Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends
LatIA,
Год журнала:
2024,
Номер
3, С. 117 - 117
Опубликована: Окт. 18, 2024
Numerous
studies
have
highlighted
the
significance
of
artificial
intelligence
(AI)
in
breast
cancer
diagnosis.
However,
systematic
reviews
AI
applications
this
field
often
lack
cohesion,
with
each
study
adopting
a
unique
approach.
The
aim
is
to
provide
detailed
examination
AI's
role
diagnosis
through
citation
analysis,
helping
categorize
key
areas
that
attract
academic
attention.
It
also
includes
thematic
analysis
identify
specific
research
topics
within
category.
A
total
30,200
related
and
AI,
published
between
2015
2024,
were
sourced
from
databases
such
as
IEEE,
Scopus,
PubMed,
Springer,
Google
Scholar.
After
applying
inclusion
exclusion
criteria,
32
relevant
identified.
Most
these
utilized
classification
models
for
prediction,
high
accuracy
being
most
commonly
reported
performance
metric.
Convolutional
Neural
Networks
(CNN)
emerged
preferred
model
many
studies.
findings
indicate
both
quantity
quality
AI-based
algorithms
are
increases
given
years.
increasingly
seen
complement
healthcare
sector
clinical
expertise,
target
enhancing
accessibility
affordability
worldwide.
Язык: Английский
Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics
Dermatological Reviews,
Год журнала:
2025,
Номер
6(1)
Опубликована: Янв. 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
Язык: Английский
Electrophysiological Variations in Auditory Potentials in Chronic Tinnitus Individuals: Treatment Response and Tinnitus Laterality
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(3), С. 760 - 760
Опубликована: Янв. 24, 2025
Background:
This
study
investigates
electrophysiological
distinctions
in
auditory
evoked
potentials
(AEPs)
among
individuals
with
chronic
subjective
tinnitus,
a
specific
focus
on
the
impact
of
treatment
response
and
tinnitus
localisation.
Methods:
Early
AEPs,
known
as
Auditory
Brainstem
Responses
(ABR),
middle
termed
Middle
Latency
(AMLR),
were
analysed
patients
across
four
clinical
centers
an
attempt
to
verify
increased
neuronal
activity,
accordance
current
models.
Our
statistical
analyses
primarily
focused
discrepancies
time–domain
core
features
ABR
AMLR
signals,
including
amplitudes
latencies,
concerning
both
laterality.
Results:
Statistically
significant
differences
observed
wave
III
V
peak
amplitude,
Na
Nb
when
comparing
groups
based
their
treatment,
accompanied
by
varying
effect
sizes.
Conversely,
examining
categorised
laterality,
no
statistically
emerged.
Conclusions:
These
results
provide
valuable
insights
into
potential
influence
responses
AEPs.
However,
further
research
is
imperative
attain
comprehensive
understanding
underlying
mechanisms
at
play.
Язык: Английский
Gender Disparities in Melanoma: Advances in Diagnosis, Treatment, and the Role of Artificial Intelligence
Dermatological Reviews,
Год журнала:
2025,
Номер
6(1)
Опубликована: Фев. 1, 2025
ABSTRACT
Background
Melanoma,
a
highly
aggressive
skin
cancer,
demonstrates
significant
gender
disparities,
with
men
facing
later‐stage
diagnoses,
more
tumor
characteristics,
and
worse
survival
rates.
This
review
examines
the
biological,
behavioral,
environmental
factors
driving
these
alongside
recent
advancements
in
diagnosis
treatment.
Additionally,
it
explores
how
artificial
intelligence
(AI)
can
address
gender‐specific
differences
melanoma
incidence
outcomes.
Results
Gender
disparities
stem
from
biological
factors,
such
as
hormonal
genetic
differences,
behavioral
patterns
like
delayed
health‐seeking
among
men.
AI‐driven
diagnostic
tools,
including
convolutional
neural
networks
(CNNs),
show
promise
but
often
reflect
biases
training
data
sets,
underrepresenting
darker
tones
patterns.
Ensuring
diverse
integrating
“super‐prompts”
or
region‐specific
demographic
prompts,
utilizing
bias‐aware
algorithms
help
mitigate
biases,
thereby
improving
accuracy
equity.
Conclusion
Reducing
requires
innovative
technologies
equitable
healthcare
policies
education.
Early
detection
using
inclusive
AI
models
tailored
to
genders,
targeted
therapeutic
strategies,
is
critical
outcomes
for
high‐risk
groups,
particularly
underserved
populations.
Язык: Английский
Global, Regional, and National Burden of Hearing Loss in Adults Aged 60 Years and Older, 1990-2021: A Systematic Analysis for the Global Burden of Disease 2021
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
Abstract
Background:
Hearing
loss
is
the
third
leading
cause
of
years
lived
with
disability
(YLDs)
worldwide,
imposing
a
substantial
burden
on
older
adults.
This
study
utilized
data
from
Global
Burden
Disease
(GBD)
2021
to
analyze
hearing
among
individuals
aged
60
and
1990
project
future
trends.
Methods:
Data
prevalence
YLDs
rates
were
extracted
GBD
2021.
The
disease
was
analyzed
by
age,
sex,
socio-demographic
index
(SDI).
Joinpoint
regression
employed
assess
temporal
trends,
while
age-period-cohort
(APC)
models
used
evaluate
independent
effects
period,
cohort.
Bayesian
(BAPC)
applied
trends
in
burden.
Results:
From
2021,
age-standardized
rate
(ASPR)
(ASYR)
exhibited
an
increasing
trend
globally,
fastest
growth
observed
65-69
age
group
(ASPR:
0.137,
95%
uncertainty
interval
[UI]:
0.110-0.163;
ASYR:
0.179,
UI:
0.150-0.209).
Middle
SDI
regions
experienced
highest
Males
had
higher
than
females,
peak
occurring
60-64
groups,
respectively.
Health
inequality
analysis
indicated
that
absolute
disparities
narrowed,
relative
inequalities
continued
increase
low
regions.
Projections
2022
2050
suggested
ASPR
ASYR
would
continue
rise,
particularly
80
older.
Conclusion:Hearing
poses
significant
public
health
challenge
adults,
necessitating
urgent
interventions
such
as
early
screening,
expanded
access
aids,
environmental
noise
control.
Future
efforts
should
prioritize
resource-limited
implement
comprehensive
strategies
mitigate
growing
loss.
Язык: Английский
Amelanotic Melanoma: Diagnostic Challenges, Treatment Innovations, and the Emerging Role of AI in Early Detection
Journal of Medicine Surgery and Public Health,
Год журнала:
2025,
Номер
unknown, С. 100189 - 100189
Опубликована: Март 1, 2025
Язык: Английский
Artificial Intelligence for Medicine, Surgery, and Public Health
Journal of Medicine Surgery and Public Health,
Год журнала:
2024,
Номер
unknown, С. 100141 - 100141
Опубликована: Окт. 1, 2024
Язык: Английский
AI-Driven Smart Auditory Health Systems: Bridging Audiology and Public Health in Low- and Middle-Income Countries
IgMin Research,
Год журнала:
2024,
Номер
2(12), С. 950 - 957
Опубликована: Дек. 5, 2024
Hearing
loss
is
a
critical
global
health
issue
that
affects
over
1.5
billion
people
worldwide,
with
disproportionate
burden
in
Low-
and
Middle-Income
Countries
(LMICs).
These
regions
face
significant
challenges,
including
limited
access
to
audiological
services,
shortage
of
healthcare
professionals,
lack
affordable
hearing
solutions.
barriers
lead
delayed
diagnoses,
inadequate
management,
negative
impact
on
individuals'
quality
life,
education,
employment
opportunities.
The
advent
Artificial
Intelligence
(AI)
advanced
technologies
offers
innovative
pathways
address
these
longstanding
challenges.
This
review
introduces
the
AI-driven
smart
Auditory
Health
Systems
(SAHS)
concept.
holistic
approach
integrates
AI,
wearable
devices,
Internet
Things
(IoT)
technology,
big
data
analytics
enhance
prevention,
diagnosis,
management
auditory
disorders.
SAHS
systems
can
provide
real-time
monitoring,
early
detection
loss,
personalized
care
solutions
tailored
individual
population
needs.
offer
community-level
interventions,
noise
pollution
monitoring
data-driven
public
strategies.
Focusing
LMIC
context,
this
explores
technological
framework,
applications,
ethical
considerations,
logistical
challenges
implementing
SAHS.
By
leveraging
technologies,
has
potential
bridge
gaps
access,
improve
outcomes,
transform
delivery
resource-constrained
settings.
underscores
importance
collaborative
efforts
research,
policy
development,
capacity
building
ensure
equitable
adoption
SAHS,
thereby
addressing
disparities
globally.
Язык: Английский