Cancers,
Journal Year:
2024,
Volume and Issue:
16(9), P. 1686 - 1686
Published: April 26, 2024
(1)
Background:
Digital
pathology
(DP)
is
transforming
the
landscape
of
clinical
practice,
offering
a
revolutionary
approach
to
traditional
analysis
and
diagnosis.
(2)
Methods:
This
innovative
technology
involves
digitization
glass
slides
which
enables
pathologists
access,
analyze,
share
high-resolution
whole-slide
images
(WSI)
tissue
specimens
in
digital
format.
By
integrating
cutting-edge
imaging
with
advanced
software,
DP
promises
enhance
practice
numerous
ways.
not
only
improves
quality
assurance
standardization
but
also
allows
remote
collaboration
among
experts
for
more
accurate
Artificial
intelligence
(AI)
significantly
cancer
diagnosis,
classification,
prognosis
by
automating
various
tasks.
It
enhances
spatial
tumor
microenvironment
(TME)
discovery
new
biomarkers,
advancing
their
translation
therapeutic
applications.
(3)
Results:
The
AI-driven
immune
assays,
Immunoscore
(IS)
Immunoscore-Immune
Checkpoint
(IS-IC),
have
emerged
as
powerful
tools
improving
prognosis,
treatment
selection
assessing
contexture
patients.
IS
quantitative
assessment
performed
on
hematoxylin-eosin
(H&E)
CD3+/CD8+
stained
from
colon
patients
has
proven
be
reproducible,
concordant,
reliable
than
expert
pathologists'
evaluation
response.
Outperforming
staging
systems,
demonstrated
robust
potential
efficiency
ultimately
patient
care.
Certainly,
addressing
challenges
encountered
essential
ensure
its
successful
integration
into
guidelines
implementation
use.
(4)
Conclusion:
ongoing
progress
holds
revolutionize
practices,
emphasizing
need
incorporate
AI
technologies,
including
IS,
settings
personalized
therapy.
Frontiers in Oncology,
Journal Year:
2025,
Volume and Issue:
15
Published: Feb. 4, 2025
Background
Artificial
intelligence
(AI)
has
emerged
as
a
transformative
tool
in
oncology,
offering
promising
applications
chemotherapy
development,
cancer
diagnosis,
and
predicting
response.
Despite
its
potential,
debates
persist
regarding
the
predictive
accuracy
of
AI
technologies,
particularly
machine
learning
(ML)
deep
(DL).
Objective
This
review
aims
to
explore
role
forecasting
outcomes
related
treatment
response,
synthesizing
current
advancements
identifying
critical
gaps
field.
Methods
A
comprehensive
literature
search
was
conducted
across
PubMed,
Embase,
Web
Science,
Cochrane
databases
up
2023.
Keywords
included
“Artificial
Intelligence
(AI),”
“Machine
Learning
(ML),”
“Deep
(DL)”
combined
with
“chemotherapy
development,”
“cancer
diagnosis,”
treatment.”
Articles
published
within
last
four
years
written
English
were
included.
The
Prediction
Model
Risk
Bias
Assessment
utilized
assess
risk
bias
selected
studies.
Conclusion
underscores
substantial
impact
AI,
including
ML
DL,
on
innovation,
response
for
both
solid
hematological
tumors.
Evidence
from
recent
studies
highlights
AI’s
potential
reduce
cancer-related
mortality
by
optimizing
diagnostic
accuracy,
personalizing
plans,
improving
therapeutic
outcomes.
Future
research
should
focus
addressing
challenges
clinical
implementation,
ethical
considerations,
scalability
enhance
integration
into
oncology
care.
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 29, 2024
Artificial
intelligence
(AI)
is
rapidly
transforming
the
field
of
radiology,
offering
significant
advancements
in
diagnostic
accuracy,
workflow
efficiency,
and
patient
care.
This
article
explores
AI's
impact
on
various
subfields
emphasizing
its
potential
to
improve
clinical
practices
enhance
outcomes.
AI-driven
technologies
such
as
machine
learning,
deep
natural
language
processing
(NLP)
are
playing
a
pivotal
role
automating
routine
tasks,
aiding
early
disease
detection,
supporting
decision-making,
allowing
radiologists
focus
more
complex
challenges.
Key
applications
AI
radiology
include
improving
image
analysis
through
computer-aided
diagnosis
(CAD)
systems,
which
detection
abnormalities
imaging,
tumors.
tools
have
demonstrated
high
accuracy
analyzing
medical
images,
integrating
data
from
multiple
imaging
modalities
CT,
MRI,
PET
provide
comprehensive
insights.
These
facilitate
personalized
treatment
planning
complement
radiologists'
workflows.
However,
for
be
fully
integrated
into
workflows,
several
challenges
must
addressed,
including
ensuring
transparency
how
algorithms
work,
protecting
data,
avoiding
biases
that
could
affect
diverse
populations.
Developing
explainable
systems
can
clearly
show
decisions
made
crucial,
seamlessly
fit
existing
systems.
Collaboration
between
radiologists,
developers,
policymakers,
alongside
strong
ethical
guidelines
regulatory
oversight,
will
key
implemented
safely
effectively
practice.
Overall,
holds
tremendous
promise
revolutionizing
radiology.
Through
ability
automate
capabilities,
streamline
has
significantly
quality
efficiency
practices.
Continued
research,
development,
collaboration
crucial
unlocking
full
addressing
accompany
adoption.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(9)
Published: Aug. 8, 2024
Abstract
The
fusion
of
blockchain
and
artificial
intelligence
(AI)
marks
a
paradigm
shift
in
healthcare,
addressing
critical
challenges
securing
electronic
health
records
(EHRs),
ensuring
data
privacy,
facilitating
secure
transmission.
This
study
provides
comprehensive
analysis
the
adoption
AI
within
spotlighting
their
role
fortifying
security
transparency
leading
trajectory
for
promising
future
realm
healthcare.
Our
study,
employing
PRISMA
model,
scrutinized
402
relevant
articles,
narrative
to
explore
review
includes
architecture
blockchain,
examines
applications
with
without
integration,
elucidates
interdependency
between
blockchain.
major
findings
include:
(i)
it
protects
transfer,
digital
records,
security;
(ii)
enhances
EHR
COVID-19
transmission,
thereby
bolstering
healthcare
efficiency
reliability
through
precise
assessment
metrics;
(iii)
addresses
like
security,
decentralized
computing,
forming
robust
tripod.
revolutionize
by
EHRs,
enhancing
security.
Private
reflects
sector’s
commitment
improved
accessibility.
convergence
promises
enhanced
disease
identification,
response,
overall
efficacy,
key
sector
challenges.
Further
exploration
advanced
features
integrated
enhance
outcomes,
shaping
global
delivery
guaranteed
innovation.
Cancer Letters,
Journal Year:
2024,
Volume and Issue:
unknown, P. 217350 - 217350
Published: Nov. 1, 2024
Pancreatic
cancer
remains
one
of
the
most
challenging
malignancies
to
treat
due
its
late-stage
diagnosis,
aggressive
progression,
and
high
resistance
existing
therapies.
This
review
examines
latest
advancements
in
early
detection,
therapeutic
strategies,
with
a
focus
on
emerging
biomarkers,
tumor
microenvironment
(TME)
modulation,
integration
artificial
intelligence
(AI)
data
analysis.
We
highlight
promising
including
microRNAs
(miRNAs)
circulating
DNA
(ctDNA),
that
offer
enhanced
sensitivity
specificity
for
early-stage
diagnosis
when
combined
multi-omics
panels.
A
detailed
analysis
TME
reveals
how
components
such
as
cancer-associated
fibroblasts
(CAFs),
immune
cells,
extracellular
matrix
(ECM)
contribute
therapy
by
creating
immunosuppressive
barriers.
also
discuss
interventions
target
these
components,
aiming
improve
drug
delivery
overcome
evasion.
Furthermore,
AI-driven
analyses
are
explored
their
potential
interpret
complex
data,
enabling
personalized
treatment
strategies
real-time
monitoring
response.
conclude
identifying
key
areas
future
research,
clinical
validation
regulatory
frameworks
AI
applications,
equitable
access
innovative
comprehensive
approach
underscores
need
integrated,
outcomes
pancreatic
cancer.
Journal of Personalized Medicine,
Journal Year:
2024,
Volume and Issue:
14(8), P. 877 - 877
Published: Aug. 19, 2024
The
emergence
of
digitalization
and
artificial
intelligence
has
had
a
profound
impact
on
society,
especially
in
the
field
medicine.
Digital
health
is
now
reality,
with
an
increasing
number
people
using
chatbots
for
prognostic
or
diagnostic
purposes,
therapeutic
planning,
monitoring,
as
well
nutritional
mental
support.
Initially
designed
various
have
demonstrated
significant
advantages
medical
field,
indicated
by
multiple
sources.
However,
there
are
conflicting
views
current
literature,
some
sources
highlighting
their
drawbacks
limitations,
particularly
use
oncology.
This
state-of-the-art
review
article
seeks
to
present
both
benefits
context
medicine
cancer,
while
also
addressing
challenges
implementation,
offering
expert
insights
subject.
Cancers,
Journal Year:
2025,
Volume and Issue:
17(6), P. 1048 - 1048
Published: March 20, 2025
Background:
This
study
aimed
to
assess
the
safety
and
efficacy
of
gemcitabine
plus
S-1-based
chemoradiotherapy
(GS-CRT)
among
patients
with
locally
advanced
pancreatic
ductal
adenocarcinoma
(PDAC),
especially
those
unresectable
(UR-LA)
cases.
Methods:
A
total
351
consecutive
PDAC
were
enrolled
prognostic
predictors
disease-specific
survival
(DSS)
identified.
Results:
The
treatment
completion
rate
was
98.9%
Grade
3
or
higher
adverse
events
occurred
in
181
cases
(51.6%).
Among
319
re-evaluated
patients,
pancreatectomy
performed
184
(57.7%).
Based
on
resectability,
5-year
DSS
rates
for
entire
cohort
39.6%
(R),
43.8%
(BR-PV),
21.2%
(BR-A)
13.3%
(UR-LA),
while
performance
status
(PS),
hemoglobin
(Hb)
level,
celiac
artery
(CA)
involvement
≥180
degrees
JPS
8th
T
category.
In
resected
cases,
preoperative
PS,
CA19-9
JPS-T
factor,
degree
histological
response
adjuvant
chemotherapy.
UR-LA
nutritional
index
(PNI),
absence
pathological
venous
invasion
chemotherapy
DSS.
Conclusions:
Even
though
encountered
about
half
they
uneventfully
managed.
Therefore,
GS-CRT
is
safe
highly
tolerable
potential
improve
patients‘
prognosis.
Preoperative
levels
are
important
factors,
as
well
therapy.
(PNI)
curative
intent
surgery.
Beni-Suef University Journal of Basic and Applied Sciences,
Journal Year:
2025,
Volume and Issue:
14(1)
Published: April 7, 2025
Abstract
Background
Pancreatic
cancer
is
the
deadliest
form
of
with
a
low
survival
rate
due
to
its
late
diagnosis.
Hence,
early
detection
and
swift
intervention
are
very
crucial
for
management.
However,
current
diagnostic
markers
lack
sufficient
precision,
effectiveness
treatment
options
remains
imprecise,
emphasizing
need
more
advanced
approaches.
Main
body
Artificial
intelligence
(AI)
technology
enables
rapid
high-risk
groups
pancreatic
using
various
techniques
such
as
medical
imaging,
pathological
examination,
biomarkers,
other
methods,
facilitating
cancer.
Simultaneously,
AI
algorithms
may
also
be
used
forecast
duration
survival,
likelihood
recurrence,
metastasis,
response
treatment,
all
which
can
impact
prognosis.
Moreover,
applied
in
handling
cases
oncology
departments,
particular,
creating
computer-assisted
systems.
Conclusion
The
end-to-end
application
management
calls
multidisciplinary
collaboration
among
doctors,
laboratory
scientists,
data
analysts,
engineers.
Despite
limitations,
powerful
computational
capabilities
will
soon
combating
health
conditions.
SSRN Electronic Journal,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
As
the
use
of
large
language
models
(LLMs)
becomes
prevalent,
exploring
their
application
in
patient
care
is
crucial.
This
study
focuses
on
evaluating
impact
PromptWISE
paradigm,
designed
for
well-structured,
interactive,
and
supportive
education,
patient-LLM
interactions.
We
asked
Amazon
Mechanical
Turk
participants
to
evaluate
responses
generated
by
LLMs
using
simple
prompts
against
those
produced
PromptWISE-designed
prompts.
1074
volunteers
participated
survey,
with
overwhelming
preference
(n=837)
(p<0.0001).
The
results
highlight
tangible
prompt
engineering,
emphasizing
need
well-crafted
enhancing
interactions
without
providing
medical
advice.
Diagnostics,
Journal Year:
2024,
Volume and Issue:
14(19), P. 2174 - 2174
Published: Sept. 29, 2024
Background:
Continuous
breakthroughs
in
computational
algorithms
have
positioned
AI-based
models
as
some
of
the
most
sophisticated
technologies
healthcare
system.
AI
shows
dynamic
contributions
advancing
various
medical
fields
involving
data
interpretation
and
monitoring,
imaging
screening
diagnosis,
treatment
response
survival
prediction.
Despite
advances
clinical
oncology,
more
effort
must
be
employed
to
tailor
therapeutic
plans
based
on
each
patient’s
unique
transcriptomic
profile
within
precision/personalized
oncology
frame.
Furthermore,
standard
analysis
method
is
not
compatible
with
comprehensive
deciphering
significant
streams,
thus
precluding
prediction
accurate
options.
Methodology:
We
proposed
a
novel
approach
that
includes
obtaining
different
tumour
tissues
preparing
RNA
samples
for
using
specifically
trained,
programmed,
optimized
extracting
large
volumes,
refining,
analyzing
them.
Next,
results
will
scanned
against
an
expansive
drug
library
predict
target
tested
drugs.
The
obtained
target-drug
combination/s
then
validated
vitro
vivo
experimental
models.
Finally,
best
combination
option/s
introduced
patient.
also
provided
review
discussing
models’
recent
innovations
implementations
aid
molecular
diagnosis
planning.
Results:
expected
generated
by
provide
inclusive
genomic
patient,
containing
statistical
bioinformatics
analyses,
identification
dysregulated
pathways,
detection
targeted
genes,
recognition
biomarkers.
Subjecting
these
pairing
processes
result
graphs
presenting
target’s
likely
rate
Different
investigations
further
validate
selection
drug/s
pairs.
Conclusions:
Leveraging
rigorous
manipulation
large-scale
datasets
specific
cancer
care
paths.
Such
strategy
would
shape
according
demand,
fortifying
avenue
personalized/precision
medicine.
Undoubtedly,
this
assist
improving
domain
alleviate
burden
clinicians
coming
decade.
