Parkinson's
illness
inhibits
movement.
Early
diagnosis
is
essential
for
effective
treatment.
Neural
networks
and
machine
learning
algorithms
are
very
adept
at
processing
large
amounts
of
data,
finding
patterns,
producing
precise
predictions.
AI
has
made
medical
prognosis
better,
particularly
disease.
Artificial
intelligence
(AI)-driven
classifiers
neural
network-based
prediction
systems
disease
early
detection
staging
two
noteworthy
applications.
As
new
data
added,
these
can
adapt
improve,
making
them
valuable
research.
models
have
shown
promise.
These
analyse
patient
biomarkers,
history,
demographics
to
predict
development
or
progression.
Thanks
their
ability
find
complex
connections,
make
personalized
predictions
improve
intervention
therapy.
improves
the
accuracy,
speed
Doctors
need
an
accurate
quickly
initiate
appropriate
medications
support
programs.
AI-powered
show
promise
in
treating
disease,
but
experts
doctors
work
together.
To
guarantee
dependability
generalizability
cutting-edge
techniques
clinical
practice,
protection,
ethical
usage,
model
validation
across
many
groups
required.
In
order
correctly
portray
illness,
research
suggests
using
five
significant
classifiers:
logistic
regression,
vector
machines,
decision
trees,
random
forests,
closest
neighbours,
sequential
networks.
Among
classifiers,
K-Nearest
Neighbour
had
highest
accuracy
rate
94%,
while
network
predicted
90%.
Journal of Clinical Medicine,
Journal Year:
2025,
Volume and Issue:
14(2), P. 550 - 550
Published: Jan. 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.
Journal of Neurology,
Journal Year:
2025,
Volume and Issue:
272(4)
Published: March 12, 2025
Abstract
Next-generation
neurostimulators
capable
of
running
closed-loop
adaptive
deep
brain
stimulation
(aDBS)
are
about
to
enter
the
clinical
landscape
for
treatment
Parkinson’s
disease.
Already
promising
results
using
aDBS
have
been
achieved
symptoms
such
as
bradykinesia,
rigidity
and
motor
fluctuations.
However,
heterogeneity
freezing
gait
(FoG)
with
its
wide
range
presentations
exacerbation
cognitive
emotional
load
make
it
more
difficult
predict
treat.
Currently,
a
successful
strategy
ameliorate
FoG
lacks
robust
oscillatory
biomarker.
Furthermore,
technical
implementation
suppressing
an
upcoming
episode
in
real-time
represents
significant
challenge.
This
review
describes
neurophysiological
signals
underpinning
explains
how
is
currently
being
implemented.
we
offer
discussion
addressing
both
theoretical
practical
areas
that
will
need
be
resolved
if
going
able
unlock
full
potential
treat
FoG.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(12)
Published: Oct. 10, 2024
Abstract
The
emergence
of
neuromorphic
computing,
inspired
by
the
structure
and
function
human
brain,
presents
a
transformative
framework
for
modelling
neurological
disorders
in
drug
development.
This
article
investigates
implications
applying
computing
to
simulate
comprehend
complex
neural
systems
affected
conditions
like
Alzheimer’s,
Parkinson’s,
epilepsy,
drawing
from
extensive
literature.
It
explores
intersection
with
neurology
pharmaceutical
development,
emphasizing
significance
understanding
processes
integrating
deep
learning
techniques.
Technical
considerations,
such
as
circuits
into
CMOS
technology
employing
memristive
devices
synaptic
emulation,
are
discussed.
review
evaluates
how
optimizes
discovery
improves
clinical
trials
precisely
simulating
biological
systems.
also
examines
role
models
comprehending
disorders,
facilitating
targeted
treatment
Recent
progress
is
highlighted,
indicating
potential
therapeutic
interventions.
As
advances,
synergy
between
neuroscience
holds
promise
revolutionizing
study
brain’s
complexities
addressing
challenges.
Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 179 - 200
Published: Feb. 14, 2024
Parkinson's
disease
(PD)
is
a
common
age-related
neurodegenerative
disorder
in
the
aging
society.
Early
diagnosis
of
PD
particularly
important
for
efficient
intervention.
Currently,
mainly
made
by
neurologists
who
assess
abnormalities
patient's
motor
system
and
evaluate
severity
according
to
established
criteria,
which
highly
dependent
on
neurologists'
expertise
often
unsatisfactory.
Artificial
intelligence
provides
new
potential
automatic
reliable
based
multimodal
data
analysis.
Some
deep
learning
models
have
been
developed
detection
diverse
biomarkers
such
as
brain
imaging
images,
electroencephalograms,
walking
postures,
speech,
handwriting,
etc.,
with
promising
accuracy.
This
chapter
summarizes
state-of-the-art,
technical
advancements,
unmet
research
gaps,
future
directions
detection.
It
reference
biomedical
engineers,
scientists,
health
professionals.
