Information Systems Management,
Journal Year:
2020,
Volume and Issue:
39(1), P. 53 - 63
Published: Dec. 8, 2020
Artificial
Intelligence
(AI)
has
diffused
into
many
areas
of
our
private
and
professional
life.
In
this
research
note,
we
describe
exemplary
risks
black-box
AI,
the
consequent
need
for
explainability,
previous
on
Explainable
AI
(XAI)
in
information
systems
research.
Moreover,
discuss
origin
term
XAI,
generalized
XAI
objectives,
stakeholder
groups,
as
well
quality
criteria
personalized
explanations.
We
conclude
with
an
outlook
to
future
XAI.
BMC Medical Education,
Journal Year:
2023,
Volume and Issue:
23(1)
Published: Sept. 22, 2023
Abstract
Introduction
Healthcare
systems
are
complex
and
challenging
for
all
stakeholders,
but
artificial
intelligence
(AI)
has
transformed
various
fields,
including
healthcare,
with
the
potential
to
improve
patient
care
quality
of
life.
Rapid
AI
advancements
can
revolutionize
healthcare
by
integrating
it
into
clinical
practice.
Reporting
AI’s
role
in
practice
is
crucial
successful
implementation
equipping
providers
essential
knowledge
tools.
Research
Significance
This
review
article
provides
a
comprehensive
up-to-date
overview
current
state
practice,
its
applications
disease
diagnosis,
treatment
recommendations,
engagement.
It
also
discusses
associated
challenges,
covering
ethical
legal
considerations
need
human
expertise.
By
doing
so,
enhances
understanding
significance
supports
organizations
effectively
adopting
technologies.
Materials
Methods
The
investigation
analyzed
use
system
relevant
indexed
literature,
such
as
PubMed/Medline,
Scopus,
EMBASE,
no
time
constraints
limited
articles
published
English.
focused
question
explores
impact
applying
settings
outcomes
this
application.
Results
Integrating
holds
excellent
improving
selection,
laboratory
testing.
tools
leverage
large
datasets
identify
patterns
surpass
performance
several
aspects.
offers
increased
accuracy,
reduced
costs,
savings
while
minimizing
errors.
personalized
medicine,
optimize
medication
dosages,
enhance
population
health
management,
establish
guidelines,
provide
virtual
assistants,
support
mental
care,
education,
influence
patient-physician
trust.
Conclusion
be
used
diagnose
diseases,
develop
plans,
assist
clinicians
decision-making.
Rather
than
simply
automating
tasks,
about
developing
technologies
that
across
settings.
However,
challenges
related
data
privacy,
bias,
expertise
must
addressed
responsible
effective
healthcare.
npj Digital Medicine,
Journal Year:
2021,
Volume and Issue:
4(1)
Published: Jan. 8, 2021
Abstract
A
decade
of
unprecedented
progress
in
artificial
intelligence
(AI)
has
demonstrated
the
potential
for
many
fields—including
medicine—to
benefit
from
insights
that
AI
techniques
can
extract
data.
Here
we
survey
recent
development
modern
computer
vision
techniques—powered
by
deep
learning—for
medical
applications,
focusing
on
imaging,
video,
and
clinical
deployment.
We
start
briefly
summarizing
a
convolutional
neural
networks,
including
tasks
they
enable,
context
healthcare.
Next,
discuss
several
example
imaging
applications
stand
to
benefit—including
cardiology,
pathology,
dermatology,
ophthalmology–and
propose
new
avenues
continued
work.
then
expand
into
general
highlighting
ways
which
workflows
integrate
enhance
care.
Finally,
challenges
hurdles
required
real-world
deployment
these
technologies.
Database,
Journal Year:
2020,
Volume and Issue:
2020
Published: Jan. 1, 2020
Precision
medicine
is
one
of
the
recent
and
powerful
developments
in
medical
care,
which
has
potential
to
improve
traditional
symptom-driven
practice
medicine,
allowing
earlier
interventions
using
advanced
diagnostics
tailoring
better
economically
personalized
treatments.
Identifying
best
pathway
population
involves
ability
analyze
comprehensive
patient
information
together
with
broader
aspects
monitor
distinguish
between
sick
relatively
healthy
people,
will
lead
a
understanding
biological
indicators
that
can
signal
shifts
health.
While
complexities
disease
at
individual
level
have
made
it
difficult
utilize
healthcare
clinical
decision-making,
some
existing
constraints
been
greatly
minimized
by
technological
advancements.
To
implement
effective
precision
enhanced
positively
impact
outcomes
provide
real-time
decision
support,
important
harness
power
electronic
health
records
integrating
disparate
data
sources
discovering
patient-specific
patterns
progression.
Useful
analytic
tools,
technologies,
databases,
approaches
are
required
augment
networking
interoperability
clinical,
laboratory
public
systems,
as
well
addressing
ethical
social
issues
related
privacy
protection
balance.
Developing
multifunctional
machine
learning
platforms
for
extraction,
aggregation,
management
analysis
support
clinicians
efficiently
stratifying
subjects
understand
specific
scenarios
optimize
decision-making.
Implementation
artificial
intelligence
compelling
vision
leading
significant
improvements
achieving
goals
providing
real-time,
lower
costs.
In
this
study,
we
focused
on
analyzing
discussing
various
published
solutions,
perspectives,
aiming
advance
academic
solutions
paving
way
new
data-centric
era
discovery
healthcare.
Nature Medicine,
Journal Year:
2020,
Volume and Issue:
26(9), P. 1364 - 1374
Published: Sept. 1, 2020
Abstract
The
CONSORT
2010
statement
provides
minimum
guidelines
for
reporting
randomized
trials.
Its
widespread
use
has
been
instrumental
in
ensuring
transparency
the
evaluation
of
new
interventions.
More
recently,
there
a
growing
recognition
that
interventions
involving
artificial
intelligence
(AI)
need
to
undergo
rigorous,
prospective
demonstrate
impact
on
health
outcomes.
CONSORT-AI
(Consolidated
Standards
Reporting
Trials–Artificial
Intelligence)
extension
is
guideline
clinical
trials
evaluating
with
an
AI
component.
It
was
developed
parallel
its
companion
trial
protocols:
SPIRIT-AI
(Standard
Protocol
Items:
Recommendations
Interventional
Intelligence).
Both
were
through
staged
consensus
process
literature
review
and
expert
consultation
generate
29
candidate
items,
which
assessed
by
international
multi-stakeholder
group
two-stage
Delphi
survey
(103
stakeholders),
agreed
upon
two-day
meeting
(31
stakeholders)
refined
checklist
pilot
(34
participants).
includes
14
items
considered
sufficiently
important
they
should
be
routinely
reported
addition
core
items.
recommends
investigators
provide
clear
descriptions
intervention,
including
instructions
skills
required
use,
setting
intervention
integrated,
handling
inputs
outputs
human–AI
interaction
provision
analysis
error
cases.
will
help
promote
completeness
assist
editors
peer
reviewers,
as
well
general
readership,
understand,
interpret
critically
appraise
quality
design
risk
bias
