Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI
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.
Язык: Английский
Artificial Intelligence in Ophthalmology: Advantages and Limits
Applied Sciences,
Год журнала:
2025,
Номер
15(4), С. 1913 - 1913
Опубликована: Фев. 12, 2025
In
recent
years,
artificial
intelligence
has
begun
to
play
a
salient
role
in
various
medical
fields,
including
ophthalmology.
This
extensive
review
is
addressed
ophthalmologists
and
aims
capture
the
current
landscape
future
potential
of
AI
applications
for
eye
health.
From
automated
retinal
screening
processes
machine
learning
models
predicting
progression
ocular
conditions
AI-driven
decision
support
systems
clinical
settings,
this
paper
provides
comprehensive
overview
implications
The
development
opened
new
horizons
ophthalmology,
offering
innovative
solutions
improve
accuracy
efficiency
disease
diagnosis
management.
importance
lies
its
strengthen
collaboration
between
researchers,
ophthalmologists,
specialists,
leading
transformative
findings
early
identification
treatment
diseases.
By
combining
with
cutting-edge
imaging
methods,
novel
biomarkers,
data-driven
approaches,
can
make
more
informed
decisions
provide
personalized
their
patients.
Furthermore,
emphasizes
translation
basic
research
outcomes
into
applications.
We
do
hope
will
act
as
significant
resource
data
scientists,
healthcare
professionals,
managers
system
who
are
interested
application
Язык: Английский
A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation
Ophthalmology and Therapy,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 21, 2025
Fundus
fluorescein
angiography
(FFA)
serves
as
the
current
gold
standard
for
visualizing
retinal
vasculature
and
detecting
various
fundus
diseases,
but
its
interpretation
is
labor-intensive
requires
much
expertise
from
ophthalmologists.
The
medical
application
of
artificial
intelligence
(AI),
especially
deep
learning
machine
learning,
has
revolutionized
field
automatic
FFA
image
analysis,
leading
to
rapid
advancements
in
AI-assisted
lesion
detection,
diagnosis,
report
generation.
This
review
examined
studies
PubMed,
Web
Science,
Google
Scholar
databases
January
2019
August
2024,
with
a
total
23
articles
incorporated.
By
integrating
research
findings,
this
highlights
crucial
breakthroughs
analysis
explores
their
potential
implications
ophthalmic
clinical
practice.
These
advances
have
shown
promising
results
improving
diagnostic
accuracy
workflow
efficiency.
However,
further
needed
enhance
model
transparency
ensure
robust
performance
across
diverse
populations.
Challenges
such
data
privacy
technical
infrastructure
remain
broader
applications.
Язык: Английский
Evaluating the Efficacy of Artificial Intelligence-Driven Chatbots in Addressing Queries on Vernal Conjunctivitis
Cureus,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 26, 2025
Background
Vernal
keratoconjunctivitis
(VKC)
is
a
recurrent
allergic
eye
disease
that
requires
accurate
patient
education
to
ensure
proper
management.
AI-driven
chatbots,
such
as
Google
Gemini
Advanced
(Mountain
View,
California,
US),
are
increasingly
being
explored
potential
tools
for
providing
medical
information.
This
study
evaluates
the
accuracy,
reliability,
and
clinical
applicability
of
in
addressing
VKC-related
queries.
Objective
To
assess
performance
delivering
medically
relevant
information
about
VKC
evaluate
its
reliability
based
on
expert
ratings.
Methods
A
total
125
responses
generated
by
25
questions
were
assessed
two
independent
cornea
specialists.
Responses
rated
completeness,
harm
using
5-point
Likert
scale
(1-5).
Inter-rater
was
measured
Cronbach's
alpha.
categorized
into
highly
(score
5),
minor
inconsistencies
4),
inaccurate
(scores
1-3).
Results
demonstrated
high
inter-rater
(Cronbach's
alpha
=
0.92,
95%
CI:
0.87-0.94).
Of
responses,
108
(86.4%)
5)
while
17
(13.6%)
had
4)
but
posed
no
harm.
No
classified
or
potentially
harmful.
The
combined
mean
score
4.88
±
0.31,
reflecting
strong
agreement
between
raters.
chatbot
consistently
provided
reliable
across
diagnostic,
treatment,
prognosis-related
queries,
with
gaps
complex
grading
treatment-related
discussions.
Discussion
findings
support
use
chatbots
like
ophthalmology.
exhibited
accuracy
consistency,
particularly
general
However,
areas
improvement
remain,
especially
detailed
guidance
treatment
protocols
ensuring
completeness
questions.
Conclusion
demonstrates
VKC,
making
it
valuable
tool
education.
While
consistent
generally
accurate,
oversight
remains
necessary
refine
AI-generated
content
applications.
Further
research
needed
enhance
chatbots'
ability
provide
nuanced
advice
integrate
them
safely
ophthalmic
decision-making.
Язык: Английский
Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models
Clinical Chemistry and Laboratory Medicine (CCLM),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 18, 2025
Abstract
Objectives
Accurate
medical
laboratory
reports
are
essential
for
delivering
high-quality
healthcare.
Recently,
advanced
artificial
intelligence
models,
such
as
those
in
the
ChatGPT
series,
have
shown
considerable
promise
this
domain.
This
study
assessed
performance
of
specific
GPT
models-namely,
4o,
o1,
and
o1
mini-in
identifying
errors
within
providing
treatment
recommendations.
Methods
In
retrospective
study,
86
Nucleic
acid
test
report
seven
upper
respiratory
tract
pathogens
were
compiled.
There
285
from
four
common
error
categories
intentionally
randomly
introduced
into
generated
incorrected
reports.
models
tasked
with
detecting
these
errors,
using
three
senior
scientists
(SMLS)
interns
(MLI)
control
groups.
Additionally,
generating
accurate
reliable
recommendations
following
positive
outcomes
based
on
corrected
χ2
tests,
Kruskal-Wallis
Wilcoxon
tests
used
statistical
analysis
where
appropriate.
Results
comparison
SMLS
or
MLI,
accurately
detected
types,
average
detection
rates
88.9
%(omission),
91.6
%
(time
sequence),
91.7
(the
same
individual
acted
both
inspector
reviewer).
However,
rate
result
input
format
by
was
only
51.9
%,
indicating
a
relatively
poor
aspect.
exhibited
substantial
to
almost
perfect
agreement
total
(kappa
[min,
max]:
0.778,
0.837).
between
MLI
moderately
lower
0.632,
0.696).
When
it
comes
reading
all
reports,
showed
obviously
reduced
time
compared
(all
p<0.001).
Notably,
our
also
found
GPT-o1
mini
model
had
better
consistency
identification
than
model,
which
that
GPT-4o
model.
The
pairwise
comparisons
model’s
outputs
across
repeated
runs
0.912,
0.996).
GPT-o1(all
significantly
outperformed
p<0.0001).
Conclusions
capability
some
accuracy
reliability
competent,
especially,
potentially
reducing
work
hours
enhancing
clinical
decision-making.
Язык: Английский
A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD)
Bioengineering,
Год журнала:
2024,
Номер
11(7), С. 711 - 711
Опубликована: Июль 13, 2024
The
rapid
advancement
of
computational
infrastructure
has
led
to
unprecedented
growth
in
machine
learning,
deep
and
computer
vision,
fundamentally
transforming
the
analysis
retinal
images.
By
utilizing
a
wide
array
visual
cues
extracted
from
fundus
images,
sophisticated
artificial
intelligence
models
have
been
developed
diagnose
various
disorders.
This
paper
concentrates
on
detection
Age-Related
Macular
Degeneration
(AMD),
significant
condition,
by
offering
an
exhaustive
examination
recent
learning
methodologies.
Additionally,
it
discusses
potential
obstacles
constraints
associated
with
implementing
this
technology
field
ophthalmology.
Through
systematic
review,
research
aims
assess
efficacy
techniques
discerning
AMD
different
modalities
as
they
shown
promise
disorders
diagnosis.
Organized
around
prevalent
datasets
imaging
techniques,
initially
outlines
assessment
criteria,
image
preprocessing
methodologies,
frameworks
before
conducting
thorough
investigation
diverse
approaches
for
detection.
Drawing
insights
more
than
30
selected
studies,
conclusion
underscores
current
trajectories,
major
challenges,
future
prospects
diagnosis,
providing
valuable
resource
both
scholars
practitioners
domain.
Язык: Английский
The role of artificial intelligence in macular hole management: A scoping review
Survey of Ophthalmology,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 1, 2024
Язык: Английский
EyeMatics – Multizentrische Datenauswertung von Real-World-Daten mit interoperabler medizinischer Informatik
Deleted Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 14, 2024
The
evaluation
of
real-world
data
(RWD)
enables
insights
to
be
gained
from
a
wide
range
patient
collected
in
routine
clinical
practice.
In
addition,
multicenter
analyses
represent
broad
and
representative
population
have
the
potential
capture
actual
treatment
situation.
As
basis
for
this,
definition
datasets
an
infrastructure
exchange
is
necessary.
Data
integration
centers
(DIC)
already
been
established
at
(university)
hospitals
throughout
Germany
order
extract
RWD
scientific
various
source
systems
integrate
them
into
research-compatible
infrastructures.
project
described
here
aims
demonstrate
added
value
this
using
case
application
ophthalmology,
defining
core
dataset
as
ophthalmology
extension
module
establishing
cross-site
infrastructure.
first
step,
success
eye
diseases
treated
with
intravitreal
injection
(IVI)
should
improved.
To
achieve
goal
dashboard
provided
that
clearly
visualizes
merged
data.
Furthermore,
algorithms
will
developed
identify
new
imaging
biomarkers
can
used
monitoring
predict
outcomes.
Язык: Английский