер
15(1), С. 9 - 9
Опубликована: Янв. 18, 2025
Large
language
models
(LLMs)
offer
promising
possibilities
in
mental
health,
yet
their
ability
to
assess
disorders
and
recommend
treatments
remains
underexplored.
This
quantitative
cross-sectional
study
evaluated
four
LLMs
(Gemini
2.0
Flash
Experimental),
Claude
(Claude
3.5
Sonnet),
ChatGPT-3.5,
ChatGPT-4)
using
text
vignettes
representing
conditions
such
as
depression,
suicidal
ideation,
early
chronic
schizophrenia,
social
phobia,
PTSD.
Each
model’s
diagnostic
accuracy,
treatment
recommendations,
predicted
outcomes
were
compared
with
norms
established
by
health
professionals.
Findings
indicated
that
for
certain
conditions,
including
depression
PTSD,
like
ChatGPT-4
achieved
higher
accuracy
human
However,
more
complex
cases,
LLM
performance
varied,
achieving
only
55%
while
other
professionals
performed
better.
tended
suggest
a
broader
range
of
proactive
treatments,
whereas
recommended
targeted
psychiatric
consultations
specific
medications.
In
terms
outcome
predictions,
generally
optimistic
regarding
full
recovery,
especially
treatment,
lower
recovery
rates
partial
rates,
particularly
untreated
cases.
While
range,
conservative
highlight
the
need
professional
oversight.
provide
valuable
support
diagnostics
planning
but
cannot
replace
discretion.
Maternal
health
remains
a
critical
global
challenge,
with
disparities
in
access
to
care
and
quality
of
services
contributing
high
maternal
mortality
morbidity
rates.
Artificial
intelligence
(AI)
has
emerged
as
promising
tool
for
addressing
these
challenges
by
enhancing
diagnostic
accuracy,
improving
patient
monitoring,
expanding
care.
This
review
explores
the
transformative
role
AI
healthcare,
focusing
on
its
applications
early
detection
pregnancy
complications,
personalized
care,
remote
monitoring
through
AI-driven
technologies.
tools
such
predictive
analytics
machine
learning
can
help
identify
at-risk
pregnancies
guide
timely
interventions,
reducing
preventable
neonatal
complications.
Additionally,
AI-enabled
telemedicine
virtual
assistants
are
bridging
healthcare
gaps,
particularly
underserved
rural
areas,
accessibility
women
who
might
otherwise
face
barriers
Despite
potential
benefits,
data
privacy,
algorithmic
bias,
need
human
oversight
must
be
carefully
addressed.
The
also
discusses
future
research
directions,
including
globally
ethical
frameworks
integration.
holds
revolutionize
both
accessibility,
offering
pathway
safer,
more
equitable
outcomes.
Advances in psychology, mental health, and behavioral studies (APMHBS) book series,
Год журнала:
2025,
Номер
unknown, С. 313 - 332
Опубликована: Янв. 3, 2025
Predictive
analytics,
powered
by
advancements
in
machine
learning
(ML),
is
reshaping
the
landscape
of
clinical
psychology
and
mental
health
care.
This
paper
explores
transformative
potential
ML
algorithms
early
diagnosis,
personalized
treatment
planning,
predictive
risk
assessments
for
disorders.
By
analysing
complex
datasets,
including
behavioural,
genetic,
environmental
variables,
models
provide
unprecedented
accuracy
identifying
patterns
factors
associated
with
conditions
such
as
depression,
anxiety,
bipolar
disorder,
schizophrenia.
The
study
highlights
integration
natural
language
processing
(NLP)
patient
interactions,
wearable
technologies
real-time
monitoring,
reinforcement
adaptive
therapeutic
interventions.
concludes
emphasizing
a
collaborative
approach
involving
clinicians,
data
scientists,
policymakers
to
ensure
equitable
effective
implementation.
Biomedicines,
Год журнала:
2025,
Номер
13(1), С. 167 - 167
Опубликована: Янв. 12, 2025
Background/Objectives:
The
dual
forces
of
structured
inquiry
and
serendipitous
discovery
have
long
shaped
neuropsychiatric
research,
with
groundbreaking
treatments
such
as
lithium
ketamine
resulting
from
unexpected
discoveries.
However,
relying
on
chance
is
becoming
increasingly
insufficient
to
address
the
rising
prevalence
mental
health
disorders
like
depression
schizophrenia,
which
necessitate
precise,
innovative
approaches.
Emerging
technologies
artificial
intelligence,
induced
pluripotent
stem
cells,
multi-omics
potential
transform
this
field
by
allowing
for
predictive,
patient-specific
interventions.
Despite
these
advancements,
traditional
methodologies
animal
models
single-variable
analyses
continue
be
used,
frequently
failing
capture
complexities
human
conditions.
Summary:
This
review
critically
evaluates
transition
serendipity
precision-based
in
research.
It
focuses
key
innovations
dynamic
systems
modeling
network-based
approaches
that
use
genetic,
molecular,
environmental
data
identify
new
therapeutic
targets.
Furthermore,
it
emphasizes
importance
interdisciplinary
collaboration
human-specific
overcoming
limitations
Conclusions:
We
highlight
precision
psychiatry’s
transformative
revolutionizing
care.
paradigm
shift,
combines
cutting-edge
systematic
frameworks,
promises
increased
diagnostic
accuracy,
reproducibility,
efficiency,
paving
way
tailored
better
patient
outcomes
Background:
AI-driven
mental
health
solutions
offer
transformative
potential
for
improving
healthcare
outcomes,
but
identifying
the
most
effective
approaches
remains
a
challenge.
This
study
addresses
this
gap
by
evaluating
and
prioritizing
alternatives
based
on
key
criteria,
including
feasibility
of
implementation,
cost-effectiveness,
scalability,
ethical
compliance,
user
satisfaction,
impact
clinical
outcomes.
Methods:
A
fuzzy
multi-criteria
decision-making
(MCDM)
model,
consisting
TOPSIS
ARAS,
was
employed
to
rank
alternatives,
while
hybridization
two
methods
used
address
discrepancies
between
methods,
each
emphasizing
distinct
evaluative
aspect.
Results:
Fuzzy
TOPSIS,
focusing
closeness
ideal
solution,
ranked
personalization
care
(A5)
as
top
alternative
with
coefficient
0.50,
followed
engagement
(A2)
at
0.45.
which
evaluates
cumulative
performance,
also
A5
highest,
an
overall
performance
rating
Si
=
0.90
utility
degree
Qi
0.92.
Combining
both
provided
balanced
assessment,
retaining
its
position
due
high
scores
in
satisfaction
Conclusions:
result
underscores
importance
optimizing
solutions,
suggesting
that
tailored,
user-focused
are
pivotal
maximizing
treatment
success
adherence.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 153 - 172
Опубликована: Март 6, 2025
Transformative
role
of
machine
learning
in
mental
health
care,
with
a
focus
on
digital
therapy
and
personalized
support.
As
challenges
increase
globally,
traditional
therapeutic
approaches
face
limitations
scalability
customization.
Machine
innovations,
such
as
natural
language
processing
(NLP)
predictive
analytics,
offer
new
avenues
for
diagnosis,
treatment,
ongoing
care.
AI-powered
platforms,
including
chatbots,
provide
real-time
interventions,
while
support
systems
analyze
user
data
to
tailor
strategies.
By
identifying
patterns
behaviors
symptoms,
enhances
the
effectiveness
treatments,
promoting
timely
individualized
However,
like
privacy,
algorithmic
bias,
potential
over-reliance
technology
must
be
addressed.
these
technologies
evolve,
they
significantly
improve
access
quality
creating
scalable
responsive
diverse
populations.