Journal of Medical Internet Research,
Год журнала:
2024,
Номер
26, С. e56780 - e56780
Опубликована: Май 31, 2024
Large
language
models
(LLMs)
such
as
ChatGPT
have
become
widely
applied
in
the
field
of
medical
research.
In
process
conducting
systematic
reviews,
similar
tools
can
be
used
to
expedite
various
steps,
including
defining
clinical
questions,
performing
literature
search,
document
screening,
information
extraction,
and
refinement,
thereby
conserving
resources
enhancing
efficiency.
However,
when
using
LLMs,
attention
should
paid
transparent
reporting,
distinguishing
between
genuine
false
content,
avoiding
academic
misconduct.
this
viewpoint,
we
highlight
potential
roles
LLMs
creation
reviews
meta-analyses,
elucidating
their
advantages,
limitations,
future
research
directions,
aiming
provide
insights
guidance
for
authors
planning
meta-analyses.
Medicine Plus,
Год журнала:
2024,
Номер
1(2), С. 100030 - 100030
Опубликована: Май 17, 2024
With
the
rapid
development
of
artificial
intelligence,
large
language
models
(LLMs)
have
shown
promising
capabilities
in
mimicking
human-level
comprehension
and
reasoning.
This
has
sparked
significant
interest
applying
LLMs
to
enhance
various
aspects
healthcare,
ranging
from
medical
education
clinical
decision
support.
However,
medicine
involves
multifaceted
data
modalities
nuanced
reasoning
skills,
presenting
challenges
for
integrating
LLMs.
review
introduces
fundamental
applications
general-purpose
specialized
LLMs,
demonstrating
their
utilities
knowledge
retrieval,
research
support,
workflow
automation,
diagnostic
assistance.
Recognizing
inherent
multimodality
medicine,
emphasizes
multimodal
discusses
ability
process
diverse
types
like
imaging
electronic
health
records
augment
accuracy.
To
address
LLMs'
limitations
regarding
personalization
complex
reasoning,
further
explores
emerging
LLM-powered
autonomous
agents
healthcare.
Moreover,
it
summarizes
evaluation
methodologies
assessing
reliability
safety
contexts.
transformative
potential
medicine;
however,
there
is
a
pivotal
need
continuous
optimizations
ethical
oversight
before
these
can
be
effectively
integrated
into
practice.
ACM Computing Surveys,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 13, 2025
As
the
applications
of
large
language
models
(LLMs)
expand
across
diverse
fields,
their
ability
to
adapt
ongoing
changes
in
data,
tasks,
and
user
preferences
becomes
crucial.
Traditional
training
methods
with
static
datasets
are
inadequate
for
coping
dynamic
nature
real-world
information.
Lifelong
learning,
or
continual
addresses
this
by
enabling
LLMs
learn
continuously
over
operational
lifetime,
integrating
new
knowledge
while
retaining
previously
learned
information
preventing
catastrophic
forgetting.
Our
survey
explores
landscape
lifelong
categorizing
strategies
into
two
groups
based
on
how
is
integrated:
Internal
Knowledge,
where
absorb
parameters
through
full
partial
training,
External
which
incorporates
as
external
resources
like
Wikipedia
APIs
without
updating
model
parameters.
The
key
contributions
our
include:
(1)
Introducing
a
novel
taxonomy
categorize
extensive
literature
learning
12
scenarios;
(2)
Identifying
common
techniques
all
scenarios
classifying
existing
various
technique
groups;
(3)
Highlighting
emerging
such
expansion
data
selection,
were
less
explored
pre-LLM
era.
Resources
available
at
https://github.com/qianlima-lab/awesome-lifelong-learning-methods-for-llm.
Patterns,
Год журнала:
2024,
Номер
5(8), С. 101028 - 101028
Опубликована: Авг. 1, 2024
The
digital
twin
(DT)
is
a
concept
widely
used
in
industry
to
create
replicas
of
physical
objects
or
systems.
dynamic,
bi-directional
link
between
the
entity
and
its
counterpart
enables
real-time
update
entity.
It
can
predict
perturbations
related
object's
function.
obvious
applications
DTs
healthcare
medicine
are
extremely
attractive
prospects
that
have
potential
revolutionize
patient
diagnosis
treatment.
However,
challenges
including
technical
obstacles,
biological
heterogeneity,
ethical
considerations
make
it
difficult
achieve
desired
goal.
Advances
multi-modal
deep
learning
methods,
embodied
AI
agents,
metaverse
may
mitigate
some
difficulties.
Here,
we
discuss
basic
concepts
underlying
DTs,
requirements
for
implementing
medicine,
their
current
uses.
We
also
provide
our
perspective
on
five
hallmarks
DT
system
advance
research
this
field.
Philosophy Compass,
Год журнала:
2024,
Номер
19(2)
Опубликована: Фев. 1, 2024
Abstract
Recent
progress
in
artificial
intelligence
(AI)
has
drawn
attention
to
the
technology's
transformative
potential,
including
what
some
see
as
its
prospects
for
causing
large‐scale
harm.
We
review
two
influential
arguments
purporting
show
how
AI
could
pose
catastrophic
risks.
The
first
argument
—
Problem
of
Power‐Seeking
claims
that,
under
certain
assumptions,
advanced
systems
are
likely
engage
dangerous
power‐seeking
behavior
pursuit
their
goals.
reasons
thinking
that
might
seek
power,
they
obtain
it,
this
lead
catastrophe,
and
we
build
deploy
such
anyway.
second
development
human‐level
will
unlock
rapid
further
progress,
culminating
far
more
capable
than
any
human
is
Singularity
Hypothesis
.
Power‐seeking
on
part
be
particularly
dangerous.
discuss
a
variety
objections
both
conclude
by
assessing
state
debate.
Computational Visual Media,
Год журнала:
2024,
Номер
10(3), С. 399 - 424
Опубликована: Май 1, 2024
Abstract
Recent
studies
have
indicated
that
foundation
models,
such
as
BERT
and
GPT,
excel
at
adapting
to
various
downstream
tasks.
This
adaptability
has
made
them
a
dominant
force
in
building
artificial
intelligence
(AI)
systems.
Moreover,
new
research
paradigm
emerged
visualization
techniques
are
incorporated
into
these
models.
study
divides
intersections
two
areas:
for
model
(VIS4FM)
(FM4VIS).
In
terms
of
VIS4FM,
we
explore
the
primary
role
visualizations
understanding,
refining,
evaluating
intricate
VIS4FM
addresses
pressing
need
transparency,
explainability,
fairness,
robustness.
Conversely,
FM4VIS,
highlight
how
models
can
be
used
advance
field
itself.
The
intersection
with
is
promising
but
also
introduces
set
challenges.
By
highlighting
challenges
opportunities,
this
aims
provide
starting
point
continued
exploration
avenue.
Frontiers in Robotics and AI,
Год журнала:
2024,
Номер
11
Опубликована: Май 27, 2024
Companion
robots
are
aimed
to
mitigate
loneliness
and
social
isolation
among
older
adults
by
providing
emotional
support
in
their
everyday
lives.
However,
adults’
expectations
of
conversational
companionship
might
substantially
differ
from
what
current
technologies
can
achieve,
as
well
other
age
groups
like
young
adults.
Thus,
it
is
crucial
involve
the
development
companion
ensure
that
these
devices
align
with
unique
experiences.
The
recent
advancement
foundation
models,
such
large
language
has
taken
a
significant
stride
toward
fulfilling
those
expectations,
contrast
prior
literature
relied
on
humans
controlling
(i.e.,
Wizard
Oz)
or
limited
rule-based
architectures
not
feasible
apply
daily
lives
Consequently,
we
conducted
participatory
design
(co-design)
study
28
adults,
demonstrating
robot
using
model
(LLM),
scenarios
represent
situations
life.
thematic
analysis
discussions
around
shows
expect
engage
conversation
actively
passively
settings,
remember
previous
conversations
personalize,
protect
privacy
provide
control
over
learned
data,
give
information
reminders,
foster
skills
connections,
express
empathy
emotions.
Based
findings,
this
article
provides
actionable
recommendations
for
designing
LLMs
vision-language
which
also
be
applied
domains.
