Holistic Integrative Oncology,
Journal Year:
2024,
Volume and Issue:
3(1)
Published: Dec. 12, 2024
Abstract
Objective
To
examine
the
role
of
animal
models
in
tumor
research,
ethical
issues
surrounding
their
use,
and
potential
artificial
intelligence
technology
improving
welfare
addressing
concerns.
Methods
This
paper
reviews
cancer
research
considers
use.
The
various
types
applications
used
as
well
controversy
use
experimental
animals
feasibility
AI
issues,
were
examined
detail.
Results
Tumor
are
a
valuable
tool
for
advancing
our
understanding
formation
evaluating
efficacy
therapeutic
approaches.
implementation
has
to
diminish
or
supplant
necessity
experimentation,
enhance
precision
credibility
outcomes,
address
Conclusion
Animal
very
important
they
should
be
combined
with
development
science
improve
relieve
pressure.
Dermatological Reviews,
Journal Year:
2025,
Volume and Issue:
6(1)
Published: Jan. 17, 2025
ABSTRACT
Background
Artificial
intelligence
(AI)
is
transforming
dermatopathology
by
enhancing
diagnostic
accuracy,
efficiency,
and
precision
medicine.
Despite
its
promise,
challenges
such
as
dataset
biases,
underrepresentation
of
diverse
populations,
limited
transparency
hinder
widespread
adoption.
Addressing
these
gaps
can
set
a
new
standard
for
equitable
patient‐centered
care.
To
evaluate
how
AI
mitigates
improves
interpretability,
promotes
inclusivity
in
while
highlighting
novel
technologies
like
multimodal
models
explainable
(XAI).
Results
AI‐driven
tools
demonstrate
significant
improvements
precision,
particularly
through
that
integrate
histological,
genetic,
clinical
data.
Inclusive
frameworks,
the
Monk
scale,
advanced
segmentation
methods
effectively
address
biases.
However,
“black
box”
nature
AI,
ethical
concerns
about
data
privacy,
access
to
low‐resource
settings
remain.
Conclusion
offers
transformative
potential
dermatopathology,
enabling
equitable,
innovative
diagnostics.
Overcoming
persistent
will
require
collaboration
among
dermatopathologists,
developers,
policymakers.
By
prioritizing
inclusivity,
transparency,
interdisciplinary
efforts,
redefine
global
standards
foster
Sports
cardiology
focuses
on
athletes'
cardiovascular
health,
yet
sudden
cardiac
death
remains
a
significant
concern
despite
preventative
measures.
Prolonged
physical
activity
leads
to
notable
adaptations,
known
as
the
athlete's
heart,
which
can
resemble
certain
pathological
conditions,
complicating
accurate
diagnoses
and
potentially
leading
serious
consequences
such
unnecessary
exclusion
from
sports
or
missed
treatment
opportunities.
Wearable
devices,
including
smartwatches
smart
glasses,
have
become
prevalent
for
monitoring
health
metrics,
offering
potential
clinical
applications
cardiologists.
These
gadgets
are
capable
of
spotting
exercise-induced
arrhythmias,
uncovering
hidden
heart
problems,
crucial
information
training
recovery,
minimize
exercise-related
incidents
enhance
care.
However,
concerns
about
data
accuracy
actionable
value
obtained
persist.
A
major
challenge
lies
in
integration
artificial
intelligence
with
wearables,
research
gaps
remain
regarding
their
ability
provide
real-time,
reliable,
clinically
relevant
insights.
Combining
wearable
devices
improve
how
is
managed
used
cardiology.
Artificial
intelligence,
particularly
machine
learning,
classify,
predict,
draw
inferences
collected
by
revolutionizing
patient
usage.
Despite
intelligence's
proven
effectiveness
managing
chronic
limited
its
application
cardiology,
creates
critical
gap
that
needs
be
addressed.
This
review
examines
commercially
available
wearables
exploring
integrated
into
technology
advance
field.
Bioengineering,
Journal Year:
2024,
Volume and Issue:
11(4), P. 342 - 342
Published: March 31, 2024
Large
language
models
(LLMs)
are
transformer-based
neural
networks
that
can
provide
human-like
responses
to
questions
and
instructions.
LLMs
generate
educational
material,
summarize
text,
extract
structured
data
from
free
create
reports,
write
programs,
potentially
assist
in
case
sign-out.
combined
with
vision
interpreting
histopathology
images.
have
immense
potential
transforming
pathology
practice
education,
but
these
not
infallible,
so
any
artificial
intelligence
generated
content
must
be
verified
reputable
sources.
Caution
exercised
on
how
integrated
into
clinical
practice,
as
produce
hallucinations
incorrect
results,
an
over-reliance
may
lead
de-skilling
automation
bias.
This
review
paper
provides
a
brief
history
of
highlights
several
use
cases
for
the
field
pathology.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 28, 2025
Abstract
Background:
Although
artificial
intelligence
(AI)
has
shown
considerable
promise
in
dermatological
diagnostics,
its
real-world
clinical
validation
remains
limited.
This
study
aimed
to
evaluate
the
diagnostic
accuracy
and
decision-support
capabilities
of
GPT-4.5
a
routine
outpatient
dermatology
setting.
Methods:
A
total
402
dermatologic
cases
from
400
patients
were
retrospectively
analyzed
at
secondary-care
clinic.
was
provided
with
dermoscopic
images,
along
brief
metadata
(e.g.,
age,
lesion
location,
duration),
generate
differential
diagnoses
management
suggestions.
Model
outputs
compared
dermatologist
assessments.
Performance
metrics
included
accuracy,
sensitivity,
specificity,
precision,
F1
score.
Misclassification
patterns
also
reviewed.
Results:
achieved
an
overall
89.3%
correctly
identified
primary
diagnosis
as
top-ranked
suggestion
71.9%
cases.
Sensitivity
specificity
89.7%
91.4%,
respectively,
score
94.3%.
Clinical
guidance
recommendations
concordant
physician
decisions
91.0%
Diagnostic
higher
non-biopsied
(96.0%)
those
requiring
histopathological
confirmation
(84.2%).
Highest
performance
observed
infectious
(94.3%)
inflammatory
(96.2%)
dermatoses.
Misclassifications
most
common
pigmented
neoplasms
morphologically
similar
disorders.
Conclusion:
demonstrated
high
strong
alignment
dermatology,
especially
for
visually
distinct
conditions.
However,
declined
diagnostically
complex
or
ambiguous
These
findings
support
potential
supplementary
tool,
while
underscoring
need
multimodal
inputs,
oversight,
broader
prospective
prior
integration.
Frontiers in Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
7
Published: Sept. 2, 2024
Background
Accurate
ICD-10
coding
is
crucial
for
healthcare
reimbursement,
patient
care,
and
research.
AI
implementation,
like
ChatGPT,
could
improve
accuracy
reduce
physician
burden.
This
study
assessed
ChatGPT’s
performance
in
identifying
codes
nephrology
conditions
through
case
scenarios
pre-visit
testing.
Methods
Two
nephrologists
created
100
simulated
cases.
ChatGPT
versions
3.5
4.0
were
evaluated
by
comparing
AI-generated
against
predetermined
correct
codes.
Assessments
conducted
two
rounds,
2
weeks
apart,
April
2024.
Results
In
the
first
round,
of
assigning
diagnosis
was
91
99%
version
4.0,
respectively.
second
code
87%
4.0.
had
higher
than
(
p
=
0.02
0.002
round
respectively).
The
did
not
significantly
differ
between
rounds
>
0.05).
Conclusion
can
testing,
potentially
reducing
professionals’
workload.
However,
small
error
percentage
underscores
need
ongoing
review
improvement
systems
to
ensure
accurate
optimal
reliable
research
data.
Journal of Clinical Medicine,
Journal Year:
2024,
Volume and Issue:
13(19), P. 5909 - 5909
Published: Oct. 3, 2024
Background/Objectives:
The
use
of
artificial
intelligence
(AI)
in
dermatology
is
expanding
rapidly,
with
ChatGPT,
a
large
language
model
(LLM)
from
OpenAI,
showing
promise
patient
education,
clinical
decision-making,
and
teledermatology.
Despite
its
potential,
the
ethical,
clinical,
practical
implications
application
remain
insufficiently
explored.
This
study
aims
to
evaluate
effectiveness,
challenges,
future
prospects
ChatGPT
dermatology,
focusing
on
applications,
interactions,
medical
writing.
was
selected
due
broad
adoption,
extensive
validation,
strong
performance
dermatology-related
tasks.
Methods:
A
thorough
literature
review
conducted,
publications
related
dermatology.
search
included
articles
English
November
2022
August
2024,
as
this
period
captures
most
recent
developments
following
launch
2022,
ensuring
that
includes
latest
advancements
discussions
role
Studies
were
chosen
based
their
relevance
ethical
issues.
Descriptive
metrics,
such
average
accuracy
scores
reliability
percentages,
used
summarize
characteristics,
key
findings
analyzed.
Results:
has
shown
significant
potential
passing
specialty
exams
providing
reliable
responses
queries,
especially
for
common
dermatological
conditions.
However,
it
faces
limitations
diagnosing
complex
cases
like
cutaneous
neoplasms,
concerns
about
completeness
information
persist.
Ethical
issues,
including
data
privacy,
algorithmic
bias,
need
transparent
guidelines,
identified
critical
challenges.
Conclusions:
While
significantly
enhance
practice,
particularly
education
teledermatology,
integration
must
be
cautious,
addressing
complementing,
rather
than
replacing,
dermatologist
expertise.
Future
research
should
refine
ChatGPT’s
diagnostic
capabilities,
mitigate
biases,
develop
comprehensive
guidelines.
