Journal of Craniofacial Surgery,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 2, 2024
Background:
The
advent
of
Large
Language
Models
(LLMs)
like
ChatGPT
has
introduced
significant
advancements
in
various
surgical
disciplines.
These
developments
have
led
to
an
increased
interest
the
utilization
LLMs
for
Current
Procedural
Terminology
(CPT)
coding
surgery.
With
CPT
being
a
complex
and
time-consuming
process,
often
exacerbated
by
scarcity
professional
coders,
there
is
pressing
need
innovative
solutions
enhance
efficiency
accuracy.
Methods:
This
observational
study
evaluated
effectiveness
5
publicly
available
large
language
models—Perplexity.AI,
Bard,
BingAI,
3.5,
4.0—in
accurately
identifying
codes
craniofacial
procedures.
A
consistent
query
format
was
employed
test
each
model,
ensuring
inclusion
detailed
procedure
components
where
necessary.
responses
were
classified
as
correct,
partially
or
incorrect
based
on
their
alignment
with
established
specified
Results:
results
indicate
that
while
no
overall
association
between
type
AI
model
correctness
code
identification,
are
notable
differences
performance
simple
among
models.
Specifically,
4.0
showed
higher
accuracy
codes,
whereas
Perplexity.AI
Bard
more
codes.
Discussion:
use
chatbots
surgery
presents
promising
avenue
reducing
administrative
burden
associated
costs
manual
coding.
Despite
lower
rates
compared
specialized,
trained
algorithms,
accessibility
minimal
training
requirements
make
them
attractive
alternatives.
also
suggests
priming
models
operative
notes
may
accuracy,
offering
resource-efficient
strategy
improving
clinical
practice.
Conclusions:
highlights
feasibility
potential
benefits
integrating
into
process
findings
advocate
further
refinement
improve
practicality,
suggesting
future
AI-assisted
could
become
standard
component
workflows,
aligning
ongoing
digital
transformation
health
care.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(22), P. 9779 - 9779
Published: Nov. 9, 2024
This
study
explored
the
transformative
potential
of
generative
artificial
intelligence
(GAI)
for
achieving
UN
Sustainable
Development
Goal
on
Quality
Education
(SDG4),
emphasizing
its
interconnectedness
with
other
SDGs.
A
proprietary
algorithm
and
cocitation
network
analysis
were
used
to
identify
analyze
SDG
features
in
GAI
research
publications
(n
=
1501).
By
examining
GAI’s
implications
ten
SDG4
targets,
findings
advocate
a
collaborative,
ethical
approach
integrating
GAI,
policy
practice
developments
that
ensure
technological
advancements
align
overarching
goals
SDG4.
The
results
highlight
multifaceted
impact
First,
this
paper
outlines
framework
leverages
enhance
educational
equity,
quality,
lifelong
learning
opportunities.
highlighting
synergy
between
SDGs,
such
as
reducing
inequalities
(SDG10)
promoting
gender
equality
(SDG5),
underscores
need
an
integrated
utilizing
GAI.
Moreover,
it
advocates
personalized
learning,
equitable
technology
access,
adherence
AI
principles,
fostering
global
citizenship,
proposing
strategic
alignment
applications
broader
agenda.
Next,
introduces
significant
challenges,
including
concerns,
data
privacy,
risk
exacerbating
digital
divide.
Overall,
our
underscore
critical
role
reforms
innovative
practices
navigating
challenges
harnessing
opportunities
presented
by
education,
thereby
contributing
comprehensive
discourse
technology’s
advancing
education
sustainable
development.
Journal of Craniofacial Surgery,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 2, 2024
Background:
The
advent
of
Large
Language
Models
(LLMs)
like
ChatGPT
has
introduced
significant
advancements
in
various
surgical
disciplines.
These
developments
have
led
to
an
increased
interest
the
utilization
LLMs
for
Current
Procedural
Terminology
(CPT)
coding
surgery.
With
CPT
being
a
complex
and
time-consuming
process,
often
exacerbated
by
scarcity
professional
coders,
there
is
pressing
need
innovative
solutions
enhance
efficiency
accuracy.
Methods:
This
observational
study
evaluated
effectiveness
5
publicly
available
large
language
models—Perplexity.AI,
Bard,
BingAI,
3.5,
4.0—in
accurately
identifying
codes
craniofacial
procedures.
A
consistent
query
format
was
employed
test
each
model,
ensuring
inclusion
detailed
procedure
components
where
necessary.
responses
were
classified
as
correct,
partially
or
incorrect
based
on
their
alignment
with
established
specified
Results:
results
indicate
that
while
no
overall
association
between
type
AI
model
correctness
code
identification,
are
notable
differences
performance
simple
among
models.
Specifically,
4.0
showed
higher
accuracy
codes,
whereas
Perplexity.AI
Bard
more
codes.
Discussion:
use
chatbots
surgery
presents
promising
avenue
reducing
administrative
burden
associated
costs
manual
coding.
Despite
lower
rates
compared
specialized,
trained
algorithms,
accessibility
minimal
training
requirements
make
them
attractive
alternatives.
also
suggests
priming
models
operative
notes
may
accuracy,
offering
resource-efficient
strategy
improving
clinical
practice.
Conclusions:
highlights
feasibility
potential
benefits
integrating
into
process
findings
advocate
further
refinement
improve
practicality,
suggesting
future
AI-assisted
could
become
standard
component
workflows,
aligning
ongoing
digital
transformation
health
care.
