Artificial
Intelligence
(AI)
is
well-suited
to
help
support
complex
decision-making
tasks
within
clinical
medicine,
including
imaging
applications
like
radiographic
differential
diagnosis
of
central
nervous
system
(CNS)
tumors.
So
far,
there
have
been
numerous
examples
theoretical
AI
solutions
for
this
space,
example,
large-scale
corporate
efforts
IBM's
Watson
AI.
However,
implementation
remains
limited
due
factors
related
the
alignment
technology
in
setting.
User-Centered
Design
(UCD)
a
design
philosophy
that
focuses
on
developing
tailored
specific
users
or
user
groups.
In
study,
we
applied
UCD
develop
an
explainable
tool
clinicians
our
use
case.
Through
four
iterations,
starting
from
basic
functionality
and
visualizations,
progressed
functional
prototypes
realistic
testing
environment.
We
discuss
motivation
approach
each
iteration,
along
with
key
insights
gained.
This
process
has
advanced
conceptual
idea
feasibility
interactive
interfaces
designed
cognitive
tasks.
It
also
provided
us
directions
further
non-invasive
CNS
IEEE Internet of Things Journal,
Journal Year:
2023,
Volume and Issue:
10(24), P. 21873 - 21891
Published: Aug. 14, 2023
Recent
technological
advancements
have
considerably
improved
healthcare
systems
to
provide
various
intelligent
services,
improving
life
quality.
The
Metaverse,
often
described
as
the
next
evolution
of
Internet,
helps
users
interact
with
each
other
and
environment,
thus
offering
a
seamless
connection
between
virtual
physical
worlds.
Additionally,
by
integrating
emerging
technologies,
such
artificial
intelligence
(AI),
cloud
edge
computing,
Internet
Things
(IoT),
blockchain,
semantic
communications,
can
potentially
transform
many
vertical
domains
in
general
sector
(healthcare
Metaverse)
particular.
Metaverse
holds
huge
potential
revolutionize
development
systems,
presenting
new
opportunities
for
significant
delivery,
personalized
experiences,
medical
education,
collaborative
research,
so
on.
However,
challenges
are
associated
realization
privacy,
interoperability,
data
management,
security.
Federated
learning
(FL),
branch
AI,
opens
up
enormous
deal
aforementioned
exploiting
computing
resources
available
at
distributed
devices.
This
motivated
us
present
survey
on
adopting
FL
Metaverse.
Initially,
we
preliminaries
IoT-based
conventional
healthcare,
Furthermore,
benefits
discussed.
Subsequently,
discuss
several
applications
FL-enabled
including
diagnosis,
patient
monitoring,
infectious
disease,
drug
discovery.
Finally,
highlight
solutions
toward
realizing
Frontiers in Digital Health,
Journal Year:
2024,
Volume and Issue:
6
Published: March 18, 2024
The
paper
reviews
the
entire
spectrum
of
Artificial
Intelligence
(AI)
in
mental
health
and
its
positive
role
health.
AI
has
a
huge
number
promises
to
offer
care
this
looks
at
multiple
facets
same.
first
defines
scope
area
It
then
various
like
machine
learning,
supervised
learning
unsupervised
other
AI.
psychiatric
disorders
neurodegenerative
disorders,
intellectual
disability
seizures
are
discussed
along
with
awareness,
diagnosis
intervention
disorders.
emotional
regulation
impact
schizophrenia,
autism
mood
is
also
highlighted.
article
discusses
limitations
based
approaches
need
for
be
culturally
aware,
structured
flexible
algorithms
an
awareness
biases
that
can
arise
ethical
issues
may
use
visited.
Brain Informatics,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: April 5, 2024
Abstract
Explainable
artificial
intelligence
(XAI)
has
gained
much
interest
in
recent
years
for
its
ability
to
explain
the
complex
decision-making
process
of
machine
learning
(ML)
and
deep
(DL)
models.
The
Local
Interpretable
Model-agnostic
Explanations
(LIME)
Shaply
Additive
exPlanation
(SHAP)
frameworks
have
grown
as
popular
interpretive
tools
ML
DL
This
article
provides
a
systematic
review
application
LIME
SHAP
interpreting
detection
Alzheimer’s
disease
(AD).
Adhering
PRISMA
Kitchenham’s
guidelines,
we
identified
23
relevant
articles
investigated
these
frameworks’
prospective
capabilities,
benefits,
challenges
depth.
results
emphasise
XAI’s
crucial
role
strengthening
trustworthiness
AI-based
AD
predictions.
aims
provide
fundamental
capabilities
XAI
enhancing
fidelity
within
clinical
decision
support
systems
prognosis.
Cognitive Computation,
Journal Year:
2023,
Volume and Issue:
16(1), P. 1 - 44
Published: Nov. 13, 2023
Abstract
The
unprecedented
growth
of
computational
capabilities
in
recent
years
has
allowed
Artificial
Intelligence
(AI)
models
to
be
developed
for
medical
applications
with
remarkable
results.
However,
a
large
number
Computer
Aided
Diagnosis
(CAD)
methods
powered
by
AI
have
limited
acceptance
and
adoption
the
domain
due
typical
blackbox
nature
these
models.
Therefore,
facilitate
among
practitioners,
models'
predictions
must
explainable
interpretable.
emerging
field
(XAI)
aims
justify
trustworthiness
predictions.
This
work
presents
systematic
review
literature
reporting
Alzheimer's
disease
(AD)
detection
using
XAI
that
were
communicated
during
last
decade.
Research
questions
carefully
formulated
categorise
into
different
conceptual
approaches
(e.g.,
Post-hoc,
Ante-hoc,
Model-Agnostic,
Model-Specific,
Global,
Local
etc.)
frameworks
(Local
Interpretable
Model-Agnostic
Explanation
or
LIME,
SHapley
Additive
exPlanations
SHAP,
Gradient-weighted
Class
Activation
Mapping
GradCAM,
Layer-wise
Relevance
Propagation
LRP,
XAI.
categorisation
provides
broad
coverage
interpretation
spectrum
from
intrinsic
Ante-hoc
models)
complex
patterns
Post-hoc
taking
local
explanations
global
scope.
Additionally,
forms
interpretations
providing
in-depth
insight
factors
support
clinical
diagnosis
AD
are
also
discussed.
Finally,
limitations,
needs
open
challenges
research
outlined
possible
prospects
their
usage
detection.
International Journal of Engineering and Geosciences,
Journal Year:
2024,
Volume and Issue:
9(2), P. 233 - 246
Published: July 28, 2024
Remote
sensing
(RS),
Geographic
information
systems
(GIS),
and
Machine
learning
can
be
integrated
to
predict
land
surface
temperatures
(LST)
based
on
the
data
related
carbon
monoxide
(CO),
Formaldehyde
(HCHO),
Nitrogen
dioxide
(NO2),
Sulphur
(SO2),
absorbing
aerosol
index
(AAI),
Aerosol
optical
depth
(AOD).
In
this
study,
LST
was
predicted
using
machine
classifiers,
i.e.,
Extra
trees
classifier
(ET),
Logistic
regressors
(LR),
Random
Forests
(RF).
The
accuracy
of
LR
(0.89
or
89%)
is
higher
than
ET
(82%)
RF
classifiers.
Evaluation
metrics
for
each
are
presented
in
form
accuracy,
Area
under
curve
(AUC),
Recall,
Precision,
F1
score,
Kappa,
MCC
(Matthew’s
correlation
coefficient).
Based
relative
performance
ML
it
concluded
that
performed
better.
RS
tools
were
used
extract
across
spatial
temporal
scales
(2019
2022).
order
evaluate
model
graphically,
ROC
(Receiver
operating
characteristic)
curve,
Confusion
matrix,
Validation
Classification
report,
Feature
importance
plot,
t-
SNE
(t-distributed
stochastic
neighbour
embedding)
plot
used.
On
validation
classifier,
observed
returned
complexity
due
limited
availability
other
factors
yet
studied
post
availability.
Sentinel-5-P
MODIS
study.
Advances in psychology, mental health, and behavioral studies (APMHBS) book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 33 - 64
Published: Jan. 3, 2025
Advancements
in
artificial
intelligence
(AI)
are
revolutionizing
neurophysiology,
enhancing
precision
and
efficiency
assessing
brain
nervous
system
function.
AI-driven
neurophysiological
assessment
integrates
machine
learning,
deep
neural
networks,
advanced
data
analytics
to
process
complex
from
electroencephalography,
electromyography
techniques.
This
technology
enables
earlier
diagnosis
of
neurological
disorders
like
epilepsy
Alzheimer's
by
detecting
subtle
patterns
that
may
be
missed
human
analysis.
AI
also
facilitates
real-time
monitoring
predictive
analytics,
improving
outcomes
critical
care
neurorehabilitation.
Challenges
include
ensuring
quality,
addressing
ethical
concerns,
overcoming
computational
limits.
The
integration
into
neurophysiology
offers
a
precise,
scalable,
accessible
approach
treating
disorders.
chapter
discusses
the
methodologies,
applications,
future
directions
assessment,
emphasizing
its
transformative
impact
clinical
research
fields.
In
this
chapter,
the
role
of
AI,
blockchain,
and
emerging
requirements
will
be
analyzed
as
drivers
revolutionary
impact
technologies
for
Internet
Medicine
(IoM).
The
chapter
addresses
issue
integration
these
new
into
healthcare
system,
while
at
same
time
trying
to
assess
whether
or
not
they
are
evidently
efficient.
This
offers
various
perspectives
on
how
AI
could
transform
administration,
therapy
personalization
diagnostics.
Additionally,
essay
investigates
blockchain
ledger
can
innovatively
exploited
protect
medical
data,
maintain
transparency
become
a
part
decentralized
system
healthcare.
studies
other
that
govern
Medical
Management
(IoMM)
along
with
some
enabling
work
Blockchain.
It
looks
their
joint
possibility
well
moral
issues
when
used
repeatedly.
Researchers,
practitioners,
policymakers
aiming
better
understanding
rapidly
evolving
transforming
sector
may
find
main
source
information
industry
goes
through
massive
change.
