Transcultural Psychiatry,
Год журнала:
2022,
Номер
59(4), С. 551 - 567
Опубликована: Авг. 1, 2022
In
this
invited
commentary
on
the
thematic
issue
of
Transcultural
Psychiatry
idioms
distress,
concern,
and
care,
I
provide
a
brief
overview
how
my
research
agenda
evolved
over
years
while
conducting
community
clinic-based
in
South
Southeast
Asia
as
well
North
America.
then
suggest
areas
where
future
resilience
will
be
needed
among
different
demographics
given
social
change
shifts
we
communicate
face
to
virtual
reality,
impact
medicalization,
pharmaceuticalization
bracket
creep,
changes
indigenous
healing
systems,
hybridization.
further
call
attention
importance
guided
occupational
settings.
Toward
end
highlight
moral
distress
health
care
workers
U.S.
have
experienced
during
Covid-19
pandemic
point
out
differentiating
individual
burnout
from
injury
related
structural
distress.
conclude
by
discussing
general
utility
an
perspective
practice
cultural
psychiatry
that
needs
included
training
all
practitioners
regardless
system
medicine
they
practice.
Doing
so
may
enable
formation
mental
communities
contexts
there
are
pluralistic
arenas.
Bulletin of the World Health Organization,
Год журнала:
2022,
Номер
100(9), С. 544 - 561
Опубликована: Сен. 1, 2022
To
compare
and
summarize
the
literature
regarding
infodemics
health
misinformation,
to
identify
challenges
opportunities
for
addressing
issues
of
infodemics.
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(4)
Опубликована: Март 15, 2024
Abstract
Advancements
in
artificial
intelligence
(AI)
have
driven
extensive
research
into
developing
diverse
multimodal
data
analysis
approaches
for
smart
healthcare.
There
is
a
scarcity
of
large-scale
literature
this
field
based
on
quantitative
approaches.
This
study
performed
bibliometric
and
topic
modeling
examination
683
articles
from
2002
to
2022,
focusing
topics
trends,
journals,
countries/regions,
institutions,
authors,
scientific
collaborations.
Results
showed
that,
firstly,
the
number
has
grown
1
220
with
majority
being
published
interdisciplinary
journals
that
link
healthcare
medical
information
technology
AI.
Secondly,
significant
rise
quantity
can
be
attributed
increasing
contribution
scholars
non-English
speaking
countries/regions
noteworthy
contributions
made
by
authors
USA
India.
Thirdly,
researchers
show
high
interest
issues,
especially,
cross-modality
magnetic
resonance
imaging
(MRI)
brain
tumor
analysis,
cancer
prognosis
through
multi-dimensional
AI-assisted
diagnostics
personalization
healthcare,
each
experiencing
increase
interest.
an
emerging
trend
towards
issues
such
as
applying
generative
adversarial
networks
contrastive
learning
image
fusion
synthesis
utilizing
combined
spatiotemporal
resolution
functional
MRI
electroencephalogram
data-centric
manner.
valuable
enhancing
researchers’
practitioners’
understanding
present
focal
points
upcoming
trajectories
AI-powered
analysis.
Machine Learning with Applications,
Год журнала:
2023,
Номер
14, С. 100492 - 100492
Опубликована: Авг. 22, 2023
Accurate
and
timely
detection
classification
of
lung
abnormalities
are
crucial
for
effective
diagnosis
treatment
planning.
In
recent
years,
Deep
Learning
(DL)
techniques
have
shown
remarkable
performance
in
medical
image
analysis.
This
paper
presents
a
novel
promising
approach,
namely
DCNN-GRU,
improving
the
abnormalities.
Our
proposed
model
combines
capabilities
Convolutional
Neural
Network
(DCNN)
with
Gated
Recurrent
Unit
(GRU)
while
incorporating
Explainable
AI
techniques.
Specifically,
DCNN-GRU
leverages
power
CNNs
to
automatically
extract
meaningful
features
from
images,
capturing
both
local
global
patterns.
The
extracted
fed
into
GRU,
which
effectively
models
temporal
dependencies
captures
sequential
information
inherent
images.
integration
allows
understand
complex
accurately.
Additionally,
we
emphasize
Artificial
Intelligence
(XAI)
like
LIME,
SHAP,
Grad-CAM
enhance
interpretability
transparency
our
model.
To
evaluate
conducted
experiments
on
COVID-19
Lung
cancer
using
two
different
datasets.
achieved
accuracy
99.30%
98.97%
COVID-19,
cancer,
respectively.
Furthermore,
significantly
reduces
training
time
compared
existing
approaches.
results
demonstrate
that
outperforms
approaches,
achieving
high
rate
tasks.
XAI
provides
valuable
insights
model's
decision-making
process,
aiding
clinicians
understanding
validating
predictions.
Misinformation
has
been
existed
for
centuries,
though
emerge
as
a
severe
concern
in
the
age
of
social
media,
and
particularly
during
COVID-19
global
pandemic.
As
pandemic
approached,
massive
influx
mixed
quality
data
appeared
on
which
had
adverse
effects
society.
This
study
highlights
possible
factors
contributing
to
sharing
spreading
misinformation
through
media
crisis.
Preferred
Reporting
Items
Meta-Analysis
guidelines
were
used
systematic
review.
Anxiety
or
risk
perception
associated
with
was
one
significant
motivators
sharing,
followed
by
entertainment,
information
seeking,
sociability,
tie
strength,
self-promotion,
trust
science,
self-efficacy,
altruism.
WhatsApp
Facebook
most
platforms
rumors
misinformation.
The
results
indicated
five
including
socio-demographic
characteristics,
financial
considerations,
political
affiliation
interest,
conspiracy
ideation,
religious
factors.
could
have
profound
consequences
individual
society
impeding
efforts
government
health
institutions
manage
SLR
focuses
solely
quantitative
studies,
hence,
studies
are
overlooked
from
qualitative
standpoint.
Furthermore,
this
only
looked
at
predictors
behavior
COVID-19.
It
did
not
look
into
that
curb
whole.
study's
findings
will
help
public,
general,
be
cautious
about
misinformation,
care
workers,
institutions,
particular,
devising
strategies
measures
reduce
flow
releasing
credible
concerned
official
accounts.
valuable
professionals
agencies
devise
handling
public
emergencies.
