LEGAL ORDER History Theory Practice,
Год журнала:
2023,
Номер
38(3), С. 45 - 56
Опубликована: Ноя. 17, 2023
Purpose
of
the
study:
to
assess
and
optimize
anti-corruption
powers
Government
Commission
for
Administrative
Reform.
Research
methods:
structural
analysis
legal
regulation
was
used
as
main
research
method.
results:
paper
presents
results
study
competencies
on
Reform,
a
result
which
author
came
conclusion
that
said
is
special
federal
collegial
coordinating
body
formation
implementation
state
policy
combating
corruption
in
executive
bodies,
activities
are
managed
by
Russian
Federation.
Its
competences
include:
monitoring
authorities,
Federation;
control
over
quality
timeliness
measures
these
authorities;
their
organizational
methodological
support
issues.
Scientific
novelty:
first
time
domestic
science
assessment
Reform
specialized
management
carried
out
Federation
given.
Practical
significance:
mechanisms
proposed
bodies
power,
can
be
improving
its
activities.
Cancers,
Год журнала:
2023,
Номер
15(4), С. 1183 - 1183
Опубликована: Фев. 13, 2023
Skin
cancer
continues
to
remain
one
of
the
major
healthcare
issues
across
globe.
If
diagnosed
early,
skin
can
be
treated
successfully.
While
early
diagnosis
is
paramount
for
an
effective
cure
cancer,
current
process
requires
involvement
specialists,
which
makes
it
expensive
procedure
and
not
easily
available
affordable
in
developing
countries.
This
dearth
specialists
has
given
rise
need
develop
automated
systems.
In
this
context,
Artificial
Intelligence
(AI)-based
methods
have
been
proposed.
These
systems
assist
detection
consequently
lower
its
morbidity,
and,
turn,
alleviate
mortality
rate
associated
with
it.
Machine
learning
deep
are
branches
AI
that
deal
statistical
modeling
inference,
progressively
learn
from
data
fed
into
them
predict
desired
objectives
characteristics.
survey
focuses
on
Learning
Deep
techniques
deployed
field
diagnosis,
while
maintaining
a
balance
between
both
techniques.
A
comparison
made
widely
used
datasets
prevalent
review
papers,
discussing
diagnosis.
The
study
also
discusses
insights
lessons
yielded
by
prior
works.
culminates
future
direction
scope,
will
subsequently
help
addressing
challenges
faced
within
Aesthetic Plastic Surgery,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 19, 2024
Abstract
Background
Abdominoplasty
is
a
common
operation,
used
for
range
of
cosmetic
and
functional
issues,
often
in
the
context
divarication
recti,
significant
weight
loss,
after
pregnancy.
Despite
this,
patient–surgeon
communication
gaps
can
hinder
informed
decision-making.
The
integration
large
language
models
(LLMs)
healthcare
offers
potential
enhancing
patient
information.
This
study
evaluated
feasibility
using
LLMs
answering
perioperative
queries.
Methods
assessed
efficacy
four
leading
LLMs—OpenAI's
ChatGPT-3.5,
Anthropic's
Claude,
Google's
Gemini,
Bing's
CoPilot—using
fifteen
unique
prompts.
All
outputs
were
Flesch–Kincaid,
Flesch
Reading
Ease
score,
Coleman–Liau
index
readability
assessment.
DISCERN
score
Likert
scale
utilized
to
evaluate
quality.
Scores
assigned
by
two
plastic
surgical
residents
then
reviewed
discussed
until
consensus
was
reached
five
surgeon
specialists.
Results
ChatGPT-3.5
required
highest
level
comprehension,
followed
CoPilot.
Claude
provided
most
appropriate
actionable
advice.
In
terms
patient-friendliness,
CoPilot
outperformed
rest,
engagement
information
comprehensiveness.
Gemini
offered
adequate,
though
unremarkable,
advice,
employing
more
professional
language.
uniquely
included
visual
aids
only
model
use
hyperlinks,
although
they
not
very
helpful
acceptable,
it
faced
limitations
responding
certain
Conclusion
showcased
differences
reliability.
offer
advantages
care
but
require
careful
selection.
Future
research
should
integrate
LLM
strengths
address
weaknesses
optimal
education.
Level
Evidence
V
journal
requires
that
authors
assign
evidence
each
article.
For
full
description
these
Evidence-Based
Medicine
ratings,
please
refer
Table
Contents
or
online
Instructions
Authors
www.springer.com/00266
.
Journal of Medical Internet Research,
Год журнала:
2024,
Номер
26, С. e54095 - e54095
Опубликована: Апрель 22, 2024
In
recent
epochs,
the
field
of
critical
medicine
has
experienced
significant
advancements
due
to
integration
artificial
intelligence
(AI).
Specifically,
AI
robots
have
evolved
from
theoretical
concepts
being
actively
implemented
in
clinical
trials
and
applications.
The
intensive
care
unit
(ICU),
known
for
its
reliance
on
a
vast
amount
medical
information,
presents
promising
avenue
deployment
robotic
AI,
anticipated
bring
substantial
improvements
patient
care.
Introduction
Given
the
increasing
number
of
artificial
intelligence
and
machine
learning
(AI/ML)
tools
in
healthcare,
we
aimed
to
gain
an
understanding
consumer
perspectives
on
use
AI/ML
for
healthcare
diagnostics.
Methods
We
conducted
a
qualitative
systematic
review,
following
established
standardized
methods,
existing
literature
indexed
databases
up
4
April
2022:
OVID
MEDLINE,
EMBASE,
Scopus
Web
Science.
Results
Fourteen
studies
were
identified
as
appropriate
inclusion
meta-synthesis
review.
Most
(
n
=
12)
high-income
countries,
with
data
extracted
from
both
mixed
methods
(42.9%)
(57.1%)
studies.
The
four
overarching
themes
across
included
studies:
(1)
Trust,
fear,
uncertainty;
(2)
Data
privacy
ML
governance;
(3)
Impact
delivery
access;
(4)
Consumers
want
be
engaged.
Conclusion
current
evidence
demonstrates
consumers’
understandings
medical
diagnosis
are
complex.
express
complex
combination
hesitancy
support
towards
diagnosis.
Importantly,
their
views
influenced
by
perceived
trustworthiness
providers
who
these
tools.
recognize
potential
improve
diagnostic
accuracy,
efficiency
access,
strong
interest
engaged
development
implementation
process
into
routine
healthcare.
