Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 19, 2024
Esthesioneuroblastomas
(ENBs)
present
unique
diagnostic
and
therapeutic
challenges
due
to
their
rare
complex
clinical
presentation.
In
recent
years,
artificial
intelligence
(AI)
machine
learning
(ML)
have
emerged
as
promising
tools
in
various
medical
specialties,
revolutionizing
accuracy,
treatment
planning,
patient
outcomes.
However,
application
ENBs
remains
relatively
unexplored.
This
comprehensive
literature
review
aims
evaluate
the
current
state
of
AI
ML
technologies
ENB
diagnosis,
radiological
histopathological
imaging,
planning.
By
synthesizing
existing
evidence
identifying
gaps
knowledge,
this
showcase
potential
benefits,
limitations,
future
directions
integrating
into
multidisciplinary
management
ENBs.
Healthcare,
Journal Year:
2024,
Volume and Issue:
12(23), P. 2330 - 2330
Published: Nov. 21, 2024
Artificial
Intelligence
(AI)
is
poised
to
revolutionize
numerous
aspects
of
human
life,
with
healthcare
among
the
most
critical
fields
set
benefit
from
this
transformation.
Medicine
remains
one
challenging,
expensive,
and
impactful
sectors,
challenges
such
as
information
retrieval,
data
organization,
diagnostic
accuracy,
cost
reduction.
AI
uniquely
suited
address
these
challenges,
ultimately
improving
quality
life
reducing
costs
for
patients
worldwide.
Despite
its
potential,
adoption
in
has
been
slower
compared
other
industries,
highlighting
need
understand
specific
obstacles
hindering
progress.
This
review
identifies
current
shortcomings
explores
possibilities,
realities,
frontiers
provide
a
roadmap
future
advancements.
Journal of Medical Education and Curricular Development,
Journal Year:
2024,
Volume and Issue:
11
Published: Jan. 1, 2024
Artificial
intelligence
(AI)
with
its
diverse
domains
such
as
expert
systems
and
machine
learning
already
has
multiple
potential
applications
in
medicine.
Based
on
the
latest
developments
multifaceted
field
of
AI,
it
will
play
a
pivotal
role
medicine,
high
transformative
areas,
including
drug
development,
diagnostics,
patient
care
monitoring.
In
pharmaceutical
industry
AI
is
also
rapidly
gaining
crucial
role.
The
introduction
innovative
medicines
requires
profound
background
knowledge
means
communication.
This
drives
us
to
intensively
engage
topic
medical
education,
which
becoming
more
demanding
due
dynamic
landscape,
among
other
things,
accelerated
even
by
digitalization
AI.
Therefore,
we
argue
for
incorporation
AI-based
tools
methods
personalized
learning,
diagnostic
pathways,
data
analysis,
prepare
healthcare
professionals
evolving
landscape
medicine
support
fluency
dealing
regular
contact
various
(Learning
AI).
Understanding
AI's
vast
caveats
well
basic
how
works
should
be
an
important
part
education
ensure
that
physicians
can
effectively
responsibly
leverage
their
daily
practice
scientific
communication
about
BACKGROUND
Publicly
available,
accessible,
and
user-friendly
artificial
intelligence
is
expected
to
serve
in
medical
processes.
Claude3,
a
newly
introduced
large-scale
multimodal
model,
has
demonstrated
significantly
superior
image
analysis
capabilities
compared
other
models
official
tests.
However,
there
no
research
reporting
the
potential
of
Claude3
analysis.
OBJECTIVE
To
explore
applications
Opus
on
dermatologic
images.
METHODS
Dermoscopy
dermatopathology
images
from
textbooks
were
collected,
question
templates
for
different
types
diseases
designed
Opus.
Three
dermatologists
used
structured
scoring
system
with
five
modules
evaluate
Opus'
based
recognition,
description,
completeness,
diagnosis,
clinical
application,
each
module
scored
out
5
total
score
25.
RESULTS
A
330
collected.
highest
pigmented
disorders
dermoscopy
(18.65/25),
followed
by
vascular
(15.97/25)
(15.86/25).
In
disorders,
its
(18.65/25)
was
higher
than
(14.54/25),
but
such
difference
existed
disorders.
Among
modules,
recognition
(3.65/5)
four
modules.
There
between
description
(3.14/5)
completeness
(3.22/5),
they
diagnostic
(2.47/5).
malignant
benign
diseases,
regardless
or
(all
p-values
<0.05),
impact
magnifications
(p>0.05)
number
evaluators.
CONCLUSIONS
exhibits
strong
disease
images,
can
accurately
describe
abnormalities
completely,
shows
high
sensitivity
diseases.
Apart
assistance,
could
potentially
be
widely
education
patient
communication.
CLINICALTRIAL
need
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 19, 2024
Esthesioneuroblastomas
(ENBs)
present
unique
diagnostic
and
therapeutic
challenges
due
to
their
rare
complex
clinical
presentation.
In
recent
years,
artificial
intelligence
(AI)
machine
learning
(ML)
have
emerged
as
promising
tools
in
various
medical
specialties,
revolutionizing
accuracy,
treatment
planning,
patient
outcomes.
However,
application
ENBs
remains
relatively
unexplored.
This
comprehensive
literature
review
aims
evaluate
the
current
state
of
AI
ML
technologies
ENB
diagnosis,
radiological
histopathological
imaging,
planning.
By
synthesizing
existing
evidence
identifying
gaps
knowledge,
this
showcase
potential
benefits,
limitations,
future
directions
integrating
into
multidisciplinary
management
ENBs.
