Leveraging Large Language Models for Enhancing Safety in Maritime Operations
Applied Sciences,
Год журнала:
2025,
Номер
15(3), С. 1666 - 1666
Опубликована: Фев. 6, 2025
Maritime
operations
play
a
critical
role
in
global
trade
but
face
persistent
safety
challenges
due
to
human
error,
environmental
factors,
and
operational
complexities.
This
review
explores
the
transformative
potential
of
Large
Language
Models
(LLMs)
enhancing
maritime
through
improved
communication,
decision-making,
compliance.
Specific
applications
include
multilingual
communication
for
international
crews,
automated
reporting,
interactive
training,
real-time
risk
assessment.
While
LLMs
offer
innovative
solutions,
such
as
data
privacy,
integration,
ethical
considerations
must
be
addressed.
concludes
with
actionable
recommendations
insights
leveraging
build
safer
more
resilient
systems.
Язык: Английский
ChatGPT-4o and 4o1 Preview as Dietary Support Tools in a Real-World Medicated Obesity Program: A Prospective Comparative Analysis
Healthcare,
Год журнала:
2025,
Номер
13(6), С. 647 - 647
Опубликована: Март 16, 2025
Background/Objectives:
Clinicians
are
becoming
increasingly
interested
in
the
use
of
large
language
models
(LLMs)
obesity
services.
While
most
experts
agree
that
LLM
integration
would
increase
access
to
care
and
its
efficiency,
many
remain
skeptical
their
scientific
accuracy
capacity
convey
human
empathy.
Recent
studies
have
shown
ChatGPT-3
capable
emulating
dietitian
responses
a
range
basic
dietary
questions.
Methods:
This
study
compared
two
ChatGPT-4o
those
from
dietitians
across
10
complex
questions
(5
broad;
5
narrow)
derived
patient–clinician
interactions
within
real-world
medicated
digital
weight
loss
service.
Results:
Investigators
found
neither
nor
Chat
GPT-4o1
preview
were
statistically
outperformed
(p
<
0.05)
by
on
any
study’s
The
same
finding
was
made
when
scores
aggregated
ten
following
four
individual
criteria:
correctness,
comprehensibility,
empathy/relatability,
actionability.
Conclusions:
These
results
provide
preliminary
evidence
advanced
LLMs
may
be
able
play
significant
supporting
role
Research
other
contexts
is
needed
before
stronger
conclusions
about
lifestyle
coaching
whether
such
initiatives
access.
Язык: Английский
Factors associated with abusive head trauma in young children presenting to emergency medical services using a large language model
Prehospital Emergency Care,
Год журнала:
2025,
Номер
unknown, С. 1 - 16
Опубликована: Янв. 13, 2025
Abusive
head
trauma
(AHT)
is
a
leading
cause
of
death
in
young
children.
Analyses
patient
characteristics
presenting
to
Emergency
Medical
Services
(EMS)
are
often
limited
structured
data
fields.
Artificial
Intelligence
(AI)
and
Large
Language
Models
(LLM)
may
identify
rare
presentations
like
AHT
through
factors
not
found
data.
Our
goal
was
apply
AI
LLM
EMS
narrative
documentation
children
detect
AHT.
This
retrospective
cohort
study
transports
<36
months
age
with
diagnosis
injury
from
the
2018-2019
ESO
Research
Data
Collaborative.
Non-abusive
closed
(NA-CHI)
distinguished
child
maltreatment
(AHT-CAN)
2
expert
reviewers;
kappa
statistic
(k)
assessed
inter-rater
reliability.
A
Natural
Processing
(NLP)
framework
using
an
augmented
derived
n-grams
developed
AHT-CAN.
We
compared
test
(sensitivity,
specificity,
negative
predictive
value
(NPV))
between
this
NLP
Generative
Pretrained
Transformer
(GPT)
or
only
models
Association
specific
word
tokens
AHT-CAN
analyzed
Pearson's
chi-square.
Area
Under
Receiver
Operator
Curve
(AUROC)
Precision-Recall
(AUPRC)
also
reported.
There
were
1082
encounters
our
cohort;
1030
(95.2%)
NA-CHI
52
(4.8%)
Inter-rater
agreement
substantial
(k=
0.71).
The
had
specificity
sensitivity
72.4%
92.3%,
respectively
NPV
99.5%.
In
comparison,
GPT
model
69.2%,
97.1%
98.4%
alone
53.8%,
62.0%,
96.4%.
AUROC
0.91
AUPRC
0.52.
total
44
bi-grams
positively
associated
including
"domestic",
"various",
"bruise",
"cheek",
"multiple",
"doa",
"not
respond",
"see
EMS".
LLMs
have
high
free-text
narratives.
Words
physical
signs
strongly
list
help
that
aid
detection
Язык: Английский
Extraction of Crohn’s Disease Clinical Phenotypes from Clinical Text Using Natural Language Processing
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Окт. 16, 2023
Abstract
Real-world
studies
based
on
electronic
health
records
often
require
manual
chart
review
to
derive
patients’
clinical
phenotypes,
a
labor-intensive
task
with
limited
scalability.
Here,
we
developed
and
compared
computable
phenotyping
rules
using
the
spaCy
frame-work
Large
Language
Model
(LLM),
GPT-4,
for
disease
behavior
age
at
diagnosis
of
Crohn’s
patients.
We
are
first
describe
algorithms
texts
these
complex
tasks
previously
described
inter-annotator
agreements
between
0.54
0.98.
The
data
comprised
notes
radiology
reports
from
584
Mount
Sinai
Health
System
Overall,
observed
similar
or
better
performance
GPT-4
rules.
On
note-level,
F1
score
was
least
0.90
0.82
diagnosis.
could
not
find
statistical
evidence
difference
human
experts
this
task.
Our
findings
underline
potential
LLMs
phenotyping.
Graphical
Язык: Английский