Medical Teacher,
Год журнала:
2025,
Номер
unknown, С. 1 - 3
Опубликована: Фев. 12, 2025
The
integration
of
machine
learning
(ML)
and
large
language
models
(LLMs)
into
healthcare
is
transforming
diagnostics,
patient
care,
administrative
workflows.
However,
most
clinicians
lack
the
foundational
knowledge
to
critically
engage
with
these
tools,
creating
risks
overreliance
missed
oversight.
Just
as
understanding
computed
tomography
(CT)
physics
became
essential
for
its
safe
application,
must
acquire
basic
AI
literacy.
Practical
education
remains
absent
from
medical
curricula.
We
propose
a
modular
curriculum
using
Colab
notebooks
teach
concepts.
Colab's
free,
cloud-based,
interactive
environment
makes
it
accessible
engaging,
even
non-data
scientists.
This
hands-on
approach
emphasizes
practical
applications,
enabling
learners
explore
datasets,
build
ML
models,
interact
locally
run
LLMs,
fostering
critical
engagement
tools.
consists
five
interconnected
modules:
introduction
data
science,
exploring
predictive
modeling,
advanced
techniques
imaging,
working
LLMs.
Designed
integrate
school
science
threads,
provides
structured,
progressive
tailored
clinical
contexts.
Global
accessibility,
engagement,
design
make
this
adaptable
across
diverse
settings.
Emphasizing
ethical
considerations
local
relevance
enhances
impact.
next
step
notebook-based
authors'
thread.
To
support
broader
adoption,
teaching
guides
will
be
developed,
implementation
at
other
schools,
including
those
in
low-resource
settings,
while
leveraging
accessibility
regional
customization.
ECS Journal of Solid State Science and Technology,
Год журнала:
2024,
Номер
13(4), С. 047004 - 047004
Опубликована: Апрель 1, 2024
Early
diagnosis
through
noninvasive
tools
is
a
cornerstone
in
the
realm
of
personalized
and
medical
healthcare,
averting
direct/indirect
infection
transmission
directly
influencing
treatment
outcomes
patient
survival
rates.
In
this
context,
optical
biochip
breathomic
sensors
integrated
with
nanomaterials,
microfluidics,
artificial
intelligence
exhibit
potential
to
design
next-generation
intelligent
diagnostics.
This
cutting-edge
tool
offers
variety
advantages,
including
being
economical,
compact,
smart,
point
care,
highly
sensitive,
noninvasive.
makes
it
an
ideal
avenue
for
screening,
diagnosing,
prognosing
various
high-risk
diseases/disorders
by
detecting
associated
breath
biomarkers.
The
underlying
detection
mechanism
relies
on
interaction
biomarkers
sensors,
which
causes
modulations
fundamental
attributes,
such
as
surface
plasmon
resonance,
fluorescence,
reflectance,
absorption,
emission,
phosphorescence,
refractive
index.
Despite
these
remarkable
commercial
development
faces
challenges,
insufficient
support
from
clinical
trials,
concerns
about
cross-sensitivity,
challenges
related
production
scalability,
validation
issues,
regulatory
compliance,
contrasts
conventional
perspective
article
sheds
light
state
disease
diagnosis,
addresses
proposes
alternative
solutions,
explores
future
avenues
revolutionize
healthcare
Artificial
intelligence
(AI)
has
come
to
play
a
pivotal
role
in
revolutionizing
medical
practices,
particularly
the
field
of
pancreatic
cancer
detection
and
management.
As
leading
cause
cancer-related
deaths,
warrants
innovative
approaches
due
its
typically
advanced
stage
at
diagnosis
dismal
survival
rates.
Present
methods,
constrained
by
limitations
accuracy
efficiency,
underscore
necessity
for
novel
solutions.
AI-driven
methodologies
present
promising
avenues
enhancing
early
prognosis
forecasting.
Through
analysis
imaging
data,
biomarker
profiles,
clinical
information,
AI
algorithms
excel
discerning
subtle
abnormalities
indicative
with
remarkable
precision.
Moreover,
machine
learning
(ML)
facilitate
amalgamation
diverse
data
sources
optimize
patient
care.
However,
despite
huge
potential,
implementation
faces
various
challenges.
Issues
such
as
scarcity
comprehensive
datasets,
biases
algorithm
development,
concerns
regarding
privacy
security
necessitate
thorough
scrutiny.
While
offers
immense
promise
transforming
management,
ongoing
research
collaborative
efforts
are
indispensable
overcoming
technical
hurdles
ethical
dilemmas.
This
review
delves
into
evolution
AI,
application
detection,
challenges
considerations
inherent
integration.
The American Journal of Managed Care,
Год журнала:
2024,
Номер
30(Spec. No. 6), С. SP445 - SP451
Опубликована: Май 30, 2024
To
present
primary
care
physician
(PCP)
suggestions
for
design
and
implementation
of
a
decision
aid
(DA)
tool
to
support
patient-provider
shared
decision-making
on
lung
cancer
screening
(LCS).
The
integration
of
artificial
intelligence
(AI)
in
intensive
care
units
(ICUs)
presents
both
opportunities
and
challenges
for
critical
nurses.
This
study
delves
into
the
human
factor,
exploring
how
nurses
with
leadership
roles
perceive
impact
AI
on
their
professional
practice.
Clinics and Practice,
Год журнала:
2025,
Номер
15(1), С. 17 - 17
Опубликована: Янв. 14, 2025
Background:
Artificial
intelligence
(AI)
has
emerged
as
a
transformative
technology
in
healthcare,
with
its
integration
into
cardiac
surgery
offering
significant
advancements
precision,
efficiency,
and
patient
outcomes.
However,
comprehensive
understanding
of
AI’s
applications,
benefits,
challenges,
future
directions
is
needed
to
inform
safe
effective
implementation.
Methods:
A
systematic
review
was
conducted
following
PRISMA
guidelines.
Literature
searches
were
performed
PubMed,
Scopus,
Cochrane
Library,
Google
Scholar,
Web
Science,
covering
publications
from
January
2000
November
2024.
Studies
focusing
on
AI
applications
surgery,
including
risk
stratification,
surgical
planning,
intraoperative
guidance,
postoperative
management,
included.
Data
extraction
quality
assessment
using
standardized
tools,
findings
synthesized
narratively.
Results:
total
121
studies
included
this
review.
demonstrated
superior
predictive
capabilities
machine
learning
models
outperforming
traditional
scoring
systems
mortality
complication
prediction.
Robotic-assisted
enhanced
precision
minimized
trauma,
while
computer
vision
augmented
cognition
improved
guidance.
Postoperative
showed
potential
predicting
complications,
supporting
monitoring,
reducing
healthcare
costs.
challenges
such
data
quality,
validation,
ethical
considerations,
clinical
workflows
remain
barriers
widespread
adoption.
Conclusions:
the
revolutionize
by
enhancing
decision
making,
accuracy,
Addressing
limitations
related
bias,
regulatory
frameworks
essential
for
Future
research
should
focus
interdisciplinary
collaboration,
robust
testing,
development
transparent
ensure
equitable
sustainable
surgery.
International Journal of Scientific Research and Management (IJSRM),
Год журнала:
2024,
Номер
12(03), С. 6166 - 6178
Опубликована: Март 21, 2024
This
article
critically
examines
the
integration
of
artificial
intelligence
(AI)
into
work
environments,
focusing
on
ethical
implications
that
arise.
It
seeks
to
underscore
need
for
balancing
technological
advancements
with
protection
human
dignity
and
fairness,
exploring
how
AI's
transformative
potential
can
be
harmonized
core
tenets
rights.
The
utilizes
a
comprehensive
literature
review
construct
theoretical
framework
outlines
capabilities
considerations.
encompasses
interdisciplinary
foundations
AI,
including
its
roots
in
cognitive
psychology,
decision
theory,
computer
engineering.
further
delves
dilemmas
presented
by
AI
workplace,
such
as
privacy
concerns,
risk
bias,
issues
accountability,
broader
impact
exploration
is
aimed
at
understanding
complexities
labor
market
occupational
safety
health.
findings
highlight
dual
nature
both
catalyst
efficiency
innovation
source
challenge.
It's
important
include
lot
different
points
view
everyone
process
developing
make
it
more
fair
respect
Laws
policies
keep
changing
up
progress
protect
people
legally
from
possible
abuses.
Strong
moral
guidelines
clear
systems
are
also
needed
reduce
bias.
study's
originality
value
emphasize
discussions
rights
contexts,
contribute
technology
governance
discussions,
discuss
debates
dignity,
face
advancement.
Power System Technology,
Год журнала:
2023,
Номер
47(4), С. 167 - 182
Опубликована: Дек. 31, 2023
This
paper
delves
into
the
ethical
implications
of
deploying
artificial
intelligence
(AI)
in
decision-making
processes
related
to
end-of-life
care
within
healthcare
settings.
As
AI
continues
advance,
its
integration
introduces
both
opportunities
and
challenges,
particularly
navigating
sensitive
realm
care.
The
explores
this
intersection,
seeking
contribute
valuable
insights
ongoing
discourse
on
responsible
implementation
sector.
Central
considerations
is
principle
autonomy,
emphasizing
importance
respecting
patients'
ability
make
informed
decisions
about
their
preferences.
argues
for
need
design
systems
that
augment
rather
than
diminish
patient
ensuring
individuals
facing
remain
active
participants
process.
Furthermore,
principles
beneficence
non-maleficence
are
highlighted,
imperative
enhance
well-being
while
minimizing
risk
harm,
physical
psychological.
Justice
distribution
resources,
including
technologies,
crucial,
emphasizes
address
potential
disparities
access.
Transparent
explainable
advocated
foster
trust
among
patients,
families,
providers,
enabling
a
better
understanding
rationale
behind
AI-driven
recommendations.
[1]
concept
accountability
explored,
continued
responsibility
professionals
overseeing
validating
recommendations
maintain
standards.
Cultural
sensitivity
identified
as
key
consideration,
recognizing
diverse
perspectives
underscores
significance
designing
accommodate
cultural
nuances
avoid
imposing
values
may
conflict
with
beliefs
Additionally,
emotional
psychological
impact
AI-assisted
addressed,
maintaining
human
touch
acknowledging
roles
empathy,
compassion,
connection.
provides
comprehensive
examination
dimensions
surrounding
By
addressing
beneficence,
justice,
transparency,
accountability,
sensitivity,
impact,
it
offers
framework
aligns
healthcare,
ultimately
contributing
enhancement
practices.
Oncotarget,
Год журнала:
2024,
Номер
15(1), С. 607 - 608
Опубликована: Янв. 6, 2024
Generative
AI
is
revolutionizing
oncological
imaging,
enhancing
cancer
detection
and
diagnosis.
This
editorial
explores
its
impact
on
expanding
datasets,
improving
image
quality,
enabling
predictive
oncology.
We
discuss
ethical
considerations
introduce
a
unique
perspective
personalized
screening
using
AI-generated
digital
twins.
approach
could
optimize
protocols,
improve
early
detection,
tailor
treatment
plans.
While
challenges
remain,
generative
in
imaging
offers
unprecedented
opportunities
to
advance
care
patient
outcomes.
