Perception,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 5, 2024
ChatGPT's
large
language
model,
GPT-4V,
has
been
trained
on
vast
numbers
of
image-text
pairs
and
is
therefore
capable
processing
visual
input.
This
model
operates
very
differently
from
current
state-of-the-art
neural
networks
designed
specifically
for
face
perception
so
I
chose
to
investigate
whether
ChatGPT
could
also
be
applied
this
domain.
With
aim,
focussed
the
task
matching,
that
is,
deciding
two
photographs
showed
same
person
or
not.
Across
six
different
tests,
demonstrated
performance
was
comparable
with
human
accuracies
despite
being
a
domain-general
‘virtual
assistant’
rather
than
specialised
tool
processing.
perhaps
surprising
result
identifies
new
avenue
exploration
in
field,
while
further
research
should
explore
boundaries
ability,
along
how
its
errors
may
relate
those
made
by
humans.
JMIR Mental Health,
Journal Year:
2024,
Volume and Issue:
11, P. e53043 - e53043
Published: March 18, 2024
Abstract
Background
The
current
paradigm
in
mental
health
care
focuses
on
clinical
recovery
and
symptom
remission.
This
model’s
efficacy
is
influenced
by
therapist
trust
patient
potential
the
depth
of
therapeutic
relationship.
Schizophrenia
a
chronic
illness
with
severe
symptoms
where
possibility
matter
debate.
As
artificial
intelligence
(AI)
becomes
integrated
into
field,
it
important
to
examine
its
ability
assess
major
psychiatric
disorders
such
as
schizophrenia.
Objective
study
aimed
evaluate
large
language
models
(LLMs)
comparison
professionals
prognosis
schizophrenia
without
professional
treatment
long-term
positive
negative
outcomes.
Methods
Vignettes
were
inputted
LLMs
interfaces
assessed
10
times
4
AI
platforms:
ChatGPT-3.5,
ChatGPT-4,
Google
Bard,
Claude.
A
total
80
evaluations
collected
benchmarked
against
existing
norms
analyze
what
(general
practitioners,
psychiatrists,
psychologists,
nurses)
general
public
think
about
outcomes
interventions.
Results
For
treatment,
ChatGPT-3.5
was
notably
pessimistic,
whereas
Claude,
Bard
aligned
views
but
differed
from
public.
All
believed
untreated
would
remain
static
or
worsen
treatment.
outcomes,
ChatGPT-4
Claude
predicted
more
than
ChatGPT-3.5.
pessimistic
ChatGPT-4.
Conclusions
finding
that
3
out
closely
predictions
when
considering
“with
treatment”
condition
demonstration
this
technology
providing
prognosis.
assessment
disturbing
since
may
reduce
motivation
patients
start
persist
for
Overall,
although
hold
promise
augmenting
care,
their
application
necessitates
rigorous
validation
harmonious
blend
human
expertise.
JMIR Mental Health,
Journal Year:
2024,
Volume and Issue:
11, P. e54781 - e54781
Published: April 18, 2024
This
paper
explores
a
significant
shift
in
the
field
of
mental
health
general
and
psychotherapy
particular
following
generative
artificial
intelligence's
new
capabilities
processing
generating
humanlike
language.
Following
Freud,
this
lingo-technological
development
is
conceptualized
as
"fourth
narcissistic
blow"
that
science
inflicts
on
humanity.
We
argue
blow
has
potentially
dramatic
influence
perceptions
human
society,
interrelationships,
self.
should,
accordingly,
expect
changes
therapeutic
act
emergence
what
we
term
third
psychotherapy.
The
introduction
an
marks
critical
juncture,
prompting
us
to
ask
important
core
questions
address
two
basic
elements
thinking,
namely,
transparency
autonomy:
(1)
What
presence
therapy
relationships?
(2)
How
does
it
reshape
our
perception
ourselves
interpersonal
dynamics?
(3)
remains
irreplaceable
at
therapy?
Given
ethical
implications
arise
from
these
questions,
proposes
can
be
valuable
asset
when
applied
with
insight
consideration,
enhancing
but
not
replacing
touch
therapy.
Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (online),
Journal Year:
2024,
Volume and Issue:
3(2), P. 11 - 20
Published: Feb. 27, 2024
The
advent
of
generative
artificial
intelligence
(AI)
technologies
heralds
a
new
era
in
industrial
innovation,
offering
unprecedented
capabilities
for
content
creation,
predictive
analytics,
and
automation.
This
paper
delves
into
the
transformative
potential
AI
across
key
sectors,
emphasizing
its
role
catalyzing
technological
advancements,
enhancing
operational
efficiencies,
fostering
sustainable
practices.
By
exploring
technical
characteristics,
developmental
trajectory,
application
scenarios
AI,
alongside
critical
examination
limitations
ethical
considerations,
this
study
aims
to
provide
comprehensive
understanding
how
is
reshaping
landscape
automotive,
manufacturing,
energy
industries.
JMIR Mental Health,
Journal Year:
2024,
Volume and Issue:
11, P. e55988 - e55988
Published: March 8, 2024
Large
language
models
(LLMs)
hold
potential
for
mental
health
applications.
However,
their
opaque
alignment
processes
may
embed
biases
that
shape
problematic
perspectives.
Evaluating
the
values
embedded
within
LLMs
guide
decision-making
have
ethical
importance.
Schwartz's
theory
of
basic
(STBV)
provides
a
framework
quantifying
cultural
value
orientations
and
has
shown
utility
examining
in
contexts,
including
cultural,
diagnostic,
therapist-client
dynamics.
UNSTRUCTURED
Knowledge
has
become
more
open
and
accessible
to
a
large
audience
with
the
"democratization
of
information"
facilitated
by
technology.
This
paper
provides
an
ethical
perspective
on
utilizing
Generative
Artificial
Intelligence
(GenAI)
for
democratization
mental
health
knowledge
practice.
It
explores
historical
context
democratizing
information,
transitioning
from
restricted
access
widespread
availability
due
internet,
open-source
movements,
most
recently,
GenAI
technologies
such
as
Large
Language
Models
(LLMs).
The
highlights
why
represent
new
phase
in
movement,
offering
unparalleled
highly
advanced
technology
well
information.
In
realm
health,
this
requires
delicate
nuanced
deliberation.
Including
may
allow,
among
other
things,
improved
accessibility
care,
personalized
responses,
conceptual
flexibility,
could
facilitate
flattening
traditional
hierarchies
between
care
providers
patients.
At
same
time,
it
also
entails
significant
risks
challenges
that
must
be
carefully
addressed.
To
navigate
these
complexities,
proposes
strategic
questionnaire
assessing
AI
based
applications.
tool
evaluates
both
benefits
risks,
emphasizing
need
balanced
approach
integration
health.
calls
cautious
yet
positive
advocating
active
engagement
professionals
guiding
development.
emphasizes
importance
ensuring
advancements
are
not
only
technologically
sound
but
ethically
grounded
patient
centered.
JMIR Mental Health,
Journal Year:
2024,
Volume and Issue:
11, P. e58011 - e58011
Published: July 24, 2024
Knowledge
has
become
more
open
and
accessible
to
a
large
audience
with
the
"democratization
of
information"
facilitated
by
technology.
This
paper
provides
sociohistorical
perspective
for
theme
issue
"Responsible
Design,
Integration,
Use
Generative
AI
in
Mental
Health."
It
evaluates
ethical
considerations
using
generative
artificial
intelligence
(GenAI)
democratization
mental
health
knowledge
practice.
explores
historical
context
democratizing
information,
transitioning
from
restricted
access
widespread
availability
due
internet,
open-source
movements,
most
recently,
GenAI
technologies
such
as
language
models.
The
highlights
why
represent
new
phase
movement,
offering
unparalleled
highly
advanced
technology
well
information.
In
realm
health,
this
requires
delicate
nuanced
deliberation.
Including
may
allow,
among
other
things,
improved
accessibility
care,
personalized
responses,
conceptual
flexibility,
could
facilitate
flattening
traditional
hierarchies
between
care
providers
patients.
At
same
time,
it
also
entails
significant
risks
challenges
that
must
be
carefully
addressed.
To
navigate
these
complexities,
proposes
strategic
questionnaire
assessing
intelligence-based
applications.
tool
both
benefits
risks,
emphasizing
need
balanced
approach
integration
health.
calls
cautious
yet
positive
advocating
active
engagement
professionals
guiding
development.
emphasizes
importance
ensuring
advancements
are
not
only
technologically
sound
but
ethically
grounded
patient-centered.
JMIR Mental Health,
Journal Year:
2025,
Volume and Issue:
12, P. e70439 - e70439
Published: Jan. 6, 2025
Abstract
Generative
artificial
intelligence
(GenAI)
shows
potential
for
personalized
care,
psychoeducation,
and
even
crisis
prediction
in
mental
health,
yet
responsible
use
requires
ethical
consideration
deliberation
perhaps
governance.
This
is
the
first
published
theme
issue
focused
on
GenAI
health.
It
brings
together
evidence
insights
GenAI’s
capabilities,
such
as
emotion
recognition,
therapy-session
summarization,
risk
assessment,
while
highlighting
sensitive
nature
of
health
data
need
rigorous
validation.
Contributors
discuss
how
bias,
alignment
with
human
values,
transparency,
empathy
must
be
carefully
addressed
to
ensure
ethically
grounded,
intelligence–assisted
care.
By
proposing
conceptual
frameworks;
best
practices;
regulatory
approaches,
including
ethics
care
preservation
socially
important
humanistic
elements,
this
underscores
that
can
complement,
rather
than
replace,
vital
role
clinical
settings.
To
achieve
this,
an
ongoing
collaboration
between
researchers,
clinicians,
policy
makers,
technologists
essential.
Applied Cognitive Psychology,
Journal Year:
2025,
Volume and Issue:
39(2)
Published: Feb. 25, 2025
ABSTRACT
Unfamiliar
face
matching
involves
deciding
whether
two
images
depict
the
same
person
or
different
people.
Individual
performance
can
be
error‐prone
but
is
improved
by
aggregating
(fusing)
responses
of
participant
pairs.
With
advances
in
automated
facial
recognition
systems
(AFR),
fusing
human
and
algorithm
also
leads
to
improvements
over
individuals
working
alone.
In
current
work,
I
investigated
ChatGPT
could
serve
as
this
fusion.
Using
a
common
test,
found
that
fusion
individual
with
those
provided
increased
comparison
both
alone
simulated
This
pattern
results
was
evident
when
participants
responded
either
using
rating
scale
(Experiment
1)
binary
decision
associated
confidence
2).
Taken
together,
these
findings
demonstrate
potential
utility
daily
identification
contexts
where
state‐of‐the‐art
AFR
may
not
available.
International Journal for Equity in Health,
Journal Year:
2025,
Volume and Issue:
24(1)
Published: Feb. 26, 2025
Abstract
Background
Large
language
models
are
increasingly
evaluated
for
use
in
healthcare.
However,
concerns
about
their
impact
on
disparities
persist.
This
study
reviews
current
research
demographic
biases
large
to
identify
prevalent
bias
types,
assess
measurement
methods,
and
evaluate
mitigation
strategies.
Methods
We
conducted
a
systematic
review,
searching
publications
from
January
2018
July
2024
across
five
databases.
included
peer-reviewed
studies
evaluating
models,
focusing
gender,
race,
ethnicity,
age,
other
factors.
Study
quality
was
assessed
using
the
Joanna
Briggs
Institute
Critical
Appraisal
Tools.
Results
Our
review
24
studies.
Of
these,
22
(91.7%)
identified
biases.
Gender
most
prevalent,
reported
15
of
16
(93.7%).
Racial
or
ethnic
were
observed
10
11
(90.9%).
Only
two
found
minimal
no
certain
contexts.
Mitigation
strategies
mainly
prompt
engineering,
with
varying
effectiveness.
these
findings
tempered
by
potential
publication
bias,
as
negative
results
less
frequently
published.
Conclusion
Biases
various
medical
domains.
While
detection
is
improving,
effective
still
developing.
As
LLMs
influence
critical
decisions,
addressing
resultant
essential
ensuring
fair
artificial
intelligence
systems.
Future
should
focus
wider
range
factors,
intersectional
analyses,
non-Western
cultural
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