JMIR Human Factors,
Journal Year:
2024,
Volume and Issue:
11, P. e52514 - e52514
Published: Sept. 13, 2024
Abstract
Background
Image-driven
specialisms
such
as
radiology
and
pathology
are
at
the
forefront
of
medical
artificial
intelligence
(AI)
innovation.
Many
believe
that
AI
will
lead
to
significant
shifts
in
professional
roles,
so
it
is
vital
investigate
how
professionals
view
pending
changes
innovation
initiate
incorporate
their
views
ongoing
developments.
Objective
Our
study
aimed
gain
insights
into
perspectives
wishes
radiologists
pathologists
regarding
promise
AI.
Methods
We
have
conducted
first
qualitative
interview
investigating
both
integration
fields.
The
design
accordance
with
consolidated
criteria
for
reporting
research
(COREQ).
Results
In
total,
21
participants
were
interviewed
this
(7
pathologists,
10
radiologists,
4
computer
scientists).
interviews
revealed
a
diverse
range
on
impact
Respondents
discussed
various
task-specific
benefits
AI;
yet,
agreed
had
yet
live
up
its
hype.
Overall,
our
shows
could
facilitate
welcome
workflows
image-driven
eventually
better
quality
care.
At
same
time,
these
also
admitted
many
hopes
expectations
unlikely
become
reality
next
decade.
Conclusions
This
points
importance
maintaining
“healthy
skepticism”
imaging
argues
more
structural
inclusive
discussions
about
whether
right
technology
solve
current
problems
encountered
daily
clinical
practice.
UNSTRUCTURED
Introduction:
The
rapid
progress
in
the
development
of
artificial
intelligence
(AI)
is
having
a
substantial
impact
on
healthcare
(HC)
delivery
and
doctor-patient
interaction.
This
scoping
review
seeks
to
offer
thorough
analysis
current
status
integrating
AI
into
medical
practice
apprehensions
expressed
by
professionals
(HPs)
over
its
application.
Methods:
utilized
PRISMA-ScR
method
examine
papers
that
investigate
HPs
about
AI.
Following
application
inclusion
exclusion
criteria,
total
32
studies
were
selected
for
final
analysis.
Our
objective
was
develop
an
attitude
measure
accurately
captures
unfavorable
attitudes
towards
We
achieved
this
selecting
then
ranking
them
scale
represents
degree
aversion,
ranging
from
mild
skepticism
intense
fear.
ultimate
depiction
as
follows:
Skepticism,
reluctance,
anxiety,
resistance,
Results:
Three
themes
identified
through
process
thematic
National
surveys
performed
among
aim
comprehensively
analyze
emotions,
worries,
regarding
integration
industry.
Research
technostress
primarily
focuses
psychological
dimensions
adopting
AI,
examining
emotional
reactions,
fears,
difficulties
experienced
human
participants
when
they
encounter
AI-powered
equipment.
high-level
perspective
category
includes
take
broad
comprehensive
approach
evaluating
overarching
themes,
trends,
implications
related
technology
HC.
Discussion:
have
discovered
15
concerns,
which
we
classified
2
distinct
groups.
designated
these
two
classifications
intrinsic
extrinsic.
initial
group
HP's
inherent
professional
identity,
encompassing
their
tasks
capacities.
Conversely,
second
pertains
patients
influence
patient
care.
Next,
shared
made
suggestions
potentially
tackle
problems.
Ultimately,
results
relation
scale,
assessing
each
portrayed.
Conclusions:
solution
addressing
resistance
appears
be
centered
around
education,
implementation
suitable
legislation,
delineation
roles.
Due
prominence
extensive
research
regulation,
suggest
future
should
focus
education.
Health Communication,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 13
Published: Nov. 4, 2024
Medical
large
language
models
are
being
introduced
to
the
public
in
collaboration
with
governments,
medical
institutions,
and
artificial
intelligence
(AI)
researchers.
However,
a
crucial
question
remains:
Will
patients
follow
advice
provided
by
AI
doctors?
The
lack
of
user
research
makes
it
difficult
provide
definitive
answers.
Based
on
clinician-patient
communication
pathway
model,
this
study
conducted
factorial
experiment
2
(medical
provider,
vs.
human)
×
(information
support,
low
high)
(response
latency,
slow
fast)
between-subjects
design
(
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(22), P. e40268 - e40268
Published: Nov. 1, 2024
Three
AI
developments,
classified
as
forms
of
human
enhancements,
center
around
progress
at
the
intersection
AI,
nanotechnology
and
biotechnology.
Our
research
advances
understanding
enhancement
by
data-driven
analytics
offers
practical
tools
for
future
societal
applications.
It
is
based
on
a
survey
that
was
launched
PRC
in
February
2021
to
more
than
5,000
respondents
from
U.S.
consists
about
100
questions
are
grouped
using
prefix
code
3
above-mentioned
enhancement,
science
role,
concerns
excitements,
perceived
algorithm
fairness
demographics.
To
investigate
this
extract
insights
regarding
general
attitude,
data
framework
proposed
clustering
DBSCAN
K-means,
ANOVA
clusters,
PCA,
t-SNE
UMAP
graphical
visualization,
prediction
advanced
customers'
profiles
analyses.
Both
methods
indicate
distinct
customers.
Most
them
moderate,
but
two
smaller
groups
define
tech-ethics
advocates
tech-forward
visionaries.
For
prediction,
multi-class
classification
task,
ROC-AUC
score
0.852
average
F1
Score
0.987.
Following
results,
both
technology
creators
legislators
must
work
collaboratively
ensure
technological
advancements
ethically
grounded,
widely
accepted
aligned
with
values.
