Digital Health,
Journal Year:
2023,
Volume and Issue:
9
Published: Jan. 1, 2023
Objective
This
qualitative
study
aims
to
present
the
aspirations,
expectations
and
critical
analysis
of
potential
for
artificial
intelligence
(AI)
transform
patient–physician
relationship,
according
multi-stakeholder
insight.
Methods
was
conducted
from
June
December
2021,
using
an
anticipatory
ethics
approach
sociology
as
theoretical
frameworks.
It
focused
mainly
on
three
groups
stakeholders;
namely,
physicians
(n
=
12),
patients
15)
healthcare
managers
11),
all
whom
are
directly
related
adoption
AI
in
medicine
38).
Results
In
this
study,
interviews
were
with
40%
sample
(15/38),
well
31%
(12/38)
29%
health
(11/38).
The
findings
highlight
following:
(1)
impact
fundamental
aspects
relationship
underlying
importance
a
synergistic
between
physician
AI;
(2)
alleviate
workload
reduce
administrative
burden
by
saving
time
putting
patient
at
centre
caring
process
(3)
risk
holistic
neglecting
humanness
healthcare.
Conclusions
which
micro-level
decision-making,
sheds
new
light
transformation
relationship.
results
current
need
adopt
awareness
implementation
applying
thinking
reasoning.
is
important
not
rely
solely
upon
recommendations
while
clinical
reasoning
physicians’
knowledge
best
practices.
Instead,
it
vital
that
core
values
existing
–
such
trust
honesty,
conveyed
through
open
sincere
communication
preserved.
IEEE Journal of Biomedical and Health Informatics,
Journal Year:
2024,
Volume and Issue:
28(6), P. 3597 - 3612
Published: Feb. 29, 2024
Machine
learning
(ML)
has
revolutionized
medical
image-based
diagnostics.
In
this
review,
we
cover
a
rapidly
emerging
field
that
can
be
potentially
significantly
impacted
by
ML
–
eye
tracking
in
imaging.
The
review
investigates
the
clinical,
algorithmic,
and
hardware
properties
of
existing
studies.
particular,
it
evaluates
1)
type
eye-tracking
equipment
used
how
aligns
with
study
aims;
2)
software
required
to
record
process
data,
which
often
requires
user
interface
development,
controller
command
voice
recording;
3)
methodology
utilized
depending
on
anatomy
interest,
gaze
data
representation,
target
clinical
application.
concludes
summary
recommendations
for
future
studies,
confirms
inclusion
broadens
applicability
Radiology
from
computer-aided
diagnosis
(CAD)
gaze-based
image
annotation,
physicians'
error
detection,
fatigue
recognition,
other
areas
high
research
impact.
Applied Ergonomics,
Journal Year:
2024,
Volume and Issue:
117, P. 104243 - 104243
Published: Feb. 1, 2024
In
healthcare,
artificial
intelligence
(AI)
is
expected
to
improve
work
processes,
yet
most
research
focuses
on
the
technical
features
of
AI
rather
than
its
real-world
clinical
implementation.
To
evaluate
implementation
process
an
AI-based
computer-aided
detection
system
(AI-CAD)
for
prostate
MRI
readings,
we
interviewed
German
radiologists
in
a
pre-post
design.
We
embedded
our
findings
Model
Workflow
Integration
and
Technology
Acceptance
analyze
workflow
effects,
facilitators,
barriers.
The
prominent
barriers
were:
(i)
time
delay
process,
(ii)
additional
steps
be
taken,
(iii)
unstable
performance
AI-CAD.
Most
frequently
named
facilitators
were
good
self-organization,
usability
software.
Our
results
underline
importance
holistic
approach
considering
sociotechnical
provide
valuable
insights
into
key
factors
successful
adoption
technologies
systems.
Journal of Endocrinological Investigation,
Journal Year:
2023,
Volume and Issue:
47(5), P. 1067 - 1082
Published: Nov. 16, 2023
Artificial
intelligence
(AI)
has
emerged
as
a
promising
technology
in
the
field
of
endocrinology,
offering
significant
potential
to
revolutionize
diagnosis,
treatment,
and
management
endocrine
disorders.
This
comprehensive
review
aims
provide
concise
overview
current
landscape
AI
applications
endocrinology
metabolism,
focusing
on
fundamental
concepts
AI,
including
machine
learning
algorithms
deep
models.
Digital Health,
Journal Year:
2023,
Volume and Issue:
9
Published: Jan. 1, 2023
Objective
This
qualitative
study
aims
to
present
the
aspirations,
expectations
and
critical
analysis
of
potential
for
artificial
intelligence
(AI)
transform
patient–physician
relationship,
according
multi-stakeholder
insight.
Methods
was
conducted
from
June
December
2021,
using
an
anticipatory
ethics
approach
sociology
as
theoretical
frameworks.
It
focused
mainly
on
three
groups
stakeholders;
namely,
physicians
(n
=
12),
patients
15)
healthcare
managers
11),
all
whom
are
directly
related
adoption
AI
in
medicine
38).
Results
In
this
study,
interviews
were
with
40%
sample
(15/38),
well
31%
(12/38)
29%
health
(11/38).
The
findings
highlight
following:
(1)
impact
fundamental
aspects
relationship
underlying
importance
a
synergistic
between
physician
AI;
(2)
alleviate
workload
reduce
administrative
burden
by
saving
time
putting
patient
at
centre
caring
process
(3)
risk
holistic
neglecting
humanness
healthcare.
Conclusions
which
micro-level
decision-making,
sheds
new
light
transformation
relationship.
results
current
need
adopt
awareness
implementation
applying
thinking
reasoning.
is
important
not
rely
solely
upon
recommendations
while
clinical
reasoning
physicians’
knowledge
best
practices.
Instead,
it
vital
that
core
values
existing
–
such
trust
honesty,
conveyed
through
open
sincere
communication
preserved.
