Bioengineering,
Год журнала:
2024,
Номер
11(4), С. 369 - 369
Опубликована: Апрель 12, 2024
This
paper
focuses
on
the
use
of
local
Explainable
Artificial
Intelligence
(XAI)
methods,
particularly
Local
Rule-Based
Explanations
(LORE)
technique,
within
healthcare
and
medical
settings.
It
emphasizes
critical
role
interpretability
transparency
in
AI
systems
for
diagnosing
diseases,
predicting
patient
outcomes,
creating
personalized
treatment
plans.
While
acknowledging
complexities
inherent
trade-offs
between
model
performance,
our
work
underscores
significance
XAI
methods
enhancing
decision-making
processes
healthcare.
By
providing
granular,
case-specific
insights,
like
LORE
enhance
physicians’
patients’
understanding
machine
learning
models
their
outcome.
Our
reviews
significant
contributions
to
healthcare,
highlighting
its
potential
improve
clinical
decision
making,
ensure
fairness,
comply
with
regulatory
standards.
The
integration
of
artificial
intelligence
(AI)
into
healthcare
promises
groundbreaking
advancements
in
patient
care,
revolutionizing
clinical
diagnosis,
predictive
medicine,
and
decision-making.
This
transformative
technology
uses
machine
learning,
natural
language
processing,
large
models
(LLMs)
to
process
reason
like
human
intelligence.
OpenAI's
ChatGPT,
a
sophisticated
LLM,
holds
immense
potential
medical
practice,
research,
education.
However,
as
AI
gains
momentum,
it
brings
forth
profound
ethical
challenges
that
demand
careful
consideration.
comprehensive
review
explores
key
concerns
the
domain,
including
privacy,
transparency,
trust,
responsibility,
bias,
data
quality.
Protecting
privacy
data-driven
is
crucial,
with
implications
for
psychological
well-being
sharing.
Strategies
homomorphic
encryption
(HE)
secure
multiparty
computation
(SMPC)
are
vital
preserving
confidentiality.
Transparency
trustworthiness
systems
essential,
particularly
high-risk
decision-making
scenarios.
Explainable
(XAI)
emerges
critical
aspect,
ensuring
clear
understanding
AI-generated
predictions.
Cybersecurity
becomes
pressing
concern
AI's
complexity
creates
vulnerabilities
breaches.
Determining
responsibility
AI-driven
outcomes
raises
important
questions,
debates
on
moral
agency
accountability.
Shifting
from
ownership
stewardship
enables
responsible
management
compliance
regulations.
Addressing
bias
crucial
avoid
inequities.
Biases
present
collection
algorithm
development
can
perpetuate
disparities.
A
public-health
approach
advocated
address
inequalities
promote
diversity
research
workforce.
Maintaining
quality
imperative
applications,
convolutional
neural
networks
showing
promise
multi-input/mixed
models,
offering
perspective.
In
this
ever-evolving
landscape,
adopt
multidimensional
involving
policymakers,
developers,
practitioners,
patients
mitigate
concerns.
By
addressing
these
challenges,
we
harness
full
while
equitable
outcomes.
Neural Computing and Applications,
Год журнала:
2023,
Номер
35(31), С. 23103 - 23124
Опубликована: Сен. 7, 2023
Abstract
The
current
development
in
deep
learning
is
witnessing
an
exponential
transition
into
automation
applications.
This
can
provide
a
promising
framework
for
higher
performance
and
lower
complexity.
ongoing
undergoes
several
rapid
changes,
resulting
the
processing
of
data
by
studies,
while
it
may
lead
to
time-consuming
costly
models.
Thus,
address
these
challenges,
studies
have
been
conducted
investigate
techniques;
however,
they
mostly
focused
on
specific
approaches,
such
as
supervised
learning.
In
addition,
did
not
comprehensively
other
techniques,
unsupervised
reinforcement
techniques.
Moreover,
majority
neglect
discuss
some
main
methodologies
learning,
transfer
federated
online
Therefore,
motivated
limitations
existing
this
study
summarizes
techniques
supervised,
unsupervised,
reinforcement,
hybrid
learning-based
addition
each
category,
brief
description
categories
their
models
provided.
Some
critical
topics
namely,
transfer,
federated,
models,
are
explored
discussed
detail.
Finally,
challenges
future
directions
outlined
wider
outlooks
researchers.
Artificial
Intelligence
(AI)
describes
computer
systems
able
to
perform
tasks
that
normally
require
human
intelligence,
such
as
visual
perception,
speech
recognition,
decision-making,
and
language
translation.
Examples
of
AI
techniques
are
machine
learning,
neural
networks,
deep
learning.
can
be
applied
in
many
different
areas,
econometrics,
biometry,
e-commerce,
the
automotive
industry.
In
recent
years,
has
found
its
way
into
healthcare
well,
helping
doctors
make
better
decisions
(“clinical
decision
support”),
localizing
tumors
magnetic
resonance
images,
reading
analyzing
reports
written
by
radiologists
pathologists,
much
more.
However,
one
big
risk:
it
perceived
a
“black
box”,
limiting
trust
reliability,
which
is
very
issue
an
area
mean
life
or
death.
As
result,
term
Explainable
(XAI)
been
gaining
momentum.
XAI
tries
ensure
algorithms
(and
resulting
decisions)
understood
humans.
this
narrative
review,
we
will
have
look
at
some
central
concepts
XAI,
describe
several
challenges
around
healthcare,
discuss
whether
really
help
advance,
for
example,
increasing
understanding
trust.
Finally,
alternatives
increase
discussed,
well
future
research
possibilities
XAI.
Sensors,
Год журнала:
2023,
Номер
23(14), С. 6434 - 6434
Опубликована: Июль 16, 2023
The
electroencephalography
(EEG)
signal
is
a
noninvasive
and
complex
that
has
numerous
applications
in
biomedical
fields,
including
sleep
the
brain–computer
interface.
Given
its
complexity,
researchers
have
proposed
several
advanced
preprocessing
feature
extraction
methods
to
analyze
EEG
signals.
In
this
study,
we
comprehensive
review
of
articles
related
processing.
We
searched
major
scientific
engineering
databases
summarized
results
our
findings.
Our
survey
encompassed
entire
process
processing,
from
acquisition
pretreatment
(denoising)
extraction,
classification,
application.
present
detailed
discussion
comparison
various
techniques
used
for
Additionally,
identify
current
limitations
these
their
future
development
trends.
conclude
by
offering
some
suggestions
research
field
Applied Biosciences,
Год журнала:
2024,
Номер
3(1), С. 14 - 44
Опубликована: Янв. 1, 2024
The
purpose
of
this
literature
review
is
to
provide
a
fundamental
synopsis
current
research
pertaining
artificial
intelligence
(AI)
within
the
domain
clinical
practice.
Artificial
has
revolutionized
field
medicine
and
healthcare
by
providing
innovative
solutions
complex
problems.
One
most
important
benefits
AI
in
practice
its
ability
investigate
extensive
volumes
data
with
efficiency
precision.
This
led
development
various
applications
that
have
improved
patient
outcomes
reduced
workload
professionals.
can
support
doctors
making
more
accurate
diagnoses
developing
personalized
treatment
plans.
Successful
examples
are
outlined
for
series
medical
specialties
like
cardiology,
surgery,
gastroenterology,
pneumology,
nephrology,
urology,
dermatology,
orthopedics,
neurology,
gynecology,
ophthalmology,
pediatrics,
hematology,
critically
ill
patients,
as
well
diagnostic
methods.
Special
reference
made
legal
ethical
considerations
accuracy,
informed
consent,
privacy
issues,
security,
regulatory
framework,
product
liability,
explainability,
transparency.
Finally,
closes
appraising
use
future
perspectives.
However,
it
also
approach
implementation
cautiously
ensure
met.
Sensors,
Год журнала:
2023,
Номер
23(17), С. 7435 - 7435
Опубликована: Авг. 25, 2023
Healthcare
4.0
is
a
recent
e-health
paradigm
associated
with
the
concept
of
Industry
4.0.
It
provides
approaches
to
achieving
precision
medicine
that
delivers
healthcare
services
based
on
patient's
characteristics.
Moreover,
enables
telemedicine,
including
telesurgery,
early
predictions,
and
diagnosis
diseases.
This
represents
an
important
for
modern
societies,
especially
current
situation
pandemics.
The
release
fifth-generation
cellular
system
(5G),
advances
in
wearable
device
manufacturing,
technologies,
e.g.,
artificial
intelligence
(AI),
edge
computing,
Internet
Things
(IoT),
are
main
drivers
evolutions
systems.
To
this
end,
work
considers
introducing
advances,
trends,
requirements
Medical
(IoMT)
ultimate
such
networks
era
5G
next-generation
discussed.
design
challenges
research
directions
these
networks.
key
enabling
technologies
systems,
AI
distributed
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Июнь 13, 2023
Autism
spectrum
disorder
(ASD)
presents
a
neurological
and
developmental
that
has
an
impact
on
the
social
cognitive
skills
of
children
causing
repetitive
behaviours,
restricted
interests,
communication
problems
difficulty
in
interaction.
Early
diagnosis
ASD
can
prevent
from
its
severity
prolonged
effects.
Federated
learning
(FL)
is
one
most
recent
techniques
be
applied
for
accurate
diagnoses
early
stages
or
prevention
long-term
In
this
article,
FL
technique
been
uniquely
autism
detection
by
training
two
different
ML
classifiers
including
logistic
regression
support
vector
machine
locally
classification
factors
adults.
Due
to
FL,
results
obtained
these
have
transmitted
central
server
where
meta
classifier
trained
determine
which
approach
Four
patient
datasets,
each
containing
more
than
600
records
effected
adults
repository
features
extraction.
The
proposed
model
predicted
with
98%
accuracy
(in
children)
81%
adults).
Human-Centric Intelligent Systems,
Год журнала:
2023,
Номер
3(3), С. 161 - 188
Опубликована: Авг. 10, 2023
Abstract
In
recent
years,
artificial
intelligence
(AI)
technology
has
been
used
in
most
if
not
all
domains
and
greatly
benefited
our
lives.
While
AI
can
accurately
extract
critical
features
valuable
information
from
large
amounts
of
data
to
help
people
complete
tasks
faster,
there
are
growing
concerns
about
the
non-transparency
decision-making
process.
The
emergence
explainable
(XAI)
allowed
humans
better
understand
control
systems,
which
is
motivated
provide
transparent
explanations
for
decisions
made
by
AI.
This
article
aims
present
a
comprehensive
overview
research
on
XAI
approaches
three
well-defined
taxonomies.
We
offer
an
in-depth
analysis
summary
status
prospects
applications
several
key
areas
where
reliable
urgently
needed
avoid
mistakes
decision-making.
conclude
discussing
XAI’s
limitations
future
directions.
