Journal of the American Medical Informatics Association,
Journal Year:
2020,
Volume and Issue:
27(12), P. 2011 - 2015
Published: April 29, 2020
The
rise
of
digital
data
and
computing
power
have
contributed
to
significant
advancements
in
artificial
intelligence
(AI),
leading
the
use
classification
prediction
models
health
care
enhance
clinical
decision-making
for
diagnosis,
treatment
prognosis.
However,
such
advances
are
limited
by
lack
reporting
standards
used
develop
those
models,
model
architecture,
evaluation
validation
processes.
Here,
we
present
MINIMAR
(MINimum
Information
Medical
AI
Reporting),
a
proposal
describing
minimum
information
necessary
understand
intended
predictions,
target
populations,
hidden
biases,
ability
generalize
these
emerging
technologies.
We
call
standard
accurately
responsibly
report
on
care.
This
will
facilitate
design
implementation
promote
development
associated
decision
support
tools,
as
well
manage
concerns
regarding
accuracy
bias.
BMJ,
Journal Year:
2020,
Volume and Issue:
unknown, P. m3164 - m3164
Published: Sept. 9, 2020
Abstract
The
CONSORT
2010
(Consolidated
Standards
of
Reporting
Trials)
statement
provides
minimum
guidelines
for
reporting
randomised
trials.
Its
widespread
use
has
been
instrumental
in
ensuring
transparency
when
evaluating
new
interventions.
More
recently,
there
a
growing
recognition
that
interventions
involving
artificial
intelligence
(AI)
need
to
undergo
rigorous,
prospective
evaluation
demonstrate
impact
on
health
outcomes.
CONSORT-AI
extension
is
guideline
clinical
trials
with
an
AI
component.
It
was
developed
parallel
its
companion
trial
protocols:
SPIRIT-AI.
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
two-day
meeting
(31
stakeholders)
refined
checklist
pilot
(34
participants).
includes
14
considered
sufficiently
important
interventions,
they
should
be
routinely
reported
addition
the
core
items.
recommends
investigators
provide
clear
descriptions
intervention,
including
instructions
skills
required
use,
setting
intervention
integrated,
handling
inputs
outputs
human-AI
interaction
providing
analysis
error
cases.
will
help
promote
completeness
assist
editors
peer-reviewers,
as
well
general
readership,
understand,
interpret
critically
appraise
quality
design
risk
bias
International Journal of Telemedicine and Applications,
Journal Year:
2020,
Volume and Issue:
2020, P. 1 - 18
Published: Dec. 3, 2020
Background.
The
implementation
of
medical
digital
technologies
can
provide
better
accessibility
and
flexibility
healthcare
for
the
public.
It
encompasses
availability
open
information
on
health,
treatment,
complications,
recent
progress
biomedical
research.
At
present,
even
in
low-income
countries,
diagnostic
services
are
becoming
more
accessible
available.
However,
many
issues
related
to
health
remain
unmet,
including
reliability,
safety,
testing,
ethical
aspects.
Purpose.
aim
review
is
discuss
analyze
application
big
data,
artificial
intelligence,
telemedicine,
block-chain
platforms,
smart
devices
healthcare,
education.
Basic
Design.
publication
search
was
carried
out
using
Google
Scholar,
PubMed,
Web
Sciences,
Medline,
Wiley
Online
Library,
CrossRef
databases.
highlights
applications
“big
data,”
telemedicine
technologies,
(internet
things)
solving
real
problems
Major
Findings.
We
identified
252
papers
area.
number
discussed
limited
152
due
exclusion
criteria.
literature
demonstrated
that
became
highly
sought
pandemics,
COVID-19.
disastrous
dissemination
COVID-19
through
all
continents
triggered
need
fast
effective
solutions
localize,
manage,
treat
viral
infection.
In
this
regard,
use
other
e-health
might
help
lessen
pressure
systems.
Summary.
Digital
platforms
optimize
diagnosis,
consulting,
treatment
patients.
lack
official
regulations
recommendations,
stakeholders,
private
governmental
organizations,
facing
problem
with
adequate
validation
approbation
novel
technologies.
proper
scientific
research
required
before
a
product
deployed
sector.
Deleted Journal,
Journal Year:
2023,
Volume and Issue:
1(2), P. 731 - 738
Published: Feb. 8, 2023
Artificial
intelligence
(AI)
has
the
potential
to
make
substantial
progress
toward
goal
of
making
healthcare
more
personalized,
predictive,
preventative,
and
interactive.
We
believe
AI
will
continue
its
present
path
ultimately
become
a
mature
effective
tool
for
sector.
Besides
this
AI-based
systems
raise
concerns
regarding
data
security
privacy.
Because
health
records
are
important
vulnerable,
hackers
often
target
them
during
breaches.
The
absence
standard
guidelines
moral
use
ML
in
only
served
worsen
situation.
There
is
debate
about
how
far
artificial
may
be
utilized
ethically
settings
since
there
no
universal
use.
Therefore,
maintaining
confidentiality
medical
crucial.
This
study
enlightens
possible
drawbacks
implementation
sector
their
solutions
overcome
these
situations.
Journal of Medical Internet Research,
Journal Year:
2021,
Volume and Issue:
23(4), P. e25759 - e25759
Published: March 9, 2021
Artificial
intelligence
(AI)
applications
are
growing
at
an
unprecedented
pace
in
health
care,
including
disease
diagnosis,
triage
or
screening,
risk
analysis,
surgical
operations,
and
so
forth.
Despite
a
great
deal
of
research
the
development
validation
care
AI,
only
few
have
been
actually
implemented
frontlines
clinical
practice.The
objective
this
study
was
to
systematically
review
AI
that
real-life
practice.We
conducted
literature
search
PubMed,
Embase,
Cochrane
Central,
CINAHL
identify
relevant
articles
published
between
January
2010
May
2020.
We
also
hand
searched
premier
computer
science
journals
conferences
as
well
registered
trials.
Studies
were
included
if
they
reported
had
real-world
settings.We
identified
51
studies
implementation
evaluation
practice,
which
13
adopted
randomized
controlled
trial
design
eight
experimental
design.
The
targeted
various
tasks,
such
screening
(n=16),
diagnosis
analysis
(n=14),
treatment
(n=7).
most
commonly
addressed
diseases
conditions
sepsis
(n=6),
breast
cancer
(n=5),
diabetic
retinopathy
(n=4),
polyp
adenoma
(n=4).
Regarding
outcomes,
we
found
26
examined
performance
settings,
33
effect
on
clinician
14
patient
one
economic
impact
associated
with
implementation.This
indicates
is
still
early
stage
despite
potential.
More
needs
assess
benefits
challenges
through
more
rigorous
methodology.
Journal of the American Medical Informatics Association,
Journal Year:
2020,
Volume and Issue:
27(12), P. 2011 - 2015
Published: April 29, 2020
The
rise
of
digital
data
and
computing
power
have
contributed
to
significant
advancements
in
artificial
intelligence
(AI),
leading
the
use
classification
prediction
models
health
care
enhance
clinical
decision-making
for
diagnosis,
treatment
prognosis.
However,
such
advances
are
limited
by
lack
reporting
standards
used
develop
those
models,
model
architecture,
evaluation
validation
processes.
Here,
we
present
MINIMAR
(MINimum
Information
Medical
AI
Reporting),
a
proposal
describing
minimum
information
necessary
understand
intended
predictions,
target
populations,
hidden
biases,
ability
generalize
these
emerging
technologies.
We
call
standard
accurately
responsibly
report
on
care.
This
will
facilitate
design
implementation
promote
development
associated
decision
support
tools,
as
well
manage
concerns
regarding
accuracy
bias.
