Actuators,
Journal Year:
2025,
Volume and Issue:
14(3), P. 133 - 133
Published: March 8, 2025
This
review
article
explores
the
transformative
impact
of
next-generation
technologies
on
patient
care
and
rehabilitation.
The
advent
tools
has
revolutionized
fields
rehabilitation,
providing
modern
solutions
to
improve
scientific
outcomes
affected
person
studies.
Powered
through
improvements
in
artificial
intelligence,
robotics,
smart
devices,
these
are
reshaping
healthcare
with
aid
improving
therapeutic
approaches
personalizing
treatments.
In
world
robotic
devices
assistive
technology
supplying
essential
help
for
people
mobility
impairments,
promoting
more
independence
healing.
Additionally,
wearable
real-time
tracking
systems
permit
continuous
fitness
information
monitoring,
taking
into
consideration
early
analysis
extra
effective,
tailored
interventions.
clinical
settings,
modern-day
innovations
have
automated
diagnostics,
enabled
remote
patient-monitoring,
brought
virtual
rehabilitation
that
expand
reach
experts.
comprehensive
delves
evolution,
cutting-edge
programs,
destiny
potential
equipment
by
examining
their
capability
deliver
progressed
even
while
addressing
growing
needs
efficient
solutions.
Furthermore,
this
challenges
related
adoption,
including
ethical
considerations,
accessibility
barriers,
need
refined
regulatory
standards
ensure
safe
widespread
use.
PLOS Digital Health,
Journal Year:
2024,
Volume and Issue:
3(11), P. e0000651 - e0000651
Published: Nov. 7, 2024
Biases
in
medical
artificial
intelligence
(AI)
arise
and
compound
throughout
the
AI
lifecycle.
These
biases
can
have
significant
clinical
consequences,
especially
applications
that
involve
decision-making.
Left
unaddressed,
biased
lead
to
substandard
decisions
perpetuation
exacerbation
of
longstanding
healthcare
disparities.
We
discuss
potential
at
different
stages
development
pipeline
how
they
affect
algorithms
Bias
occur
data
features
labels,
model
evaluation,
deployment,
publication.
Insufficient
sample
sizes
for
certain
patient
groups
result
suboptimal
performance,
algorithm
underestimation,
clinically
unmeaningful
predictions.
Missing
findings
also
produce
behavior,
including
capturable
but
nonrandomly
missing
data,
such
as
diagnosis
codes,
is
not
usually
or
easily
captured,
social
determinants
health.
Expertly
annotated
labels
used
train
supervised
learning
models
may
reflect
implicit
cognitive
care
practices.
Overreliance
on
performance
metrics
during
obscure
bias
diminish
a
model's
utility.
When
applied
outside
training
cohort,
deteriorate
from
previous
validation
do
so
differentially
across
subgroups.
How
end
users
interact
with
deployed
solutions
introduce
bias.
Finally,
where
are
developed
published,
by
whom,
impacts
trajectories
priorities
future
development.
Solutions
mitigate
must
be
implemented
care,
which
include
collection
large
diverse
sets,
statistical
debiasing
methods,
thorough
emphasis
interpretability,
standardized
reporting
transparency
requirements.
Prior
real-world
implementation
settings,
rigorous
through
trials
critical
demonstrate
unbiased
application.
Addressing
crucial
ensuring
all
patients
benefit
equitably
AI.
Technologies,
Journal Year:
2024,
Volume and Issue:
12(9), P. 163 - 163
Published: Sept. 13, 2024
The
synergy
between
artificial
intelligence
(AI)
and
hyperspectral
imaging
(HSI)
holds
tremendous
potential
across
a
wide
array
of
fields.
By
leveraging
AI,
the
processing
interpretation
vast
complex
data
generated
by
HSI
are
significantly
enhanced,
allowing
for
more
accurate,
efficient,
insightful
analysis.
This
powerful
combination
has
to
revolutionize
key
areas
such
as
agriculture,
environmental
monitoring,
medical
diagnostics
providing
precise,
real-time
insights
that
were
previously
unattainable.
In
instance,
AI-driven
can
enable
precise
crop
monitoring
disease
detection,
optimizing
yields
reducing
waste.
this
technology
track
changes
in
ecosystems
with
unprecedented
detail,
aiding
conservation
efforts
disaster
response.
diagnostics,
AI-HSI
could
earlier
accurate
improving
patient
outcomes.
As
AI
algorithms
advance,
their
integration
is
expected
drive
innovations
enhance
decision-making
various
sectors.
continued
development
these
technologies
likely
open
new
frontiers
scientific
research
practical
applications,
accessible
tools
wider
range
users.
Future Healthcare Journal,
Journal Year:
2024,
Volume and Issue:
11(3), P. 100182 - 100182
Published: Sept. 1, 2024
The
presence
of
artificial
intelligence
(AI)
in
healthcare
is
a
powerful
and
game-changing
force
that
completely
transforming
the
industry
as
whole.
Using
sophisticated
algorithms
data
analytics,
AI
has
unparalleled
prospects
for
improving
patient
care,
streamlining
operational
efficiency,
fostering
innovation
across
ecosystem.
This
study
conducts
comprehensive
bibliometric
analysis
research
on
healthcare,
utilising
SCOPUS
database
primary
source.
Public Health Nursing,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 4, 2024
ABSTRACT
Background
Artificial
intelligence
now
encompasses
technologies
like
machine
learning,
natural
language
processing,
and
robotics,
allowing
machines
to
undertake
complex
tasks
traditionally
done
by
humans.
AI's
application
in
healthcare
has
led
advancements
diagnostic
tools,
predictive
analytics,
surgical
precision.
Aim
This
comprehensive
review
aims
explore
the
transformative
impact
of
AI
across
diverse
domains,
highlighting
its
applications,
advancements,
challenges,
contributions
enhancing
patient
care.
Methodology
A
literature
search
was
conducted
multiple
databases,
covering
publications
from
2014
2024.
Keywords
related
applications
were
used
gather
data,
focusing
on
studies
exploring
role
medical
specialties.
Results
demonstrated
substantial
benefits
various
fields
medicine.
In
cardiology,
it
aids
automated
image
interpretation,
risk
prediction,
management
cardiovascular
diseases.
oncology,
enhances
cancer
detection,
treatment
planning,
personalized
drug
selection.
Radiology
improved
analysis
accuracy,
while
critical
care
sees
triage
resource
optimization.
integration
into
pediatrics,
surgery,
public
health,
neurology,
pathology,
mental
health
similarly
shown
significant
improvements
precision,
treatment,
overall
The
implementation
low‐resource
settings
been
particularly
impactful,
access
advanced
tools
treatments.
Conclusion
is
rapidly
changing
industry
greatly
increasing
accuracy
diagnoses,
streamlining
plans,
improving
outcomes
a
variety
specializations.
underscores
potential,
early
disease
detection
ability
augment
delivery,
resource‐limited
settings.
Healthcare,
Journal Year:
2024,
Volume and Issue:
12(24), P. 2555 - 2555
Published: Dec. 18, 2024
Nurses
are
frontline
caregivers
who
handle
heavy
workloads
and
high-stakes
activities.
They
face
several
mental
health
issues,
including
stress,
burnout,
anxiety,
depression.
The
welfare
of
nurses
the
standard
patient
treatment
depends
on
resolving
this
problem.
Artificial
intelligence
is
revolutionising
healthcare,
its
integration
provides
many
possibilities
in
addressing
these
concerns.
This
review
examines
literature
published
over
past
40
years,
concentrating
AI
nursing
for
support,
improved
care,
ethical
issues.
Using
databases
such
as
PubMed
Google
Scholar,
a
thorough
search
was
conducted
with
Boolean
operators,
narrowing
results
relevance.
Critically
examined
were
publications
artificial
applications
care
ethics,
health,
health.
examination
revealed
that,
by
automating
repetitive
chores
improving
workload
management,
(AI)
can
relieve
challenges
faced
improve
care.
Practical
implications
highlight
requirement
using
rigorous
implementation
strategies
that
address
data
privacy,
human-centred
decision-making.
All
changes
must
direct
to
guarantee
sustained
significant
influence
healthcare.
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 16, 2024
Maternal
health
remains
a
critical
global
challenge,
with
disparities
in
access
to
care
and
quality
of
services
contributing
high
maternal
mortality
morbidity
rates.
Artificial
intelligence
(AI)
has
emerged
as
promising
tool
for
addressing
these
challenges
by
enhancing
diagnostic
accuracy,
improving
patient
monitoring,
expanding
care.
This
review
explores
the
transformative
role
AI
healthcare,
focusing
on
its
applications
early
detection
pregnancy
complications,
personalized
care,
remote
monitoring
through
AI-driven
technologies.
tools
such
predictive
analytics
machine
learning
can
help
identify
at-risk
pregnancies
guide
timely
interventions,
reducing
preventable
neonatal
complications.
Additionally,
AI-enabled
telemedicine
virtual
assistants
are
bridging
healthcare
gaps,
particularly
underserved
rural
areas,
accessibility
women
who
might
otherwise
face
barriers
Despite
potential
benefits,
data
privacy,
algorithmic
bias,
need
human
oversight
must
be
carefully
addressed.
The
also
discusses
future
research
directions,
including
globally
ethical
frameworks
integration.
holds
revolutionize
both
accessibility,
offering
pathway
safer,
more
equitable
outcomes.
