IntechOpen eBooks,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 31, 2024
This
chapter
delves
into
how
artificial
intelligence
(AI)
is
set
to
transform
paramedicine
practices.
It
explores
emerging
AI
technologies—like
wearable
devices,
autonomous
drones,
and
advanced
robotics—are
not
just
tools
of
the
future
but
are
beginning
change
paramedics
make
decisions,
respond
emergencies,
ultimately
improve
patient
care.
The
also
discusses
ethical
practical
challenges
bringing
this
critical
field,
such
as
ensuring
data
privacy,
avoiding
biases
in
algorithms,
balancing
technology
with
essential
human
touch
By
highlighting
both
exciting
possibilities
real-world
challenges,
offers
a
thoughtful
guide
for
paramedics,
healthcare
leaders,
policymakers
on
responsibly
effectively
integrate
prehospital
care
systems.
successful
integration
requires
addressing
that
augments
rather
than
replaces
vital
element
emergency
medical
services.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 197 - 216
Опубликована: Март 7, 2025
This
chapter
examines
how
artificial
intelligence
is
revolutionizing
healthcare
by
improving
patient
autonomy
and
engagement.
In
order
to
empower
patients
take
charge
of
their
health
information
make
educated
decisions
about
care
it
looks
at
AI-driven
digital
tools
can
support
personalized
data
management.
The
focuses
on
important
ethical
issues
such
as
informed
consent
privacy
the
requirement
for
AI
algorithms
be
transparent
making
sure
that
patients'
rights
are
given
top
priority
when
these
technologies
implemented.
It
also
discusses
difficulties
in
incorporating
into
current
systems
highlighting
significance
stakeholder
cooperation
between
legislators'
technology
developers
providers.
uses
case
studies
demonstrate
successfully
implemented
improve
empowerment
outcomes.
Frontiers in Robotics and AI,
Год журнала:
2024,
Номер
11
Опубликована: Ноя. 28, 2024
Artificial
Intelligence
(AI)
has
demonstrated
exceptional
performance
in
automating
critical
healthcare
tasks,
such
as
diagnostic
imaging
analysis
and
predictive
modeling,
often
surpassing
human
capabilities.
The
integration
of
AI
promises
substantial
improvements
patient
outcomes,
including
faster
diagnosis
personalized
treatment
plans.
However,
models
frequently
lack
interpretability,
leading
to
significant
challenges
concerning
their
generalizability
across
diverse
populations.
These
opaque
technologies
raise
serious
safety
concerns,
non-interpretable
can
result
improper
decisions
due
misinterpretations
by
providers.
Our
systematic
review
explores
various
applications
healthcare,
focusing
on
the
assessment
model
interpretability
accuracy.
We
identify
elucidate
most
limitations
current
systems,
black-box
nature
deep
learning
variability
different
clinical
settings.
By
addressing
these
challenges,
our
objective
is
provide
providers
with
well-informed
strategies
develop
innovative
safe
solutions.
This
aims
ensure
that
future
implementations
not
only
enhance
but
also
maintain
transparency
safety.
BACKGROUND
The
integration
of
automation
in
nursing
practice
presents
both
transformative
opportunities
and
significant
challenges.
OBJECTIVE
As
healthcare
systems
increasingly
adopt
automated
technologies,
such
as
robotic-assisted
procedures,
electronic
health
records
(EHRs),
AI-driven
decision
support
systems,
it
is
crucial
to
assess
their
impact
on
workflows
patient
care.
METHODS
This
discursive
paper
explores
the
potential
benefits
automation,
including
enhanced
efficiency,
reduced
workload,
improved
outcomes.
RESULTS
By
synthesizing
existing
research,
this
identifies
gaps
current
understanding
automation’s
role
highlights
areas
requiring
further
exploration.
Additionally,
discussion
considers
implications
for
future
practice,
emphasizing
need
policies
that
ensure
seamless
ethical
technology
while
preserving
essential
human
elements
CONCLUSIONS
findings
suggest
can
optimize
processes,
a
balanced
approach
prioritizes
patient-centered
care
professional
development
necessary.
Ultimately,
provides
recommendations
integrating
effectively,
ensuring
technological
advancements
rather
than
undermine
profession’s
core
values.
Italian Journal of Medicine,
Год журнала:
2024,
Номер
18(2)
Опубликована: Апрель 15, 2024
In
hospital
settings,
effective
risk
management
is
critical
to
ensuring
patient
safety,
regulatory
compliance,
and
operational
effectiveness.
Conventional
approaches
assessment
mitigation
frequently
rely
on
manual
procedures
retroactive
analysis,
which
might
not
be
sufficient
recognize
respond
new
risks
as
they
arise.
This
study
examines
how
artificial
intelligence
(AI)
technologies
can
improve
in
healthcare
facilities,
fortifying
safety
precautions
guidelines
while
improving
the
standard
of
care
overall.
Hospitals
proactively
identify
mitigate
risks,
optimize
resource
allocation,
clinical
outcomes
by
utilizing
AI-driven
predictive
analytics,
natural
language
processing,
machine
learning
algorithms.
The
different
applications
AI
are
discussed
this
paper,
along
with
opportunities,
problems,
suggestions
for
their
use
settings.
InfoScience Trends,
Год журнала:
2024,
Номер
1(2), С. 29 - 42
Опубликована: Июль 5, 2024
In
the
quest
to
enhance
patient
care
and
transform
healthcare
delivery,
integration
of
information
science,
artificial
intelligence
(AI),
medical
engineering
emerges
as
a
beacon
hope.
This
article
explores
current
trends
future
directions
in
this
dynamic
field,
shedding
light
on
its
promises
challenges.
The
systematic
review
conducted
herein
analyzed
6381
articles
from
reputable
databases
such
Web
Science,
Scopus,
Embase,
PubMed,
filtered
focusing
65
published
2014
2024.
At
forefront
lies
concept
data-driven
healthcare,
where
vast
amounts
data
are
leveraged
drive
decision-making
processes.
AI-powered
diagnostics
personalized
medicine
also
gaining
traction,
showcasing
potential
revolutionize
diagnosis,
treatment,
disease
prevention
strategies.
However,
alongside
these
come
significant
Data
privacy
security
concerns,
interoperability
issues,
ethical
considerations,
regulatory
complexities
loom
large.
Overcoming
hurdles
necessitates
collaborative
efforts
providers,
technology
developers,
policymakers,
other
stakeholders
ensure
responsible
use
AI-driven
technologies.
Our
findings
suggest
that
integrating
technologies
offers
promising
pathway
toward
personalized,
proactive,
effective
potentially
improving
outcomes
quality
life.
underscores
need
for
robust
frameworks
interdisciplinary
collaboration
realize
benefits
advanced
fully.
Advances in healthcare information systems and administration book series,
Год журнала:
2024,
Номер
unknown, С. 83 - 112
Опубликована: Ноя. 27, 2024
Missed
diagnoses
and
medication
errors
are
significant
risks
in
healthcare,
leading
to
increased
patient
morbidity
mortality.
Traditional
Clinical
Decision
Support
Systems
(CDSS)
rely
on
static,
predefined
rules,
limiting
their
adaptability
personalized
care.
This
chapter
explores
how
integrating
Artificial
Intelligence
(AI)
Machine
Learning
(ML)
can
revolutionize
CDSS,
driving
next-generation
systems.
By
analyzing
clinical
datasets
real
time,
AI
ML
enable
insights
that
enhance
diagnostic
accuracy,
optimize
treatment
recommendations,
improve
risk
stratification,
streamline
workflows.
These
advancements
promise
better
outcomes,
informed
decisions,
reduced
costs.
The
also
addresses
challenges
like
data
quality,
explainability,
regulatory
compliance,
ethics,
proposing
strategies
for
overcoming
these.
Through
collaboration
research,
transform
CDSS
into
foundational
healthcare
elements,
fostering
personalized,
data-driven,
efficient
