Artificial Intelligence in Primary Care Decision-Making: Survey of Healthcare Professionals in Saudi Arabia
Najlaa Mohammad Alsudairy,
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Alaa Omar Alahdal,
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Mohammed Alrashidi
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et al.
Cureus,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 9, 2025
Artificial
intelligence
(AI)
has
the
potential
to
revolutionize
healthcare,
particularly
in
primary
care,
by
improving
clinical
decision-making
and
patient
outcomes.
AI
technologies,
such
as
machine
learning
natural
language
processing,
can
assist
clinicians
diagnosing
conditions,
predicting
outcomes,
recommending
treatments,
identifying
at-risk
individuals.
Despite
its
potential,
adoption
care
is
slow
due
various
challenges,
including
resource
limitations,
clinician
training,
concerns
about
reliability
of
systems.
Understanding
healthcare
professionals'
perceptions
crucial
for
overcoming
these
barriers
promoting
integration
into
practice.
A
cross-sectional,
survey-based
study
was
conducted
assess
awareness,
usage,
perceptions,
Saudi
Arabia.
The
included
250
professionals
from
settings
across
urban,
rural,
hospital-based
clinics.
Data
were
collected
via
an
electronic
survey
that
both
quantitative
qualitative
questions,
analyzed
using
descriptive
inferential
statistics.
total
participated
survey.
majority
physicians
(44.8%),
with
remaining
participants
consisting
nurses
(27.2%),
medical
assistants
(15.6%),
administrators
(8.8%).
Awareness
tools
mixed,
14.8%
respondents
very
familiar
47.2%
unfamiliar.
Thirty-one
percent
reported
tools,
primarily
diagnostic
support
(59.5%).
Common
high
implementation
costs
(49.2%)
lack
training
(34%).
significant
portion
(48%)
expressed
undermining
human
touch
healthcare.
hindered
low
familiarity
well
several
barriers,
cost,
However,
there
optimism
AI's
decision-making.
Overcoming
through
targeted
education,
infrastructure
investment,
further
research
essential
realizing
benefits
Language: Английский
Case Report: The intersection of psychiatry and medicine: diagnostic and ethical insights from case studies
Frontiers in Psychiatry,
Journal Year:
2025,
Volume and Issue:
16
Published: April 22, 2025
The
intersection
of
psychiatry
and
medicine
presents
unique
diagnostic
ethical
challenges,
particularly
for
conditions
involving
significant
brain-body
interactions,
such
as
psychosomatic,
somatopsychic,
complex
systemic
disorders.
This
article
explores
the
historical
contemporary
issues
in
diagnosing
conditions,
emphasizing
fragmentation
medical
psychiatric
knowledge,
biases
clinical
guidelines,
mismanagement
illnesses.
Diagnostic
errors
often
arise
from
insufficient
integration
between
general
psychiatry,
compounded
by
reliance
on
population-based
guidelines
that
neglect
individual
patient
needs.
Misclassification
like
myalgic
encephalomyelitis/chronic
fatigue
syndrome
(ME/CFS),
Lyme
disease,
fibromyalgia
psychosomatic
or
psychogenic
has
led
to
stigmatization
delayed
care.
While
these
are
referenced
emblematic
examples
misclassified
poorly
understood
disorders,
five
cases
discussed
this
do
not
directly
illustrate
diseases.
Instead,
they
exemplify
shared
dilemmas
at
medicine-psychiatry
interface,
including
uncertainty,
fragmentation,
risk
epistemic
injustice.
critically
examines
terms
medically
unexplained
symptoms
functional
highlighting
their
limitations
potential
misuse.
Case
underscore
consequences
inaccuracies
urgent
need
improved
approaches.
Ethical
considerations
also
explored,
respecting
experiences,
promoting
individualized
care,
acknowledging
inherent
uncertainties
diagnosis.
Advances
technologies
brain
imaging
molecular
diagnostics
offer
hope
bridging
gap
medicine,
enabling
more
accurate
assessments
better
outcomes.
concludes
advocating
comprehensive
training
interface
a
patient-centered
approach
integrates
observation,
research
insights,
nuanced
understanding
mind-body
dynamics.
Language: Английский
Explainable Machine Learning in the Prediction of Depression
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(11), P. 1412 - 1412
Published: June 2, 2025
Background:
Depression
constitutes
a
major
public
health
issue,
being
one
of
the
leading
causes
burden
disease
worldwide.
The
risk
depression
is
determined
by
both
genetic
and
environmental
factors.
While
factors
cannot
be
altered,
identification
potentially
reversible
crucial
in
order
to
try
limit
prevalence
depression.
Aim:
A
cross-sectional,
questionnaire-based
study
on
sample
from
multicultural
region
Thrace
northeast
Greece
was
designed
assess
potential
association
with
several
sociodemographic
characteristics,
lifestyle,
status.
employed
four
machine
learning
(ML)
methods
depression:
logistic
regression
(LR),
support
vector
(SVM),
XGBoost,
neural
networks
(NNs).
These
models
were
compared
identify
best-performing
approach.
Additionally,
algorithm
(GA)
utilized
for
feature
selection
SHAP
(SHapley
Additive
exPlanations)
interpreting
contributions
each
feature.
Results:
XGBoost
classifier
demonstrated
highest
performance
test
dataset
predict
excellent
accuracy
(97.83%),
NNs
close
second
(accuracy,
97.02%).
15
most
significant
identified
GA
algorithm.
analysis
revealed
that
anxiety,
education
level,
alcohol
consumption,
body
mass
index
influential
predictors
Conclusions:
findings
provide
valuable
insights
development
personalized
interventions
clinical
strategies,
ultimately
promoting
improved
mental
well-being
individuals.
Future
research
should
expand
datasets
enhance
model
accuracy,
enabling
early
detection
healthcare
systems
better
intervention.
Language: Английский