International Journal of Innovative Science and Research Technology (IJISRT),
Год журнала:
2024,
Номер
unknown, С. 500 - 507
Опубликована: Окт. 21, 2024
Artificial
intelligence
(AI)
has
the
potential
to
revolutionize
healthcare
by
enhancing
diagnostic
accuracy,
reducing
administrative
burdens,
and
providing
personalized
treatment.
However,
slow
adoption
of
AI
in
is
due
obstacles
associated
with
ethical
considerations,
data
management,
regulations,
technological
capabilities.
The
results
our
study
highlight
specific
challenges
related
ethics,
technology,
regulatory,
social,
economic,
workforce
barriers
that
affect
implementation
healthcare.
We
aim
improve
current
knowledge
a
more
comprehensive
understanding,
bridging
gap,
addressing
implement
sector.
Health Systems & Reform,
Год журнала:
2025,
Номер
11(1)
Опубликована: Янв. 6, 2025
There
are
approximately
220
million
(about
12%
regional
prevalence)
adults
living
with
diabetes
mellitus
(DM)
its
related
complications,
and
morbidity
knowingly
or
unconsciously
in
the
Western
Pacific
Region
(WP).
The
estimated
healthcare
cost
WP
Malaysia
was
240
billion
USD
1.0
2021
2017,
respectively,
unmeasurable
suffering
loss
of
health
quality
economic
productivity.
This
urgently
calls
for
nothing
less
than
concerted
preventive
efforts
from
all
stakeholders
to
invest
transforming
professionals
reforming
system
that
prioritizes
primary
medical
care
setting,
empowering
allied
professionals,
improvising
organization
providers,
improving
facilities
non-medical
support
people
DM.
article
alludes
challenges
optimal
proposes
evidence-based
initiatives
over
a
5-year
period
detailed
roadmap
bring
about
dynamic
efficient
services
effective
managing
DM
using
as
case
study
reference
other
countries
similar
backgrounds
issues.
includes
scanning
on
landscape
clinical
research
DM,
dimensions
spectrum
misconducts,
possible
common
biases
along
whole
process,
key
strategies,
implementation
limitations
toward
high-quality
research.
Lastly,
digital
medicine
how
artificial
intelligence
could
contribute
open
science
practices
also
discussed.
Advances in healthcare information systems and administration book series,
Год журнала:
2025,
Номер
unknown, С. 495 - 512
Опубликована: Янв. 17, 2025
The
global
healthcare
environment
is
poised
for
a
revolution
because
to
the
convergence
of
Generative
Artificial
Intelligence
(AI)
and
Internet
Medical
Things
(IoMT).
This
article
investigates
how
these
technologies
can
improve
delivery
efficiency,
enable
tailored
treatment
regimens,
diagnostic
accuracy.
A
summary
development
technology
across
time
provided,
emphasizing
significant
advancements
in
AI
IoMT.
Through
case
studies,
application
generative
drug
development,
customized
medicine,
predictive
analytics
explored;
influence
IoMT
on
telemedicine,
remote
patient
monitoring,
real-time
data
also
covered.
amalgamation
with
holds
potential
elevate
clinical
results,
augment
operational
efficacy,
accessible,
specifically
marginalized
areas.
However,
there
are
drawbacks
such
as
socioeconomic
inequality,
privacy
problems
quality.
Strong
cybersecurity
defenses,
governance
needed
future
development.
Family Relations,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 3, 2025
Abstract
Objective
Although
still
in
its
infancy,
research
shows
promise
that
artificial
intelligence
(AI)
models
can
be
integrated
into
relationship
interventions,
and
the
potential
benefits
are
substantial.
This
article
articulates
challenges
opportunities
for
developing
interventions
integrate
AI.
Background
After
defining
AI
differentiating
machine
learning
from
deep
learning,
we
review
key
concepts
strategies
related
to
AI,
specifically
natural
language
processing,
interpretability,
human‐in‐the‐loop
strategies,
as
approaches
needed
develop
interventions.
Method
We
explore
how
is
currently
family
life
literature
has
served
foundation
further
integrating
The
use
of
therapy
contexts
examined,
identify
ethical
need
addressed
this
technology
develops.
Results
examine
using
focusing
on
four
areas:
diagnosis
problems,
providing
autonomous
treatment,
predicting
successful
treatment
outcomes
(prognosis),
biomarkers
monitor
client
reactions.
Opportunities
explored
include
development
data‐efficient
training
methods,
creating
interpretable
focused
relationships,
integration
clinical
expertise
during
model
development,
combining
biomarker
data
with
other
modalities.
Conclusion
Despite
obstacles,
provide
families
personalized
support
strengthen
bonds
overcome
relational
challenges.
Implications
emerging
intersection
science
pioneer
innovative
solutions
diverse
needs.
Digital
medicine
and
smart
healthcare
will
not
be
realised
without
the
cognizant
participation
of
clinicians.
Artificial
intelligence
(AI)
today
primarily
involves
computers
or
machines
designed
to
simulate
aspects
human
using
mathematically
neural
networks,
although
early
AI
systems
relied
on
a
variety
non-neural
network
techniques.
With
increased
complexity
layers,
deep
machine
learning
(ML)
can
self-learn
augment
many
tasks
that
require
decision-making
basis
multiple
sources
data.
Clinicians
are
important
stakeholders
in
use
ML
tools.
The
review
questions
as
follows:
What
is
typical
process
tool
development
full
cycle?
concepts
technical
each
step?
This
synthesises
targeted
literature
reports
summarises
online
structured
materials
present
succinct
explanation
whole
tools
series
cyclical
processes:
(1)
identifying
clinical
problems
suitable
for
solutions,
(2)
forming
project
teams
collaborating
with
experts,
(3)
organising
curating
relevant
data,
(4)
establishing
robust
physical
virtual
infrastructure,
computer
systems'
architecture
support
subsequent
stages,
(5)
exploring
networks
open
access
platforms
before
making
new
decision,
(6)
validating
AI/ML
models,
(7)
registration,
(8)
deployment
continuous
performance
monitoring
(9)
improving
ecosystem
ensures
its
adaptability
evolving
needs.
A
sound
understanding
this
would
help
clinicians
appreciate
engage
codesigning,
evaluating
facilitate
broader
closer
regulation
settings.
