Alzheimer s & Dementia,
Journal Year:
2023,
Volume and Issue:
19(5), P. 2135 - 2149
Published: Feb. 3, 2023
Abstract
Introduction
Machine
learning
research
into
automated
dementia
diagnosis
is
becoming
increasingly
popular
but
so
far
has
had
limited
clinical
impact.
A
key
challenge
building
robust
and
generalizable
models
that
generate
decisions
can
be
reliably
explained.
Some
are
designed
to
inherently
“interpretable,”
whereas
post
hoc
“explainability”
methods
used
for
other
models.
Methods
Here
we
sought
summarize
the
state‐of‐the‐art
of
interpretable
machine
dementia.
Results
We
identified
92
studies
using
PubMed,
Web
Science,
Scopus.
Studies
demonstrate
promising
classification
performance
vary
in
their
validation
procedures
reporting
standards
rely
heavily
on
data
sets.
Discussion
Future
work
should
incorporate
clinicians
validate
explanation
make
conclusive
inferences
about
dementia‐related
disease
pathology.
Critically
analyzing
model
explanations
also
requires
an
understanding
interpretability
itself.
Patient‐specific
required
benefit
practice.
Medical Image Analysis,
Journal Year:
2021,
Volume and Issue:
76, P. 102306 - 102306
Published: Nov. 18, 2021
Recent
developments
in
data
science
general
and
machine
learning
particular
have
transformed
the
way
experts
envision
future
of
surgery.
Surgical
Data
Science
(SDS)
is
a
new
research
field
that
aims
to
improve
quality
interventional
healthcare
through
capture,
organization,
analysis
modeling
data.
While
an
increasing
number
data-driven
approaches
clinical
applications
been
studied
fields
radiological
science,
translational
success
stories
are
still
lacking
In
this
publication,
we
shed
light
on
underlying
reasons
provide
roadmap
for
advances
field.
Based
international
workshop
involving
leading
researchers
SDS,
review
current
practice,
key
achievements
initiatives
as
well
available
standards
tools
topics
relevant
field,
namely
(1)
infrastructure
acquisition,
storage
access
presence
regulatory
constraints,
(2)
annotation
sharing
(3)
analytics.
We
further
complement
technical
perspective
with
(4)
currently
SDS
products
progress
from
academia
(5)
faster
translation
exploitation
full
potential
based
multi-round
Delphi
process.
JHEP Reports,
Journal Year:
2022,
Volume and Issue:
4(4), P. 100443 - 100443
Published: Feb. 2, 2022
Clinical
routine
in
hepatology
involves
the
diagnosis
and
treatment
of
a
wide
spectrum
metabolic,
infectious,
autoimmune
neoplastic
diseases.
Clinicians
integrate
qualitative
quantitative
information
from
multiple
data
sources
to
make
diagnosis,
prognosticate
disease
course,
recommend
treatment.
In
last
5
years,
advances
artificial
intelligence
(AI),
particularly
deep
learning,
have
made
it
possible
extract
clinically
relevant
complex
diverse
clinical
datasets.
particular,
histopathology
radiology
image
contain
diagnostic,
prognostic
predictive
which
AI
can
extract.
Ultimately,
such
systems
could
be
implemented
as
decision
support
tools.
However,
context
hepatology,
this
requires
further
large-scale
validation
regulatory
approval.
Herein,
we
summarise
state
art
with
particular
focus
on
data.
We
present
roadmap
for
development
novel
biomarkers
outline
critical
obstacles
need
overcome.
European Radiology,
Journal Year:
2021,
Volume and Issue:
31(6), P. 3786 - 3796
Published: March 5, 2021
Abstract
Artificial
intelligence
(AI)
has
made
impressive
progress
over
the
past
few
years,
including
many
applications
in
medical
imaging.
Numerous
commercial
solutions
based
on
AI
techniques
are
now
available
for
sale,
forcing
radiology
practices
to
learn
how
properly
assess
these
tools.
While
several
guidelines
describing
good
conducting
and
reporting
AI-based
research
medicine
have
been
published,
fewer
efforts
focused
recommendations
addressing
key
questions
consider
when
critically
assessing
before
purchase.
Commercial
typically
complicated
software
products,
evaluation
of
which
factors
be
considered.
In
this
work,
authors
from
academia
industry
joined
propose
a
practical
framework
that
will
help
stakeholders
evaluate
(the
ECLAIR
guidelines)
reach
an
informed
decision.
Topics
include
relevance
solution
point
view
each
stakeholder,
issues
regarding
performance
validation,
usability
integration,
regulatory
legal
aspects,
financial
support
services.
Key
Points
•
artificial
We
focusing
points
imaging,
allowing
all
conduct
relevant
discussions
with
manufacturers
decision
as
whether
purchase
imaging
applications.
Computers in Biology and Medicine,
Journal Year:
2023,
Volume and Issue:
156, P. 106668 - 106668
Published: Feb. 20, 2023
Artificial
Intelligence
(AI)
techniques
of
deep
learning
have
revolutionized
the
disease
diagnosis
with
their
outstanding
image
classification
performance.
In
spite
results,
widespread
adoption
these
in
clinical
practice
is
still
taking
place
at
a
moderate
pace.
One
major
hindrance
that
trained
Deep
Neural
Networks
(DNN)
model
provides
prediction,
but
questions
about
why
and
how
prediction
was
made
remain
unanswered.
This
linkage
utmost
importance
for
regulated
healthcare
domain
to
increase
trust
automated
system
by
practitioners,
patients
other
stakeholders.
The
application
medical
imaging
has
be
interpreted
caution
due
health
safety
concerns
similar
blame
attribution
case
an
accident
involving
autonomous
cars.
consequences
both
false
positive
negative
cases
are
far
reaching
patients'
welfare
cannot
ignored.
exacerbated
fact
state-of-the-art
algorithms
comprise
complex
interconnected
structures,
millions
parameters,
'black
box'
nature,
offering
little
understanding
inner
working
unlike
traditional
machine
algorithms.
Explainable
AI
(XAI)
help
understand
predictions
which
develop
system,
accelerate
diagnosis,
meet
adherence
regulatory
requirements.
survey
comprehensive
review
promising
field
XAI
biomedical
diagnostics.
We
also
provide
categorization
techniques,
discuss
open
challenges,
future
directions
would
interest
clinicians,
regulators
developers.
Nature Machine Intelligence,
Journal Year:
2022,
Volume and Issue:
4(10), P. 867 - 878
Published: Oct. 10, 2022
Abstract
Saliency
methods,
which
produce
heat
maps
that
highlight
the
areas
of
medical
image
influence
model
prediction,
are
often
presented
to
clinicians
as
an
aid
in
diagnostic
decision-making.
However,
rigorous
investigation
accuracy
and
reliability
these
strategies
is
necessary
before
they
integrated
into
clinical
setting.
In
this
work,
we
quantitatively
evaluate
seven
saliency
including
Grad-CAM,
across
multiple
neural
network
architectures
using
two
evaluation
metrics.
We
establish
first
human
benchmark
for
chest
X-ray
segmentation
a
multilabel
classification
set-up,
examine
under
what
conditions
might
be
more
prone
failure
localizing
important
pathologies
compared
with
expert
benchmark.
find
(1)
while
Grad-CAM
generally
localized
better
than
other
evaluated
all
performed
significantly
worse
benchmark,
(2)
gap
localization
performance
between
was
largest
were
smaller
size
had
shapes
complex,
(3)
confidence
positively
correlated
performance.
Our
work
demonstrates
several
limitations
methods
must
addressed
can
rely
on
them
deep
learning
explainability
imaging.
Artificial Intelligence in Medicine,
Journal Year:
2022,
Volume and Issue:
133, P. 102423 - 102423
Published: Oct. 9, 2022
The
rapid
increase
of
interest
in,
and
use
of,
artificial
intelligence
(AI)
in
computer
applications
has
raised
a
parallel
concern
about
its
ability
(or
lack
thereof)
to
provide
understandable,
or
explainable,
output
users.
This
is
especially
legitimate
biomedical
contexts,
where
patient
safety
paramount
importance.
position
paper
brings
together
seven
researchers
working
the
field
with
different
roles
perspectives,
explore
depth
concept
explainable
AI,
XAI,
offering
functional
definition
conceptual
framework
model
that
can
be
used
when
considering
XAI.
followed
by
series
desiderata
for
attaining
explainability
each
which
touches
upon
key
domain
biomedicine.
npj Digital Medicine,
Journal Year:
2022,
Volume and Issue:
5(1)
Published: Oct. 28, 2022
Abstract
Hundreds
of
millions
operations
are
performed
worldwide
each
year,
and
the
rising
uptake
in
minimally
invasive
surgery
has
enabled
fiber
optic
cameras
robots
to
become
both
important
tools
conduct
sensors
from
which
capture
information
about
surgery.
Computer
vision
(CV),
application
algorithms
analyze
interpret
visual
data,
a
critical
technology
through
study
intraoperative
phase
care
with
goals
augmenting
surgeons’
decision-making
processes,
supporting
safer
surgery,
expanding
access
surgical
care.
While
much
work
been
on
potential
use
cases,
there
currently
no
CV
widely
used
for
diagnostic
or
therapeutic
applications
Using
laparoscopic
cholecystectomy
as
an
example,
we
reviewed
current
techniques
that
have
applied
their
clinical
applications.
Finally,
discuss
challenges
obstacles
remain
be
overcome
broader
implementation
adoption
