International Journal of Colorectal Disease,
Год журнала:
2024,
Номер
39(1)
Опубликована: Июнь 20, 2024
Abstract
Purpose
To
examine
the
ability
of
generative
artificial
intelligence
(GAI)
to
answer
patients’
questions
regarding
colorectal
cancer
(CRC).
Methods
Ten
clinically
relevant
about
CRC
were
selected
from
top-rated
hospitals’
websites
and
patient
surveys
presented
three
GAI
tools
(Chatbot
Generative
Pre-Trained
Transformer
[GPT-4],
Google
Bard,
CLOVA
X).
Their
responses
compared
with
answers
information
book.
Response
evaluation
was
performed
by
two
groups,
each
consisting
five
healthcare
professionals
(HCP)
patients.
Each
question
scored
on
a
1–5
Likert
scale
based
four
criteria
(maximum
score,
20
points/question).
Results
In
an
analysis
including
only
HCPs,
book
11.8
±
1.2,
GPT-4
13.5
1.1,
Bard
11.5
0.7,
X
12.2
1.4
(
P
=
0.001).
The
score
significantly
higher
than
those
0.020)
patients,
14.1
1.4,
15.2
1.8,
15.5
14.4
without
significant
differences
0.234).
When
both
groups
evaluators
included,
13.0
0.9,
1.0,
13.3
1.5
0.070).
Conclusion
GAIs
demonstrated
similar
or
better
communicative
competence
related
surgery
in
Korean.
If
high-quality
medical
provided
is
supervised
properly
HCPs
published
as
book,
it
could
be
helpful
for
patients
obtain
accurate
make
informed
decisions.
European Archives of Oto-Rhino-Laryngology,
Год журнала:
2024,
Номер
281(11), С. 6123 - 6131
Опубликована: Май 4, 2024
The
widespread
diffusion
of
Artificial
Intelligence
(AI)
platforms
is
revolutionizing
how
health-related
information
disseminated,
thereby
highlighting
the
need
for
tools
to
evaluate
quality
such
information.
This
study
aimed
propose
and
validate
Quality
Assessment
Medical
(QAMAI),
a
tool
specifically
designed
assess
health
provided
by
AI
platforms.
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Март 5, 2024
Abstract
The
introduction
of
large
language
models
(LLMs)
into
clinical
practice
promises
to
improve
patient
education
and
empowerment,
thereby
personalizing
medical
care
broadening
access
knowledge.
Despite
the
popularity
LLMs,
there
is
a
significant
gap
in
systematized
information
on
their
use
care.
Therefore,
this
systematic
review
aims
synthesize
current
applications
limitations
LLMs
using
data-driven
convergent
synthesis
approach.
We
searched
5
databases
for
qualitative,
quantitative,
mixed
methods
articles
published
between
2022
2023.
From
4,349
initial
records,
89
studies
across
29
specialties
were
included,
primarily
examining
based
GPT-3.5
(53.2%,
n=66
124
different
examined
per
study)
GPT-4
(26.6%,
n=33/124)
architectures
question
answering,
followed
by
generation,
including
text
summarization
or
translation,
documentation.
Our
analysis
delineates
two
primary
domains
LLM
limitations:
design
output.
Design
included
6
second-order
12
third-order
codes,
such
as
lack
domain
optimization,
data
transparency,
accessibility
issues,
while
output
9
32
example,
non-reproducibility,
non-comprehensiveness,
incorrectness,
unsafety,
bias.
In
conclusion,
study
first
systematically
map
care,
providing
foundational
framework
taxonomy
implementation
evaluation
healthcare
settings.
Journal of Clinical Medicine,
Год журнала:
2024,
Номер
13(11), С. 3041 - 3041
Опубликована: Май 22, 2024
Background:
Large
language
models
(LLMs)
represent
a
recent
advancement
in
artificial
intelligence
with
medical
applications
across
various
healthcare
domains.
The
objective
of
this
review
is
to
highlight
how
LLMs
can
be
utilized
by
clinicians
and
surgeons
their
everyday
practice.
Methods:
A
systematic
was
conducted
following
the
Preferred
Reporting
Items
for
Systematic
Reviews
Meta-Analyses
guidelines.
Six
databases
were
searched
identify
relevant
articles.
Eligibility
criteria
emphasized
articles
focused
primarily
on
clinical
surgical
LLMs.
Results:
literature
search
yielded
333
results,
34
meeting
eligibility
criteria.
All
from
2023.
There
14
original
research
articles,
four
letters,
one
interview,
15
These
covered
wide
variety
specialties,
including
subspecialties.
Conclusions:
have
potential
enhance
delivery.
In
settings,
assist
diagnosis,
treatment
guidance,
patient
triage,
physician
knowledge
augmentation,
administrative
tasks.
documentation,
planning,
intraoperative
guidance.
However,
addressing
limitations
concerns,
particularly
those
related
accuracy
biases,
crucial.
should
viewed
as
tools
complement,
not
replace,
expertise
professionals.
Diagnostics,
Год журнала:
2024,
Номер
14(8), С. 839 - 839
Опубликована: Апрель 18, 2024
In
the
evolving
field
of
maxillofacial
surgery,
integrating
advanced
technologies
like
Large
Language
Models
(LLMs)
into
medical
practices,
especially
for
trauma
triage,
presents
a
promising
yet
largely
unexplored
potential.
This
study
aimed
to
evaluate
feasibility
using
LLMs
triaging
complex
cases
by
comparing
their
performance
against
expertise
tertiary
referral
center.
Communications Medicine,
Год журнала:
2025,
Номер
5(1)
Опубликована: Янв. 21, 2025
Abstract
Background
The
introduction
of
large
language
models
(LLMs)
into
clinical
practice
promises
to
improve
patient
education
and
empowerment,
thereby
personalizing
medical
care
broadening
access
knowledge.
Despite
the
popularity
LLMs,
there
is
a
significant
gap
in
systematized
information
on
their
use
care.
Therefore,
this
systematic
review
aims
synthesize
current
applications
limitations
LLMs
Methods
We
systematically
searched
5
databases
for
qualitative,
quantitative,
mixed
methods
articles
published
between
2022
2023.
From
4349
initial
records,
89
studies
across
29
specialties
were
included.
Quality
assessment
was
performed
using
Mixed
Appraisal
Tool
2018.
A
data-driven
convergent
synthesis
approach
applied
thematic
syntheses
LLM
free
line-by-line
coding
Dedoose.
Results
show
that
most
investigate
Generative
Pre-trained
Transformers
(GPT)-3.5
(53.2%,
n
=
66
124
different
examined)
GPT-4
(26.6%,
33/124)
answering
questions,
followed
by
generation,
including
text
summarization
or
translation,
documentation.
Our
analysis
delineates
two
primary
domains
limitations:
design
output.
Design
include
6
second-order
12
third-order
codes,
such
as
lack
domain
optimization,
data
transparency,
accessibility
issues,
while
output
9
32
example,
non-reproducibility,
non-comprehensiveness,
incorrectness,
unsafety,
bias.
Conclusions
This
maps
care,
providing
foundational
framework
taxonomy
implementation
evaluation
healthcare
settings.
JAMA Network Open,
Год журнала:
2025,
Номер
8(2), С. e2457879 - e2457879
Опубликована: Фев. 4, 2025
Importance
There
is
much
interest
in
the
clinical
integration
of
large
language
models
(LLMs)
health
care.
Many
studies
have
assessed
ability
LLMs
to
provide
advice,
but
quality
their
reporting
uncertain.
Objective
To
perform
a
systematic
review
examine
variability
among
peer-reviewed
evaluating
performance
generative
artificial
intelligence
(AI)–driven
chatbots
for
summarizing
evidence
and
providing
advice
inform
development
Chatbot
Assessment
Reporting
Tool
(CHART).
Evidence
Review
A
search
MEDLINE
via
Ovid,
Embase
Elsevier,
Web
Science
from
inception
October
27,
2023,
was
conducted
with
help
sciences
librarian
yield
7752
articles.
Two
reviewers
screened
articles
by
title
abstract
followed
full-text
identify
primary
accuracy
AI-driven
(chatbot
studies).
then
performed
data
extraction
137
eligible
studies.
Findings
total
were
included.
Studies
examined
topics
surgery
(55
[40.1%]),
medicine
(51
[37.2%]),
care
(13
[9.5%]).
focused
on
treatment
(91
[66.4%]),
diagnosis
(60
[43.8%]),
or
disease
prevention
(29
[21.2%]).
Most
(136
[99.3%])
evaluated
inaccessible,
closed-source
did
not
enough
information
version
LLM
under
evaluation.
All
lacked
sufficient
description
characteristics,
including
temperature,
token
length,
fine-tuning
availability,
layers,
other
details.
describe
prompt
engineering
phase
study.
The
date
querying
reported
54
(39.4%)
(89
[65.0%])
used
subjective
means
define
successful
chatbot,
while
less
than
one-third
addressed
ethical,
regulatory,
patient
safety
implications
LLMs.
Conclusions
Relevance
In
this
chatbot
studies,
heterogeneous
may
CHART
standards.
Ethical,
considerations
are
crucial
as
grows
