Advances in healthcare information systems and administration book series,
Год журнала:
2024,
Номер
unknown, С. 1 - 12
Опубликована: Июнь 5, 2024
This
chapter
focuses
on
the
best
relationship
between
humanity
and
AI
in
healthcare.
The
focus
this
is
patient-centered
approach
hospitals
with
AI.
research
emphasizes
human
resource
talent
to
foster
agility
healthcare
industries
for
managing
high-end
growth.
In
passionate
zealous
world,
HRM
promotes
advanced,
quick,
fast
decisions.
Innovations
through
promote
strategies
give
birth
opportunities,
if
plans
activities
properly
adopts
new
technologies
sector
from
time
as
per
requirement,
then
HR
works
more
efficiently
effectively.
Professionals
are
focusing
fostering
agility,
making
work
accurate
time-saving,
avoiding
replications,
good
decision-making
short-term
long-term
welfare
of
industries.
To
enhance
strategic
capabilities,
humans
must
embrace
learning
an
environment
innovation,
knowledge
development
practices.
discusses
healthcare's
growth
patients'
priorities.
This
paper
examines
the
potential
of
Human-Centered
AI
(HCAI)
solutions
to
support
radiologists
in
diagnosing
prostate
cancer.
Prostate
cancer
is
one
most
prevalent
and
increasing
cancers
among
men.
The
scarcity
raises
concerns
about
their
ability
address
growing
demand
for
diagnosis,
leading
a
significant
surge
workload
radiologists.
Drawing
on
an
HCAI
approach,
we
sought
understand
current
practices
concerning
radiologists'
work
detecting
cancer,
as
well
challenges
they
face.
findings
from
our
empirical
studies
point
toward
that
has
expedite
informed
decision-making
enhance
accuracy,
efficiency,
consistency.
particularly
beneficial
collaborative
diagnosis
processes.
We
discuss
these
results
introduce
design
recommendations
concepts
domain
with
aim
amplifying
professional
capabilities
ACM Transactions on Computing for Healthcare,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 4, 2025
This
paper
offers
an
extensive
survey
of
one
the
fundamental
aspects
trustworthiness
Artificial
Intelligence
(AI)
in
healthcare,
namely
uncertainty,
focusing
on
large
panoply
recent
studies
addressing
connection
between
AI,
and
healthcare.
The
concept
uncertainty
is
a
recurring
theme
across
multiple
disciplines,
with
varying
focuses
approaches.
Here,
we
focus
diverse
nature
medical
applications,
emphasizing
importance
quantifying
model
predictions
its
advantages
specific
clinical
settings.
Questions
that
emerge
this
context
range
from
guidelines
for
AI
integration
healthcare
domain
to
ethical
deliberations
their
compatibility
cutting-edge
research.
Together
description
main
works
context,
also
discuss
that,
as
medicine
evolves
introduces
novel
sources
there
need
more
versatile
quantification
methods
be
developed
collaboratively
by
researchers
professionals.
Finally,
acknowledge
limitations
current
different
facets
within
domain.
In
particular,
identify
relative
paucity
approaches
user’s
perception
accordingly
trustworthiness.
ACM Transactions on Computer-Human Interaction,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 12, 2025
Nasogastric
tubes
(NGTs)
are
feeding
that
inserted
through
the
nose
into
stomach
to
deliver
nutrition
or
medication.
If
not
placed
correctly,
they
can
cause
serious
harm,
even
death
patients.
Recent
AI
developments
demonstrate
feasibility
of
robustly
detecting
NGT
placement
from
Chest
X-ray
images
reduce
risks
sub-optimally
critically
NGTs
being
missed
delayed
in
their
detection,
but
gaps
remain
clinical
practice
integration.
In
this
study,
we
present
a
human-centered
approach
problem
and
describe
insights
derived
following
contextual
inquiry
in-depth
interviews
with
15
stakeholders.
The
helped
understand
challenges
existing
workflows,
how
best
align
technical
capabilities
user
needs
expectations.
We
discovered
trade-offs
complexities
need
consideration
when
choosing
suitable
workflow
stages,
target
users,
design
configurations
for
different
proposals.
explored
balance
benefits
healthcare
staff
patients
within
broader
organizational,
technical,
medical-legal
constraints.
also
identified
data
issues
related
edge
cases
biases
affect
model
training
evaluation;
documentation
practices
influence
preparation
labelling;
measure
relevant
outcomes
reliably
future
evaluations.
discuss
our
work
informs
development
applications
clinically
useful,
ethical,
acceptable
real-world
services.
Abstract
Over
the
last
decade,
we’ve
witnessed
re-convergence
of
Human–computer
Interaction
(HCI)
to
emerging
spaces
such
as
artificial
intelligence
(AI),
big
data,
edge
computing
and
so
on.
Specific
agentistic
turn
in
HCI,
researchers
practitioners
have
grappled
with
central
issues
around
AI
a
research
programme
or
methodological
instrument—from
cognitive
science
emphasis
on
technical
computational
systems
philosophy
ethics
focus
agency,
perception,
interpretation,
action,
meaning,
understanding.
Even
proliferation
discourses
globally,
recognised
how
discourse
from
Africa
is
undermined.
Consequently,
interested
HCI
identified
growing
need
for
exploring
potentials
challenges
associated
design
adoption
AI-mediated
technologies
critical
sectors
economy
matter
socio-technical
interest
concern.
In
this
chapter,
we
consider
normative
framing
Africa—from
ethical,
responsible,
trustworthy—can
be
better
understood
when
their
subject
matters
are
conceived
Latourian
“Distributed
Concern”.
Building
Bruno
Latour’s
analytical
“matters
facts”
concerns”,
argue
that
operationalising
trustworthy
distributed
concern—which
socio-cultural,
geo-political,
economic,
pedagogical,
technical,
on—entails
continual
process
reconciling
value(s).
To
highlight
scalable
dimension
trustworthiness
design,
engaged
sustained
discursive
argumentation
showing
procedural
analysis
trust
spectrum
might
explicate
modalities
normalisation
lawful,
robust.
ACM Computing Surveys,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 16, 2025
Dialog
systems
(e.g.,
chatbots)
have
been
widely
studied,
yet
related
research
that
leverages
artificial
intelligence
(AI)
and
natural
language
processing
(NLP)
is
constantly
evolving.
These
typically
developed
to
interact
with
humans
in
the
form
of
speech,
visual,
or
text
conversation.
As
continue
adopt
dialog
for
various
objectives,
there
a
need
involve
every
facet
development
life
cycle
synergistic
augmentation
both
system
actors
real-world
settings.
We
provide
holistic
literature
survey
on
recent
advancements
human-centered
(HCDS).
Specifically,
we
background
context
surrounding
machine
learning-based
AI.
then
bridge
gap
between
two
AI
sub-fields
organize
works
HCDS
under
three
major
categories
(i.e.,
Human-Chatbot
Collaboration,
Alignment,
Human-Centered
Chatbot
Design
&
Governance).
In
addition,
discuss
applicability
accessibility
implementations
through
benchmark
datasets,
application
scenarios,
downstream
NLP
tasks.
International Journal For Multidisciplinary Research,
Год журнала:
2024,
Номер
6(3)
Опубликована: Май 14, 2024
Artificial
Intelligence
(AI)
is
revolutionizing
the
healthcare
sector
by
offering
innovative
solutions
to
various
challenges.
This
review
explores
applications
and
benefits
of
AI
in
including
techniques,
machine
learning,
natural
language
processing,
computer
vision,
which
are
being
utilized
enhance
medical
diagnostics,
treatment
planning,
patient
care,
administrative
processes.
One
significant
application
imaging
analysis.
Machine
learning
algorithms
can
analyze
images
such
as
X-rays,
MRIs,
CT
scans
with
high
accuracy,
aiding
early
detection
diagnosis
diseases
like
cancer
neurological
disorders.
Additionally,
AI-powered
predictive
analytics
enable
providers
forecast
outcomes
identify
individuals
at
risk
developing
certain
conditions,
allowing
for
proactive
intervention
personalized
plans.
Furthermore,
AI-driven
virtual
health
assistants
chabot’s
provide
patients
instant
access
information,
advice,
support,
improving
accessibility
engagement.
Natural
processing
these
systems
understand
respond
patients'
queries
concerns
effectively.
In
clinical
decision
support
systems,
vast
amounts
data,
records,
genetic
real-time
physiological
assist
professionals
making
informed
decisions
about
strategies.
Moreover,
robotic
surgery
surgical
precision,
reduce
errors,
shorten
recovery
times.
Despite
numerous
benefits,
challenges
data
privacy
concerns,
regulatory
compliance,
need
interdisciplinary
collaboration
remain.
However,
ongoing
advancements
technology
increased
adoption
organizations,
potential
transform
delivery,
improve
outcomes,
costs
substantial.
Collaborative
efforts
between
developers,
providers,
policymakers,
regulators
essential
harnessing
full
while
ensuring
ethical
responsible
use.
Transforming Government People Process and Policy,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 27, 2024
Purpose
This
study
aims
to
explore
how
artificial
intelligence
(AI)
can
be
used
overcome
the
challenges
associated
with
implementing
electronic
health
record
(EHR)
systems
in
primary
health-care
facilities
Tanzania.
It
assess
technological,
organisational
and
environmental
barriers
EHR
system
implementation
investigate
role
of
AI
optimising
these
for
more
effective
delivery.
Design/methodology/approach
The
adopts
a
qualitative
approach,
using
case
studies
from
five
regions
Tanzania:
Dar
es
Salaam,
Mwanza,
Morogoro,
Singida
Pwani.
Data
were
collected
through
26
semi-structured
interviews
providers,
including
medical
doctors,
nurses,
pharmacists
IT
personnel.
applied
diffusion
innovation
(DOI)
theory
technology-organisation-environment
framework
factors
affecting
potential
integration
enhance
systems.
Findings
Key
include
unreliable
network
connectivity,
frequent
power
outages,
insufficient
training
complex
usability
issues.
Despite
challenges,
have
improved
patient
data
accessibility
workflow
efficiency.
presents
opportunities
address
mainly
predictive
analytics,
AI-driven
encryption
security
personalised
modules.
reliability,
security,
ultimately
improving
outcomes.
Originality/value
provides
valuable
insights
into
integrating
optimise
resource-constrained
environments
like
addresses
gap
literature
by
focusing
on
adapted
low-resource
settings
future
implementations
similar
contexts.
findings
contribute
global
discourse
informatics
developing
countries.
