International Journal of Community Medicine and Public Health,
Год журнала:
2025,
Номер
12(3), С. 1571 - 1577
Опубликована: Фев. 28, 2025
Breast
cancer
is
a
leading
cause
of
illness
and
death
worldwide,
with
early
detection
being
key
to
improving
survival
rates.
However,
in
low-resource
settings,
the
lack
accessible,
affordable,
efficient
screening
methods
significantly
hinders
timely
diagnosis
intervention.
Traditional
breast
methods,
such
as
mammography,
are
often
unavailable
or
impractical
these
regions
due
high
costs,
inadequate
infrastructure,
shortage
trained
professionals.
To
address
challenges,
artificial
intelligence
(AI)
technologies
have
emerged
promising
tools
enhance
screening.
AI-based
solutions,
AI-enhanced
ultrasound
imaging,
thermography,
mobile
applications,
potential
challenges
settings
by
offering
cost-effective,
portable,
user-friendly
alternatives.
These
innovations
can
facilitate
detection,
decrease
diagnostic
errors,
empower
healthcare
workers
limited
training
perform
screenings
effectively.
This
review
examines
role
AI
screening,
particularly
settings.
It
highlights
associated
conventional
explores
how
help
fill
gaps.
Success
stories
from
initiatives
RAD-AID
International,
Tata
memorial
centre,
AI-driven
project
Rwanda
demonstrate
feasibility
integrating
into
underserved
systems.
The
also
discusses
strategies
for
effective
integration,
including
data
collection,
infrastructure
development,
training.
Additionally,
it
outlines
future
directions
enhancing
applications
global
health.
has
bridge
gap
ensuring
that
populations
benefit
improved
better
health
outcomes.
provides
comprehensive
overview
offers
insights
Journal of Microbiological Methods,
Год журнала:
2024,
Номер
224, С. 106998 - 106998
Опубликована: Июль 15, 2024
Vaccine
development
stands
as
a
cornerstone
of
public
health
efforts,
pivotal
in
curbing
infectious
diseases
and
reducing
global
morbidity
mortality.
However,
traditional
vaccine
methods
are
often
time-consuming,
costly,
inefficient.
The
advent
artificial
intelligence
(AI)
has
ushered
new
era
design,
offering
unprecedented
opportunities
to
expedite
the
process.
This
narrative
review
explores
role
AI
development,
focusing
on
antigen
selection,
epitope
prediction,
adjuvant
identification,
optimization
strategies.
algorithms,
including
machine
learning
deep
learning,
leverage
genomic
data,
protein
structures,
immune
system
interactions
predict
antigenic
epitopes,
assess
immunogenicity,
prioritize
antigens
for
experimentation.
Furthermore,
AI-driven
approaches
facilitate
rational
design
immunogens
identification
novel
candidates
with
optimal
safety
efficacy
profiles.
Challenges
such
data
heterogeneity,
model
interpretability,
regulatory
considerations
must
be
addressed
realize
full
potential
development.
Integrating
emerging
technologies,
single-cell
omics
synthetic
biology,
promises
enhance
precision
scalability.
underscores
transformative
impact
highlights
need
interdisciplinary
collaborations
harmonization
accelerate
delivery
safe
effective
vaccines
against
diseases.
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(5), С. 1605 - 1605
Опубликована: Фев. 27, 2025
Background/Objectives:
Artificial
intelligence
(AI)
is
transforming
healthcare,
enabling
advances
in
diagnostics,
treatment
optimization,
and
patient
care.
Yet,
its
integration
raises
ethical,
regulatory,
societal
challenges.
Key
concerns
include
data
privacy
risks,
algorithmic
bias,
regulatory
gaps
that
struggle
to
keep
pace
with
AI
advancements.
This
study
aims
synthesize
a
multidisciplinary
framework
for
trustworthy
focusing
on
transparency,
accountability,
fairness,
sustainability,
global
collaboration.
It
moves
beyond
high-level
ethical
discussions
provide
actionable
strategies
implementing
clinical
contexts.
Methods:
A
structured
literature
review
was
conducted
using
PubMed,
Scopus,
Web
of
Science.
Studies
were
selected
based
relevance
ethics,
governance,
policy
prioritizing
peer-reviewed
articles,
analyses,
case
studies,
guidelines
from
authoritative
sources
published
within
the
last
decade.
The
conceptual
approach
integrates
perspectives
clinicians,
ethicists,
policymakers,
technologists,
offering
holistic
“ecosystem”
view
AI.
No
trials
or
patient-level
interventions
conducted.
Results:
analysis
identifies
key
current
governance
introduces
Regulatory
Genome—an
adaptive
oversight
aligned
trends
Sustainable
Development
Goals.
quantifiable
trustworthiness
metrics,
comparative
categories
applications,
bias
mitigation
strategies.
Additionally,
it
presents
interdisciplinary
recommendations
aligning
deployment
environmental
sustainability
goals.
emphasizes
measurable
standards,
multi-stakeholder
engagement
strategies,
partnerships
ensure
future
innovations
meet
practical
healthcare
needs.
Conclusions:
Trustworthy
requires
more
than
technical
advancements—it
demands
robust
safeguards,
proactive
regulation,
continuous
By
adopting
recommended
roadmap,
stakeholders
can
foster
responsible
innovation,
improve
outcomes,
maintain
public
trust
AI-driven
healthcare.
npj Digital Medicine,
Год журнала:
2025,
Номер
8(1)
Опубликована: Фев. 17, 2025
With
rapidly
evolving
artificial
intelligence
solutions,
healthcare
organizations
need
an
implementation
roadmap.
A
"clinical
trials"
informed
approach
can
promote
safe
and
impactful
of
intelligence.
This
framework
includes
four
phases:
(1)
Safety;
(2)
Efficacy;
(3)
Effectiveness
comparison
to
existing
standard;
(4)
Monitoring.
Combined
with
inter-institutional
collaboration
national
funding
support,
this
will
advance
safe,
usable,
effective,
equitable
deployments
in
healthcare.
Journal of Health Organization and Management,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 7, 2025
Purpose
This
study
explores
how
corporate
social
responsibility
(CSR)
and
artificial
intelligence
(AI)
can
be
combined
in
the
healthcare
industry
during
post-COVID-19
recovery
phase.
The
aim
is
to
showcase
this
fusion
help
tackle
inequalities,
enhance
accessibility
support
long-term
sustainability.
Design/methodology/approach
Adopting
a
viewpoint
approach,
leverages
existing
literature
case
studies
analyze
intersection
of
CSR
AI.
It
investigates
AI’s
capabilities
predictive
analytics,
telemedicine
resource
management
within
framework
principles.
Findings
Integrating
AI
profoundly
delivery
by
ensuring
equitable
access,
optimizing
allocation
fostering
trust
through
transparency
ethical
standards.
synergy
benefits
public
health
enhances
image
viability
organizations.
Research
limitations/implications
conceptual
relies
on
studies.
Future
research
should
empirically
test
proposed
models
frameworks
diverse
settings
validate
refine
these
insights.
Practical
implications
insights
from
directly
applied
organizations
develop
policies
practices
that
integrate
CSR.
integration
promote
standards,
operational
efficiency
and,
most
importantly,
improve
patient
outcomes.
Social
sector
carries
consequences.
plays
role
promoting
fairness
among
patients,
bridging
gaps
services,
boosting
independence
clear
responsible
use
technologies.
highlights
groundbreaking
impact
industry.
Originality/value
paper
offers
perspective
strategic
alignment
CSR,
presenting
novel
approach
creating
resilient
systems
era.
provides
managers
policymakers
with
valuable
leveraging
achieve
sustainable
solutions,
thereby
contributing
significantly
field.
Medical Sciences,
Год журнала:
2025,
Номер
13(1), С. 8 - 8
Опубликована: Янв. 11, 2025
Depression
poses
significant
challenges
to
global
healthcare
systems
and
impacts
the
quality
of
life
individuals
their
family
members.
Recent
advancements
in
artificial
intelligence
(AI)
have
had
a
transformative
impact
on
diagnosis
treatment
depression.
These
innovations
potential
significantly
enhance
clinical
decision-making
processes
improve
patient
outcomes
settings.
AI-powered
tools
can
analyze
extensive
data—including
medical
records,
genetic
information,
behavioral
patterns—to
identify
early
warning
signs
depression,
thereby
enhancing
diagnostic
accuracy.
By
recognizing
subtle
indicators
that
traditional
assessments
may
overlook,
these
enable
providers
make
timely
precise
decisions
are
crucial
preventing
onset
or
escalation
depressive
episodes.
In
terms
treatment,
AI
algorithms
assist
personalizing
therapeutic
interventions
by
predicting
effectiveness
various
approaches
for
individual
patients
based
unique
characteristics
history.
This
includes
recommending
tailored
plans
consider
patient’s
specific
symptoms.
Such
personalized
strategies
aim
optimize
overall
efficiency
healthcare.
theoretical
review
uniquely
synthesizes
current
evidence
applications
primary
care
depression
management,
offering
comprehensive
analysis
both
personalization
capabilities.
Alongside
advancements,
we
also
address
conflicting
findings
field
presence
biases
necessitate
important
limitations.
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
Population Health Management,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 3, 2025
In
recent
decades,
the
integration
of
artificial
intelligence
(AI)
into
health
care
has
revolutionized
diagnostics,
treatment
customization,
and
delivery.
low-resource
settings,
AI
offers
significant
potential
to
address
disparities
exacerbated
by
shortages
medical
professionals
other
resources.
However,
implementing
effectively
responsibly
in
these
settings
requires
careful
consideration
context-specific
needs
barriers
equitable
care.
This
article
explores
practical
deployment
environments
through
a
review
existing
literature
interviews
with
experts,
ranging
from
providers
administrators
tool
developers
government
consultants.
The
authors
highlight
4
critical
areas
for
effective
deployment:
infrastructure
requirements,
data
management,
education
training,
responsible
practices.
By
addressing
aspects,
proposed
framework
aims
guide
sustainable
integration,
minimizing
risk,
enhancing
access
underserved
regions.
