This
examination
explores
joined
picking
up
gathering
appraisals,
unequivocally
United
Averaging
(FedAvg),
Weighted
Consolidated
(FedAvg-W),
Bound
together
Learning
with
Adaptable
Rate
(FedAdapt),
and
Secure
Combination
for
Brought
(SecAgg),
inside
the
space
of
assertion
saving
clinical
benefits
data
assessment.
The
reason
organized
assessments
was
to
assess
their
performance
in
terms
accuracy,
evidence
coverage
communication
speed.
article
provides
a
comparative
evaluation
help
practitioners
select
most
appropriate
algorithm
reasoning
applications.
results
show
that
FedAvg-W
achieves
much
higher
accuracy
than
other
algorithms
especially
when
used
locations
varying
attributes
implying
it
can
adapt
changes.
In
relation
this,
method
called
FedAdapt
mixes
quickly
while
maintaining
high
by
way
dynamically
changing
learning
rate
limits
respect
particular
instances
distribution
information.
A
secure
aggregation
framework
based
on
homomorphic
encryption
guarantees
exact
compliance.
review
subtle
experiences
into
space-related
works,
such
as
health
informatics
federated
learning.
On
one
hand,
SecAgg
fulfills
basic
requirement
ensuring
preserving
medical
side,
FedAdapt's
flexibility
concerns
anticipated
scalability
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(2), P. 675 - 675
Published: Jan. 12, 2024
Integrating
Artificial
Intelligence
(AI)
in
healthcare
represents
a
transformative
shift
with
substantial
potential
for
enhancing
patient
care.
This
paper
critically
examines
this
integration,
confronting
significant
ethical,
legal,
and
technological
challenges,
particularly
privacy,
decision-making
autonomy,
data
integrity.
A
structured
exploration
of
these
issues
focuses
on
Differential
Privacy
as
critical
method
preserving
confidentiality
AI-driven
systems.
We
analyze
the
balance
between
privacy
preservation
practical
utility
data,
emphasizing
effectiveness
encryption,
Privacy,
mixed-model
approaches.
The
navigates
complex
ethical
legal
frameworks
essential
AI
integration
healthcare.
comprehensively
examine
rights
nuances
informed
consent,
along
challenges
harmonizing
advanced
technologies
like
blockchain
General
Data
Protection
Regulation
(GDPR).
issue
algorithmic
bias
is
also
explored,
underscoring
urgent
need
effective
detection
mitigation
strategies
to
build
trust.
evolving
roles
decentralized
sharing,
regulatory
frameworks,
agency
are
discussed
depth.
Advocating
an
interdisciplinary,
multi-stakeholder
approach
responsive
governance,
aims
align
principles,
prioritize
patient-centered
outcomes,
steer
towards
responsible
equitable
enhancements
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
4(2), P. 20 - 62
Published: May 23, 2024
Cutting-edge
technologies
have
been
widely
employed
in
healthcare
delivery,
resulting
transformative
advances
and
promising
enhanced
patient
care,
operational
efficiency,
resource
usage.
However,
the
proliferation
of
networked
devices
data-driven
systems
has
created
new
cybersecurity
threats
that
jeopardize
integrity,
confidentiality,
availability
critical
data.
This
review
paper
offers
a
comprehensive
evaluation
current
state
context
smart
healthcare,
presenting
structured
taxonomy
its
existing
cyber
threats,
mechanisms
essential
roles.
study
explored
(SHSs).
It
identified
discussed
most
pressing
attacks
SHSs
face,
including
fake
base
stations,
medjacking,
Sybil
attacks.
examined
security
measures
deployed
to
combat
SHSs.
These
include
cryptographic-based
techniques,
digital
watermarking,
steganography,
many
others.
Patient
data
protection,
prevention
breaches,
maintenance
SHS
integrity
are
some
roles
ensuring
sustainable
healthcare.
The
long-term
viability
depends
on
constant
assessment
risks
harm
providers,
patients,
professionals.
aims
inform
policymakers,
practitioners,
technology
stakeholders
about
imperatives
best
practices
for
fostering
secure
resilient
ecosystem
by
synthesizing
insights
from
multidisciplinary
perspectives,
such
as
cybersecurity,
management,
sustainability
research.
Understanding
recent
is
controlling
escalating
networks
encouraging
intelligent
delivery.
Electronics,
Journal Year:
2023,
Volume and Issue:
12(19), P. 4074 - 4074
Published: Sept. 28, 2023
During
the
COVID-19
pandemic,
urgency
of
effective
testing
strategies
had
never
been
more
apparent.
The
fusion
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
models,
particularly
within
medical
imaging
(e.g.,
chest
X-rays),
holds
promise
in
smart
healthcare
systems.
Deep
(DL),
a
subset
AI,
has
exhibited
prowess
enhancing
classification
accuracy,
crucial
aspect
expediting
diagnosis.
However,
journey
to
harness
DL’s
potential
is
rife
with
challenges:
notably,
intricate
landscape
data
privacy.
Striking
balance
between
utilizing
patient
for
insights
while
upholding
privacy
formidable.
Federated
(FL)
emerges
as
solution
by
enabling
collaborative
model
training
across
decentralized
sources,
thus
bypassing
centralization
preserving
This
study
presents
tailored,
FL
architecture
screening
via
X-ray
images.
Designed
facilitate
cooperation
among
institutions,
framework
ensures
remain
localized,
eliminating
need
direct
sharing.
Addressing
imbalanced
non-identically
distributed
data,
robust
solution.
Implementation
entails
localized
fog-computing-based
models.
Localized
models
utilize
Convolutional
Neural
Networks
(CNNs)
on
institution-specific
datasets,
model,
refined
iteratively,
takes
precedence
final
classification.
Intriguingly,
global
fortified
fog
computing,
frontrunner
after
weight
refinement,
surpassing
local
Validation
COLAB
platform
gauges
model’s
performance
through
metrics
such
precision,
recall,
F1-score.
Remarkably,
proposed
excels
these
metrics,
solidifying
its
efficacy.
research
navigates
confluence
FL,
imaging,
unveiling
that
could
reshape
delivery.
enriches
scientific
discourse
addressing
learning
carries
implications
enhanced
care.
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 125 - 148
Published: April 24, 2025
The
sudden
infusion
of
artificial
intelligence
(AI)
into
the
medical
sector
requires
strong
frameworks
that
address
regulatory
requirements,
ethical
issues,
privacy
and
concerns
related
to
interoperability.
current
research
conceptualizes
a
dynamic
system
AI
governance
changes
dynamically
along
with
advancements
in
AI,
avoiding
constraints
fixed
models
governance.
In
contrast
conventional
methods,
model
incorporates
adaptive
compliance
processes,
explainable
models,
user-managed
data-sharing
systems
enable
transparency
trustworthiness.
It
also
uses
federated
learning
methods
secure
scalable
adoption
healthcare
avoid
data
heterogeneity
challenges.
includes
enhanced
protection
less
homomorphic
encryption
optimally
utilized
blockchain,
reducing
computation
overhead
allowing
practical
application.
OPUS Toplum Araştırmaları Dergisi,
Journal Year:
2025,
Volume and Issue:
22(1), P. 23 - 32
Published: Feb. 16, 2025
This
study
aims
to
elucidate
the
interdependent
effects
of
challenges
and
risks
using
artificial
intelligence
in
healthcare
sector.
The
ten
obtained
by
literature
were
assessed
five
professionals
involved
managing
health.
Participants
selected
based
on
having
at
least
years
academic
or
professional
experience
participants
made
their
judgments
topic
structured
forms.
DEMATEL
(The
Decision-Making
Trial
Evaluation
Laboratory)
technique
investigated
cause-effect
relationships
between
identified
integration
challenges.
According
analysis
results
terms
degree
importance,
safety
security
risk
(SSR)
is
ranked
first
place,
inadequate
patient
assessments
(IPRA),
data
quality
(DQR),
verifiability
(VR),
stakeholders
perceived
mistrust
(SPM),
(IC),
ethical
considerations
(EC),
algorithm/decision-making
bias
(AMB)
job
displacement
(JDR)
are
following
places.
In
addition,
DQR,
AMB,
SSR,
VR,
IPRA,
DPR
causal
variables;
EC,
IC,
JDR,
SPM
regarded
as
effects.
These
factors
highlight
need
for
robust
mechanisms
ensure
integrity
data,
accuracy
assessments,
transparency
decision-making
processes
AI.
Negative
impacts
ethics,
inclusion,
employment,
trust
will
likely
be
reduced
addressing
root
causes,
such
quality,
assessment,
algorithmic
bias,
developing
policies
address
them.
Blockchain
technology,
a
decentralized
and
immutable
ledger,
has
transformed
identity
access
management
(IAM)
by
enhancing
security,
privacy,
trust
in
digital
ecosystems.
Ensuring
safe
authentication
data
integrity
is
made
possible
its
integration
with
sophisticated
cryptographic
techniques
like
zero-knowledge
proofs
(ZKPs)
public-
key
infrastructure
(PKI).
Other
methods
include
verifiable
credentials
(VCs)
identifiers
(DIDs).
This
paper
provides
comprehensive
analysis
of
blockchain-based
IAM
systems,
comparing
leading
blockchain
platforms,
including
Ethereum,
Hyperledger
Indy,
IOTA,
IoTeX,
management.
The
role
mitigating
identity-related
threats,
such
as
theft
unauthorized
access,
explored
through
decentralization,
immutability,
smart
contract
automation.
Additionally,
security
enhancements,
mechanisms
that
strengthen
solutions
privacy-preserving
authentication,
are
examined.
potential
to
establish
self-sovereign
framework
fosters
trust,
scalability,
ecosystems
highlighted,
paving
the
way
for
next
generation
solutions.
Abstract
Intensive
Care
Unit
(ICU)
patient
monitoring
plays
a
vital
role
in
ensuring
the
safety
and
well-being
of
critically
ill
patients
by
providing
continuous
real-time
insights
into
their
health
status.
The
integration
Internet
Medical
Things
(IoMT)
devices
ICU
including
wearable
sensors
remote
tools,
enables
seamless
collection
transmission
data,
allowing
for
tracking
signs.
Federated
learning
(FL)
enhances
this
process
utilizing
decentralized
data
to
improve
model
generalization
while
maintaining
privacy.
However,
FL-based
faced
challenges
high
delays
decision-making
due
centralized
processing,
significant
execution
time
caused
need
transfer
large
volumes
data.
This
research
proposes
novel
FEDerated
learning-based
LIFE
saving
system
(FED-LIFE)
effective
timely
services
patients.
FED-LIFE
initially
trains
local
models
Ghostnet
combined
Enhanced
LinkNet
(Ghost_EliNet)
which
combines
GhostNet
LinkNet,
tuning
Ghost_EliNet
Red
Deer
Optimization
(RDO)
algorithm
is
employed
accurate
service
allocation.
suggested
approach
implemented
Python
programming.
efficacy
developed
evaluated
several
metrics
namely
Precision,
recall,
f1-score,
accuracy,
delay,
throughput,
time.
proposed
method
achieves
lowest
delay
22
seconds
50
Whereas
existing
FEDSDM,
Deep-CFL,
FL-IRL
attain
45
seconds,
37
35
respectively.
