Current Opinion in Ophthalmology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 27, 2024
Purpose
of
review
Vision
Language
Models
are
an
emerging
paradigm
in
artificial
intelligence
that
offers
the
potential
to
natively
analyze
both
image
and
textual
data
simultaneously,
within
a
single
model.
The
fusion
these
two
modalities
is
particular
relevance
ophthalmology,
which
has
historically
involved
specialized
imaging
techniques
such
as
angiography,
optical
coherence
tomography,
fundus
photography,
while
also
interfacing
with
electronic
health
records
include
free
text
descriptions.
This
then
surveys
fast-evolving
field
they
apply
current
ophthalmologic
research
practice.
Recent
findings
Although
models
incorporating
have
long
provenance
effective
multimodal
recent
development
exploiting
advances
technologies
transformer
autoencoder
models.
Summary
offer
assist
streamline
existing
clinical
workflow
whether
previsit,
during,
or
post-visit.
There
are,
however,
important
challenges
be
overcome,
particularly
regarding
patient
privacy
explainability
model
recommendations.
Current Opinion in Ophthalmology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 26, 2024
Purpose
of
review
Last
year
marked
the
development
first
foundation
model
in
ophthalmology,
RETFound,
setting
stage
for
generalizable
medical
artificial
intelligence
(GMAI)
that
can
adapt
to
novel
tasks.
Additionally,
rapid
advancements
large
language
(LLM)
technology,
including
models
such
as
GPT-4
and
Gemini,
have
been
tailored
specialization
evaluated
on
clinical
scenarios
with
promising
results.
This
explores
opportunities
challenges
further
these
technologies.
Recent
findings
RETFound
outperforms
traditional
deep
learning
specific
tasks,
even
when
only
fine-tuned
small
datasets.
LMMs
like
Med-Gemini
Medprompt
perform
better
than
out-of-the-box
ophthalmology
However,
there
is
still
a
significant
deficiency
ophthalmology-specific
multimodal
models.
gap
primarily
due
substantial
computational
resources
required
train
limitations
high-quality
Summary
Overall,
present
but
face
challenges,
particularly
need
high-quality,
standardized
datasets
training
specialization.
Although
has
focused
vision
models,
greatest
lie
advancing
which
more
closely
mimic
capabilities
clinicians.
Ophthalmology and Therapy,
Journal Year:
2024,
Volume and Issue:
13(10), P. 2543 - 2558
Published: Aug. 24, 2024
A
large
language
model
(LLM)
is
an
artificial
intelligence
(AI)
that
uses
natural
processing
(NLP)
to
understand,
interpret,
and
generate
human-like
responses
from
unstructured
text
input.
Its
real-time
response
capabilities
eloquent
dialogue
enhance
the
interactive
user
experience
in
human–AI
communication
like
never
before.
By
gathering
several
sources
on
internet,
LLM
chatbots
can
interact
respond
a
wide
range
of
queries,
including
problem
solving,
summarization,
creating
informative
notes.
Since
ophthalmology
one
medical
fields
integrating
image
analysis,
telemedicine,
AI,
other
technologies,
LLMs
are
likely
play
important
role
eye
care
near
future.
This
review
summarizes
performance
potential
applicability
according
currently
available
publications.
Current Opinion in Ophthalmology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 27, 2024
Purpose
of
review
Vision
Language
Models
are
an
emerging
paradigm
in
artificial
intelligence
that
offers
the
potential
to
natively
analyze
both
image
and
textual
data
simultaneously,
within
a
single
model.
The
fusion
these
two
modalities
is
particular
relevance
ophthalmology,
which
has
historically
involved
specialized
imaging
techniques
such
as
angiography,
optical
coherence
tomography,
fundus
photography,
while
also
interfacing
with
electronic
health
records
include
free
text
descriptions.
This
then
surveys
fast-evolving
field
they
apply
current
ophthalmologic
research
practice.
Recent
findings
Although
models
incorporating
have
long
provenance
effective
multimodal
recent
development
exploiting
advances
technologies
transformer
autoencoder
models.
Summary
offer
assist
streamline
existing
clinical
workflow
whether
previsit,
during,
or
post-visit.
There
are,
however,
important
challenges
be
overcome,
particularly
regarding
patient
privacy
explainability
model
recommendations.
