Generative artificial intelligence in oncology
Conner Ganjavi,
No information about this author
Sam Melamed,
No information about this author
Brett Biedermann
No information about this author
et al.
Current Opinion in Urology,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 3, 2025
Purpose
of
review
By
leveraging
models
such
as
large
language
(LLMs)
and
generative
computer
vision
tools,
artificial
intelligence
(GAI)
is
reshaping
cancer
research
oncologic
practice
from
diagnosis
to
treatment
follow-up.
This
timely
provides
a
comprehensive
overview
the
current
applications
future
potential
GAI
in
oncology,
including
urologic
malignancies.
Recent
findings
has
demonstrated
significant
improving
by
integrating
multimodal
data,
diagnostic
workflows,
assisting
imaging
interpretation.
In
treatment,
shows
promise
aligning
clinical
decisions
with
guidelines,
optimizing
systemic
therapy
choices,
aiding
patient
education.
Posttreatment,
include
streamlining
administrative
tasks,
follow-up
care,
monitoring
adverse
events.
image
analysis,
data
extraction,
outcomes
research.
Future
developments
could
stimulate
discovery,
improve
efficiency,
enhance
patient-physician
relationship.
Summary
Integration
into
oncology
shown
some
ability
accuracy,
optimize
decisions,
ultimately
strengthening
Despite
these
advancements,
inherent
stochasticity
GAI's
performance
necessitates
human
oversight,
more
specialized
models,
proper
physician
training,
robust
guidelines
ensure
its
well
tolerated
effective
integration
practice.
Language: Английский
Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation
Jihwan Song,
No information about this author
Sungmin Yoon
No information about this author
Energy and Buildings,
Journal Year:
2024,
Volume and Issue:
325, P. 114983 - 114983
Published: Nov. 2, 2024
Language: Английский
Generative AI Models (2018–2024): Advancements and Applications in Kidney Care
Fnu Neha,
No information about this author
Deepshikha Bhati,
No information about this author
Deepak Kumar Shukla
No information about this author
et al.
BioMedInformatics,
Journal Year:
2025,
Volume and Issue:
5(2), P. 18 - 18
Published: April 3, 2025
Kidney
disease
poses
a
significant
global
health
challenge,
affecting
millions
and
straining
healthcare
systems
due
to
limited
nephrology
resources.
This
paper
examines
the
transformative
potential
of
Generative
AI
(GenAI),
Large
Language
Models
(LLMs),
Vision
(LVMs)
in
addressing
critical
challenges
kidney
care.
GenAI
supports
research
early
interventions
through
generation
synthetic
medical
data.
LLMs
enhance
clinical
decision-making
by
analyzing
texts
electronic
records,
while
LVMs
improve
diagnostic
accuracy
advanced
image
analysis.
Together,
these
technologies
show
promise
for
advancing
patient
education,
risk
stratification,
diagnosis,
personalized
treatment
strategies.
highlights
key
advancements
GenAI,
LLMs,
from
2018
2024,
focusing
on
their
applications
care
presenting
common
use
cases.
It
also
discusses
limitations,
including
knowledge
cutoffs,
hallucinations,
contextual
understanding
challenges,
data
representation
biases,
computational
demands,
ethical
concerns.
By
providing
comprehensive
analysis,
this
outlines
roadmap
integrating
into
nephrology,
emphasizing
need
further
real-world
validation
fully
realize
potential.
Language: Английский
Large language models: Tools for new environmental decision-making
Journal of Environmental Management,
Journal Year:
2025,
Volume and Issue:
375, P. 124373 - 124373
Published: Feb. 1, 2025
Language: Английский
Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes
Jessica Hsueh,
No information about this author
Daniel Nethala,
No information about this author
Shiva M. Singh
No information about this author
et al.
Urology Practice,
Journal Year:
2024,
Volume and Issue:
11(5), P. 782 - 789
Published: Sept. 1, 2024
GPT-4
is
a
large
language
model
with
potential
for
multiple
applications
in
urology.
Our
study
sought
to
evaluate
GPT-4's
performance
data
extraction
from
renal
surgery
operative
notes.
Language: Английский