Can ChatGPT play a significant role in anatomy education? A scoping review
Morphologie,
Год журнала:
2025,
Номер
109(365), С. 100949 - 100949
Опубликована: Янв. 14, 2025
Язык: Английский
Integrating retrieval-augmented generation for enhanced personalized physician recommendations in web-based medical services: model development study
Frontiers in Public Health,
Год журнала:
2025,
Номер
13
Опубликована: Янв. 29, 2025
Background
Web-based
medical
services
have
significantly
improved
access
to
healthcare
by
enabling
remote
consultations,
streamlining
scheduling,
and
improving
information.
However,
providing
personalized
physician
recommendations
remains
a
challenge,
often
relying
on
manual
triage
schedulers,
which
can
be
limited
scalability
availability.
Objective
This
study
aimed
develop
validate
Retrieval-Augmented
Generation-Based
Physician
Recommendation
(RAGPR)
model
for
better
performance.
Methods
utilizes
comprehensive
dataset
consisting
of
646,383
consultation
records
from
the
Internet
Hospital
First
Affiliated
Xiamen
University.
The
research
primarily
evaluates
performance
various
embedding
models,
including
FastText,
SBERT,
OpenAI,
purposes
clustering
classifying
condition
labels.
Additionally,
assesses
effectiveness
large
language
models
(LLMs)
comparing
Mistral,
GPT-4o-mini,
GPT-4o.
Furthermore,
includes
participation
three
staff
members
who
contributed
evaluation
efficiency
RAGPR
through
questionnaires.
Results
results
highlight
different
levels
in
text
tasks.
FastText
has
an
F
1
-score
46%,
while
SBERT
OpenAI
outperform
it,
achieving
-scores
95
96%,
respectively.
analysis
highlights
LLMs,
with
GPT-4o
highest
95%,
followed
Mistral
GPT-4o-mini
94
92%,
In
addition,
ratings
are
as
follows:
4.56,
4.45
4.67.
Among
these,
identified
optimal
choices
due
their
balanced
performance,
cost
effectiveness,
ease
implementation.
Conclusion
improve
accuracy
personalization
web-based
services,
scalable
solution
patient-physician
matching.
Язык: Английский
Cognitive Domain Assessment of Artificial Intelligence Chatbots: A Comparative Study Between ChatGPT and Gemini’s Understanding of Anatomy Education
Medical Science Educator,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 15, 2025
Язык: Английский
Evaluating the Performance of Large Language Models in Anatomy Education Advancing Anatomy Learning with ChatGPT-4o
European Journal of Therapeutics,
Год журнала:
2025,
Номер
31(1), С. 35 - 43
Опубликована: Фев. 28, 2025
Objective:
Large
language
models
(LLMs),
such
as
ChatGPT,
Gemini,
and
Copilot,
have
garnered
significant
attention
across
various
domains,
including
education.
Their
application
is
becoming
increasingly
prevalent,
particularly
in
medical
education,
where
rapid
access
to
accurate
up-to-date
information
imperative.
This
study
aimed
assess
the
validity,
accuracy,
comprehensiveness
of
utilizing
LLMs
for
preparation
lecture
notes
school
anatomy
Methods:
The
evaluated
performance
four
large
models—ChatGPT-4o,
ChatGPT-4o-Mini,
Copilot—in
generating
students.
In
first
phase,
produced
by
these
using
identical
prompts
were
compared
a
widely
used
textbook
through
thematic
analysis
relevance
alignment
with
standard
educational
materials.
second
generated
content
validity
index
(CVI)
analysis.
threshold
values
S-CVI/Ave
S-CVI/UA
set
at
0.90
0.80,
respectively,
determine
acceptability
content.
Results:
ChatGPT-4o
demonstrated
highest
performance,
achieving
theme
success
rate
94.6%
subtheme
76.2%.
ChatGPT-4o-Mini
followed,
rates
89.2%
62.3%,
respectively.
Copilot
achieved
moderate
results,
91.8%
54.9%,
while
Gemini
showed
lowest
86.4%
52.3%.
Content
Validity
Index
analysis,
again
outperformed
other
models,
exceeding
thresholds
an
value
0.943
0.857.
met
(0.714)
but
fell
slightly
short
(0.800).
however,
exhibited
significantly
lower
CVI
results.
0.486
0.286,
obtained
scores,
0.286
0.143.
Conclusion:
assessed
two
distinct
methods,
revealing
that
performed
best
both
evaluations.
These
results
suggest
educators
students
could
benefit
from
adopting
supplementary
tool
generation.
Conversely,
like
require
further
improvements
meet
standards
necessary
reliable
use
Язык: Английский
Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications
Applied Sciences,
Год журнала:
2025,
Номер
15(8), С. 4234 - 4234
Опубликована: Апрель 11, 2025
Retrieval-Augmented
Generation
(RAG)
overcomes
the
main
barrier
for
adoption
of
LLM-based
chatbots
in
education:
hallucinations.
The
uncomplicated
architecture
RAG
makes
it
relatively
easy
to
implement
that
serve
specific
purposes
and
thus
are
capable
addressing
various
needs
educational
domain.
With
five
years
having
passed
since
introduction
RAG,
time
has
come
check
progress
attained
its
education.
This
paper
identifies
47
papers
dedicated
chatbots’
uses
kinds
purposes,
which
analyzed
terms
their
character,
target
support
provided
by
chatbots,
thematic
scope
knowledge
accessible
via
underlying
large
language
model,
character
evaluation.
Язык: Английский