Kosin Medical Journal,
Год журнала:
2024,
Номер
39(4), С. 229 - 237
Опубликована: Дек. 6, 2024
The
integration
of
artificial
intelligence
(AI)
technologies
into
medical
research
introduces
significant
ethical
challenges
that
necessitate
the
strengthening
frameworks.
This
review
highlights
issues
privacy,
bias,
accountability,
informed
consent,
and
regulatory
compliance
as
central
concerns.
AI
systems,
particularly
in
research,
may
compromise
patient
data
perpetuate
biases
if
they
are
trained
on
nondiverse
datasets,
obscure
accountability
owing
to
their
“black
box”
nature.
Furthermore,
complexity
role
affect
patients’
not
fully
grasp
extent
involvement
care.
Compliance
with
regulations
such
Health
Insurance
Portability
Accountability
Act
General
Data
Protection
Regulation
is
essential,
address
liability
cases
errors.
advocates
a
balanced
approach
autonomy
clinical
decisions,
rigorous
validation
ongoing
monitoring,
robust
governance.
Engaging
diverse
stakeholders
crucial
for
aligning
development
norms
addressing
practical
needs.
Ultimately,
proactive
management
AI’s
implications
vital
ensure
its
healthcare
improves
outcomes
without
compromising
integrity.
Algorithms,
Год журнала:
2025,
Номер
18(2), С. 96 - 96
Опубликована: Фев. 8, 2025
Computer
vision
and
artificial
intelligence
have
revolutionized
the
field
of
pathological
image
analysis,
enabling
faster
more
accurate
diagnostic
classification.
Deep
learning
architectures
like
convolutional
neural
networks
(CNNs),
shown
superior
performance
in
tasks
such
as
classification,
segmentation,
object
detection
pathology.
has
significantly
improved
accuracy
disease
diagnosis
healthcare.
By
leveraging
advanced
algorithms
machine
techniques,
computer
systems
can
analyze
medical
images
with
high
precision,
often
matching
or
even
surpassing
human
expert
performance.
In
pathology,
deep
models
been
trained
on
large
datasets
annotated
pathology
to
perform
cancer
diagnosis,
grading,
prognostication.
While
approaches
show
great
promise
challenges
remain,
including
issues
related
model
interpretability,
reliability,
generalization
across
diverse
patient
populations
imaging
settings.
International Journal of Scientific Research in Computer Science Engineering and Information Technology,
Год журнала:
2025,
Номер
11(1), С. 3594 - 3613
Опубликована: Фев. 25, 2025
This
article
presents
a
comprehensive
framework
for
human-AI
collaborative
workflow
optimization
in
automation-heavy
industries,
addressing
the
limitations
of
fully
automated
approaches
while
leveraging
complementary
strengths
human
judgment
and
artificial
intelligence.
We
introduce
Collaborative
Workflow
Intelligence
Framework
(CWIF),
which
establishes
structured
information
flows
decision
authority
boundaries
between
operators
AI
components
across
manufacturing,
logistics,
financial
services
domains.
Through
industry-specific
applications,
we
demonstrate
how
this
approach
enhances
production
scheduling,
quality
control,
supply
chain
efficiency,
transportation
optimization,
risk
assessment
maintaining
appropriate
oversight.
Our
methodology
provides
practical
guidance
system
architecture
design,
data
integration,
performance
evaluation,
phased
implementation,
with
particular
attention
to
ethical
considerations
including
worker
autonomy
skills
development.
The
balances
operational
efficiency
expertise,
creating
systems
that
suggest
process
improvements
identify
inefficiencies
preserving
complex
consequential
paradigm
represents
significant
advance
over
traditional
automation
approaches,
offering
organizations
path
rather
than
replaces
capabilities
technical,
organizational,
challenges
implementation.
Data & Metadata,
Год журнала:
2025,
Номер
4, С. 751 - 751
Опубликована: Март 19, 2025
Artificial
Intelligence
(AI)
technologies
are
promised
to
improve
digital
services
and
automate
tasks.
However,
there
still
significant
barriers
ensuring
that
AI
accessible
usable
by
a
broad
range
of
users.
As
solutions
proliferate
across
mainstream
systems
applications,
design-based
approaches
explicitly
bring
in
inclusive
human-centric
values
have
become
critical.
This
paper
provides
concerted
look
at
user-centered
design
the
intersection
AI,
accessibility,
usability,
proposing
framework
cuts
technological,
social,
regulatory
challenges.
Contributions
include
identifying
existing
work
current
literature
gaps,
key
research
questions,
methodology
explore
how
optimize
for
widest
possible
We
anchor
our
recommendations
with
use-inspired
case
an
AI-driven
public
transportation
assistant
individuals
diverse
physical
cognitive
abilities
demonstrate
could
benefit
real-world
applications.
On
basis
standards
theoretical
insights,
this
argues
process
should
be
proactive,
iterative,
implemented
participation
multiple
stakeholders.
In
their
systems,
is
meant
make
adaptive
users,
rather
than
users
being
thus
revealing
“AI
all”
can
indeed
realistic
realizable
paradigm.
Computer Applications in Engineering Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 27, 2024
Abstract
Occupational
Health
and
Safety
(OHS)
education
is
essential
for
preparing
engineering
students
to
maintain
safety
standards
prevent
workplace
hazards.
Traditional
learning
resources,
such
as
textbooks,
can
be
time‐consuming
inadequate
immediate,
context‐specific
queries.
Advanced
AI
chatbots
offer
interactive
immediate
feedback,
but
they
often
lack
specificity
depend
on
users'
prompting
skills,
which
not
all
possess.
This
study
introduces
“Genie‐on‐Demand,”
a
custom
chatbot
designed
address
students'
queries
with
precise,
curriculum‐aligned
responses.
Educators
train
the
using
specific
materials
by
uploading
PDFs,
ensuring
relevant
accurate
answers.
A
quasi‐experimental
was
conducted
106
electrical
divided
into
three
groups:
those
chatbot,
conventional
(ChatGPT),
employing
traditional
methods.
Results
demonstrated
that
significantly
improved
performance,
self‐efficacy,
technology
acceptance
compared
other
Students
reported
increased
confidence
effectiveness
in
assistant.
highlights
potential
of
customized
solutions
education,
versatile
applications
across
various
disciplines.
