Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(2), С. 550 - 550
Опубликована: Янв. 16, 2025
The
convergence
of
Artificial
Intelligence
(AI)
and
neuroscience
is
redefining
our
understanding
the
brain,
unlocking
new
possibilities
in
research,
diagnosis,
therapy.
This
review
explores
how
AI’s
cutting-edge
algorithms—ranging
from
deep
learning
to
neuromorphic
computing—are
revolutionizing
by
enabling
analysis
complex
neural
datasets,
neuroimaging
electrophysiology
genomic
profiling.
These
advancements
are
transforming
early
detection
neurological
disorders,
enhancing
brain–computer
interfaces,
driving
personalized
medicine,
paving
way
for
more
precise
adaptive
treatments.
Beyond
applications,
itself
has
inspired
AI
innovations,
with
architectures
brain-like
processes
shaping
advances
algorithms
explainable
models.
bidirectional
exchange
fueled
breakthroughs
such
as
dynamic
connectivity
mapping,
real-time
decoding,
closed-loop
systems
that
adaptively
respond
states.
However,
challenges
persist,
including
issues
data
integration,
ethical
considerations,
“black-box”
nature
many
systems,
underscoring
need
transparent,
equitable,
interdisciplinary
approaches.
By
synthesizing
latest
identifying
future
opportunities,
this
charts
a
path
forward
integration
neuroscience.
From
harnessing
multimodal
cognitive
augmentation,
fusion
these
fields
not
just
brain
science,
it
reimagining
human
potential.
partnership
promises
where
mysteries
unlocked,
offering
unprecedented
healthcare,
technology,
beyond.
Язык: Английский
Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach
AI,
Год журнала:
2025,
Номер
6(1), С. 14 - 14
Опубликована: Янв. 16, 2025
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.
Язык: Английский
The Use of Feedback in Mental Health Services: Expanding Horizons on Reach and Implementation
Administration and Policy in Mental Health and Mental Health Services Research,
Год журнала:
2024,
Номер
52(1), С. 1 - 10
Опубликована: Ноя. 28, 2024
Язык: Английский
Konzeptuelle Grundlagen von Psychotherapie-Integration und modularer Psychotherapie
Deleted Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 17, 2024
Im
vorliegenden
Beitrag
werden
einige
Strömungen
zeitgenössischer
Psychotherapieforschung
unter
einer
konzeptuellen
Perspektive
in
den
Blick
genommen.
Hinsichtlich
der
Behandelnden
gibt
es
einen
Fokus
auf
die
Kompetenzorientierung,
hinsichtlich
Behandelten
eine
transdiagnostische
und
personalisierte
Behandlungen
modularisierte
Psychotherapie.
Diese
Schwerpunktsetzungen
als
"atomistisch"
bezeichnet,
also
Orientierung
an
"kleinsten"
Bestandteilen.
Abschließend
wird
Frage
aufgeworfen,
ob
Aspekte
"Gestalt"
des
Individuums
personalisierten,
modularisierten
Psychotherapie
Platz
finden
sollten
können.