Machine Learning for Combating Mental Health Stigma
Advances in psychology, mental health, and behavioral studies (APMHBS) book series,
Год журнала:
2025,
Номер
unknown, С. 333 - 366
Опубликована: Янв. 3, 2025
Stigma
around
mental
health
hinders
care
through
discrimination,
misconceptions,
and
shame.
Machine
learning
(ML)
offers
data-driven
solutions
to
address
this,
using
sentiment
analysis
NLP
analyze
public
attitudes,
identify
stigmatizing
language,
guide
awareness
campaigns.
In
healthcare,
ML
reduces
biases,
enhances
patient
interactions,
fosters
inclusivity.
Personalized
education
via
AI
combats
misinformation,
while
advocacy
campaigns
leverage
assess
impact.
By
addressing
ethical
concerns
like
bias
privacy,
can
transform
stigma
reduction.
Язык: Английский
Is artificial intelligence an opportunity or a threat in nursing care?: An in-depth phenomenological study
Archives of Psychiatric Nursing,
Год журнала:
2025,
Номер
54, С. 54 - 62
Опубликована: Янв. 21, 2025
Язык: Английский
AI to Analyze Individual Medical Records and Genetic Information to Recommend Personalised Treatment Plans
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 147 - 164
Опубликована: Март 7, 2025
The
integration
of
Artificial
Intelligence
(AI)
into
healthcare
has
open
a
new
era
medical
diagnosis
and
treatment.
This
chapter
critically
examines
collection
scholarly
articles
that
delve
the
multifaceted
applications
AI
in
domain
such
as
using
image
chemical
studies,
patient
familiar
history
big
data
acquired
through
use
wearables.
Also,
we
dive
ethical
legal
aspects
training
IA
with
private
which
repercussions
can
have.
Язык: Английский
An Academic Viewpoint (2025) on the Integration of Generative Artificial Intelligence in Medical Education: Transforming Learning and Practices
Cureus,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 25, 2025
Язык: Английский
Navigating and Addressing Public Concerns in AI: Insights From Social Media Analytics and Delphi
IEEE Access,
Год журнала:
2024,
Номер
12, С. 126043 - 126062
Опубликована: Янв. 1, 2024
The
rapid
advancement
and
integration
of
artificial
intelligence
(AI)
in
various
domains
society
have
given
rise
to
a
complex
landscape
public
concerns.
This
research
endeavors
systematically
explore
these
concerns
by
employing
multi-stage
methodology
that
combines
large-scale
social
media
data
collection
from
Twitter
advanced
text
analytics.
study
identifies
seven
distinct
clusters
concerns,
encompassing
privacy
security,
workforce
displacement,
existential
risks,
ethical
implications,
dependency
on
AI,
misuse
lack
transparency.
To
further
contextualize
findings,
the
Delphi
method
was
employed
gather
insights
AI
ethics
experts,
providing
deeper
understanding
public's
apprehensions.
results
underscore
critical
need
for
addressing
foster
trust
acceptance
technologies.
comprehensive
analysis
offers
valuable
guidance
policymakers,
developers,
stakeholders
navigate
mitigate
multifaceted
issues
associated
with
ultimately
contributing
more
informed
responsible
deployment.
By
aims
pave
way
ethically
sound
socially
acceptable
into
society,
ensuring
benefits
can
be
realized
while
minimizing
potential
risks
negative
impacts.
Through
this
systematic
approach,
highlights
importance
continuous
monitoring
proactive
management
AI-related
sustain
confidence
promote
beneficial
innovation.
Язык: Английский
An Artificial Intelligence-Based Virtual Human Avatar Application to Assess the Mental Health of Health Care Professionals: A Validation Study
Deleted Journal,
Год журнала:
2024,
Номер
1(1), С. 215 - 226
Опубликована: Март 1, 2024
Язык: Английский
Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews
Yingzhuo Ma,
Yi Zeng,
Tong Liu
и другие.
Frontiers in Public Health,
Год журнала:
2024,
Номер
12
Опубликована: Ноя. 4, 2024
Background
Progress
in
developing
artificial
intelligence
(AI)
products
represented
by
large
language
models
(LLMs)
such
as
OpenAI’s
ChatGPT
has
sparked
enthusiasm
for
their
potential
use
mental
health
practice.
However,
the
perspectives
on
integration
of
LLMs
within
practice
remain
an
underreported
topic.
Therefore,
this
study
aimed
to
explore
how
and
AI
experts
conceptualize
perceive
integrating
into
Method
In
February–April
2024,
online
semi-structured
interviews
were
conducted
with
21
(12
psychiatrists,
7
nurses,
2
researchers
medical
intelligence)
from
four
provinces
China,
using
snowballing
purposive
selection
sampling.
Respondents’
discussions
about
expectations
analyzed
conventional
content
analysis.
Results
Four
themes
eleven
sub-themes
emerged
study.
Firstly,
participants
discussed
(1)
application
reform
brought
(fair
access
services,
enhancement
patient
participation,
improvement
work
efficiency
quality),
then
(2)
technological-mental
gap
(misleading
information,
lack
professional
nuance
depth,
user
risk).
Based
these
points,
they
provided
a
range
(3)
prerequisites
(training
competence,
guidelines
management,
engagement
transparency)
expressed
(4)
future
developments
(reasonable
allocation
workload,
upgrades
revamps
LLMs).
Conclusion
These
findings
provide
valuable
insights
practice,
offering
critical
guidance
institutions
effectively
implement,
manage,
optimize
tools,
thereby
enhancing
quality
accessibility
services.
Язык: Английский
Synthetic Biology and AI
Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Год журнала:
2024,
Номер
unknown, С. 265 - 290
Опубликована: Дек. 17, 2024
Synthetic
biology
and
artificial
intelligence
are
ushering
in
a
new
era
of
healthcare.
In
the
specific
context
bioengineering,
organoids,
brain-computer
interfaces,
ethical
considerations
particularly
salient.
Challenges
such
as
data
inadequacy,
unintended
bias
can
undermine
reliability
fairness
decision
making.
Additionally,
cultural
barriers
concerns
related
to
nonmaleficence,
autonomy,
justice
must
be
carefully
considered.
To
fully
realize
benefits
this
technological
synergy,
multidisciplinary
approach
is
necessary,
involving
scientists,
engineers,
ethicists,
policymakers.
Transparent
accountable
AI
systems
essential
mitigate
biases,
protect
privacy,
avoid
consequences.
By
proactively
addressing
developing
robust
regulatory
frameworks,
we
harness
power
these
technologies
for
betterment
humanity.
Язык: Английский