Insights into Imaging,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Feb. 5, 2024
Abstract
Objective
To
provide
a
comprehensive
framework
for
value
assessment
of
artificial
intelligence
(AI)
in
radiology.
Methods
This
paper
presents
the
RADAR
framework,
which
has
been
adapted
from
Fryback
and
Thornbury’s
imaging
efficacy
to
facilitate
valuation
radiology
AI
conception
local
implementation.
Local
newly
introduced
underscore
importance
appraising
an
technology
within
its
environment.
Furthermore,
is
illustrated
through
myriad
study
designs
that
help
assess
value.
Results
seven-level
hierarchy,
providing
radiologists,
researchers,
policymakers
with
structured
approach
AI.
designed
be
dynamic
meet
different
needs
throughout
AI’s
lifecycle.
Initial
phases
like
technical
diagnostic
(RADAR-1
RADAR-2)
are
assessed
pre-clinical
deployment
via
silico
clinical
trials
cross-sectional
studies.
Subsequent
stages,
spanning
thinking
patient
outcome
(RADAR-3
RADAR-5),
require
integration
explored
randomized
controlled
cohort
Cost-effectiveness
(RADAR-6)
takes
societal
perspective
on
financial
feasibility,
addressed
health-economic
evaluations.
The
final
level,
RADAR-7,
determines
how
prior
valuations
translate
locally,
evaluated
budget
impact
analysis,
multi-criteria
decision
analyses,
prospective
monitoring.
Conclusion
offers
valuing
Its
layered,
hierarchical
structure,
combined
focus
relevance,
aligns
seamlessly
principles
value-based
Critical
relevance
statement
advances
by
delineating
much-needed
valuation.
Keypoints
•
Radiology
lacks
assessment.
provides
dynamic,
method
thorough
bridging
implementation
gap.
New England Journal of Medicine,
Journal Year:
2023,
Volume and Issue:
388(21), P. 1981 - 1990
Published: May 24, 2023
The
authors
examine
the
advantages
and
limitations
of
current
clinical
radiologic
AI
systems,
new
workflows,
potential
effect
generative
large
multimodal
foundation
models.
Japanese Journal of Radiology,
Journal Year:
2023,
Volume and Issue:
42(1), P. 3 - 15
Published: Aug. 4, 2023
Abstract
In
this
review,
we
address
the
issue
of
fairness
in
clinical
integration
artificial
intelligence
(AI)
medical
field.
As
adoption
deep
learning
algorithms,
a
subfield
AI,
progresses,
concerns
have
arisen
regarding
impact
AI
biases
and
discrimination
on
patient
health.
This
review
aims
to
provide
comprehensive
overview
associated
with
fairness;
discuss
strategies
mitigate
biases;
emphasize
need
for
cooperation
among
physicians,
researchers,
developers,
policymakers,
patients
ensure
equitable
integration.
First,
define
introduce
concept
applications
healthcare
radiology,
emphasizing
benefits
challenges
incorporating
into
practice.
Next,
delve
healthcare,
addressing
various
causes
potential
such
as
misdiagnosis,
unequal
access
treatment,
ethical
considerations.
We
then
outline
fairness,
importance
diverse
representative
data
algorithm
audits.
Additionally,
legal
considerations
privacy,
responsibility,
accountability,
transparency,
explainability
AI.
Finally,
present
Fairness
Artificial
Intelligence
Recommendations
(FAIR)
statement
offer
best
practices.
Through
these
efforts,
aim
foundation
discussing
responsible
implementation
deployment
healthcare.
Bioengineering,
Journal Year:
2024,
Volume and Issue:
11(4), P. 337 - 337
Published: March 29, 2024
As
healthcare
systems
around
the
world
face
challenges
such
as
escalating
costs,
limited
access,
and
growing
demand
for
personalized
care,
artificial
intelligence
(AI)
is
emerging
a
key
force
transformation.
This
review
motivated
by
urgent
need
to
harness
AI’s
potential
mitigate
these
issues
aims
critically
assess
integration
in
different
domains.
We
explore
how
AI
empowers
clinical
decision-making,
optimizes
hospital
operation
management,
refines
medical
image
analysis,
revolutionizes
patient
care
monitoring
through
AI-powered
wearables.
Through
several
case
studies,
we
has
transformed
specific
domains
discuss
remaining
possible
solutions.
Additionally,
will
methodologies
assessing
solutions,
ethical
of
deployment,
importance
data
privacy
bias
mitigation
responsible
technology
use.
By
presenting
critical
assessment
transformative
potential,
this
equips
researchers
with
deeper
understanding
current
future
impact
on
healthcare.
It
encourages
an
interdisciplinary
dialogue
between
researchers,
clinicians,
technologists
navigate
complexities
implementation,
fostering
development
AI-driven
solutions
that
prioritize
standards,
equity,
patient-centered
approach.
JCO Clinical Cancer Informatics,
Journal Year:
2023,
Volume and Issue:
7
Published: March 1, 2023
PURPOSE
This
study
documents
the
creation
of
automated,
longitudinal,
and
prospective
data
analytics
platform
for
breast
cancer
at
a
regional
center.
combines
principles
warehousing
with
natural
language
processing
(NLP)
to
provide
integrated,
timely,
meaningful,
high-quality,
actionable
required
establish
learning
health
system.
METHODS
Data
from
six
hospital
information
systems
one
external
source
were
integrated
on
nightly
basis
by
automated
extract/transform/load
jobs.
Free-text
clinical
documentation
was
processed
using
commercial
NLP
engine.
RESULTS
The
contains
141
elements
7,019
patients
newly
diagnosed
who
received
care
our
center
January
1,
2014,
June
3,
2022.
Daily
updating
database
takes
an
average
56
minutes.
Evaluation
tuning
jobs
found
overall
high
performance,
F1
1.0
19
variables,
further
16
variables
>
0.95.
CONCLUSION
describes
how
combined
can
be
used
create
enable
Although
upfront
time
investment
considerable,
now
that
it
has
been
developed,
daily
is
completed
automatically
in
less
than
hour.
The Lancet Digital Health,
Journal Year:
2024,
Volume and Issue:
6(6), P. e428 - e432
Published: April 23, 2024
With
the
rapid
growth
of
interest
in
and
use
large
language
models
(LLMs)
across
various
industries,
we
are
facing
some
crucial
profound
ethical
concerns,
especially
medical
field.
The
unique
technical
architecture
purported
emergent
abilities
LLMs
differentiate
them
substantially
from
other
artificial
intelligence
(AI)
natural
processing
techniques
used,
necessitating
a
nuanced
understanding
LLM
ethics.
In
this
Viewpoint,
highlight
concerns
stemming
perspectives
users,
developers,
regulators,
notably
focusing
on
data
privacy
rights
use,
provenance,
intellectual
property
contamination,
broad
applications
plasticity
LLMs.
A
comprehensive
framework
mitigating
strategies
will
be
imperative
for
responsible
integration
into
practice,
ensuring
alignment
with
principles
safeguarding
against
potential
societal
risks.
British Journal of Radiology,
Journal Year:
2023,
Volume and Issue:
96(1150)
Published: March 27, 2023
Data
drift
refers
to
differences
between
the
data
used
in
training
a
machine
learning
(ML)
model
and
that
applied
real-world
operation.
Medical
ML
systems
can
be
exposed
various
forms
of
drift,
including
sampled
for
clinical
operation,
medical
practices
or
context
use
use,
time-related
changes
patient
populations,
disease
patterns,
acquisition,
name
few.
In
this
article,
we
first
review
terminology
literature
related
define
distinct
types
discuss
detail
potential
causes
within
applications
with
an
emphasis
on
imaging.
We
then
recent
regarding
effects
systems,
which
overwhelmingly
show
major
cause
performance
deterioration.
methods
monitoring
mitigating
its
pre-
post-deployment
techniques.
Some
detection
issues
around
retraining
when
is
detected
are
included.
Based
our
review,
find
concern
deployment
more
research
needed
so
models
identify
early,
incorporate
effective
mitigation
strategies
resist
decay.
Communications Medicine,
Journal Year:
2023,
Volume and Issue:
3(1)
Published: April 1, 2023
Several
principles
have
been
proposed
to
improve
use
of
artificial
intelligence
(AI)
in
healthcare,
but
the
need
for
AI
longstanding
healthcare
challenges
has
not
sufficiently
emphasized.
We
propose
that
should
be
designed
alleviate
health
disparities,
report
clinically
meaningful
outcomes,
reduce
overdiagnosis
and
overtreatment,
high
value,
consider
biographical
drivers
health,
easily
tailored
local
population,
promote
a
learning
system,
facilitate
shared
decision-making.
These
are
illustrated
by
examples
from
breast
cancer
research
we
provide
questions
can
used
developers
when
applying
each
principle
their
work.
Humanities and Social Sciences Communications,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: March 15, 2024
Abstract
The
purpose
of
this
research
is
to
identify
and
evaluate
the
technical,
ethical
regulatory
challenges
related
use
Artificial
Intelligence
(AI)
in
healthcare.
potential
applications
AI
healthcare
seem
limitless
vary
their
nature
scope,
ranging
from
privacy,
research,
informed
consent,
patient
autonomy,
accountability,
health
equity,
fairness,
AI-based
diagnostic
algorithms
care
management
through
automation
for
specific
manual
activities
reduce
paperwork
human
error.
main
faced
by
states
regulating
were
identified,
especially
legal
voids
complexities
adequate
regulation
better
transparency.
A
few
recommendations
made
protect
data,
mitigate
risks
regulate
more
efficiently
international
cooperation
adoption
harmonized
standards
under
World
Health
Organization
(WHO)
line
with
its
constitutional
mandate
digital
public
health.
European
Union
(EU)
law
can
serve
as
a
model
guidance
WHO
reform
International
Regulations
(IHR).
