Genes,
Год журнала:
2024,
Номер
15(4), С. 468 - 468
Опубликована: Апрель 8, 2024
In
recent
years,
the
FDA
has
approved
numerous
anti-cancer
drugs
that
are
mutation-based
for
clinical
use.
These
have
improved
precision
of
treatment
and
reduced
adverse
effects
side
effects.
Personalized
therapy
is
a
prominent
hot
topic
current
medicine
also
represents
future
direction
development.
With
continuous
advancements
in
gene
sequencing
high-throughput
screening,
research
development
strategies
personalized
developed
rapidly.
This
review
elaborates
strategies,
which
include
artificial
intelligence,
multi-omics
analysis,
chemical
proteomics,
computation-aided
drug
design.
technologies
rely
on
molecular
classification
diseases,
global
signaling
network
within
organisms,
new
models
all
targets,
significantly
support
medicine.
Meanwhile,
we
summarize
drugs,
such
as
lorlatinib,
osimertinib,
other
natural
products,
deliver
therapeutic
based
genetic
mutations.
highlights
potential
challenges
interpreting
mutations
combining
while
providing
ideas
pharmacogenomics
cancer
study.
Indian Journal of Ophthalmology,
Год журнала:
2023,
Номер
71(2), С. 424 - 430
Опубликована: Фев. 1, 2023
Purpose:
This
study
was
done
to
explore
the
utility
of
artificial
intelligence
(AI)
and
machine
learning
in
diagnosis
grouping
intraocular
retinoblastoma
(iRB).
Methods:
It
a
retrospective
observational
using
AI
Machine
learning,
Computer
Vision
(OpenCV).
Results:
Of
771
fundus
images
109
eyes,
181
had
no
tumor
590
displayed
iRB
based
on
review
by
two
independent
ocular
oncologists
(with
an
interobserver
variability
<1%).
The
sensitivity,
specificity,
positive
predictive
value,
negative
value
trained
model
were
85%,
99%,
99.6%,
67%,
respectively.
for
detection
RB
96%,
94%,
97%,
91%,
these,
eyes
normal
(n
=
31)
or
belonged
groupA
(n=1),
B
(n=22),
C
(n=8),
D
(n=23),and
E
(n=24)
0%).
100%,
100%
group
A;
82%,
20
21
98%,
90%,
96%
B;
63%,
83%,
97%
C;
78%,
94%
D,
92%,
73%,
98%
E,
Conclusion:
Based
our
study,
we
conclude
that
is
highly
sensitive
with
high
specificity
classification
iRB.
Tomography,
Год журнала:
2023,
Номер
9(4), С. 1443 - 1455
Опубликована: Июль 28, 2023
Objectives:
This
scoping
review
was
conducted
to
determine
the
barriers
and
enablers
associated
with
acceptance
of
artificial
intelligence/machine
learning
(AI/ML)-enabled
innovations
into
radiology
practice
from
a
physician’s
perspective.
Methods:
A
systematic
search
performed
using
Ovid
Medline
Embase.
Keywords
were
used
generate
refined
queries
inclusion
computer-aided
diagnosis,
intelligence,
enablers.
Three
reviewers
assessed
articles,
fourth
reviewer
for
disagreements.
The
risk
bias
mitigated
by
including
both
quantitative
qualitative
studies.
Results:
An
electronic
January
2000
2023
identified
513
Twelve
articles
found
fulfill
criteria:
studies
(n
=
4),
survey
7),
randomized
controlled
trials
(RCT)
1).
Among
most
common
AI
implementation
radiologists’
lack
trust
in
innovations;
awareness,
knowledge,
familiarity
technology;
perceived
threat
professional
autonomy
radiologists.
important
high
expectations
AI’s
potential
added
value;
decrease
errors
diagnosis;
increase
efficiency
when
reaching
improve
quality
patient
care.
Conclusions:
that
few
have
been
designed
specifically
identify
practice.
majority
perception
replacing
radiologists,
rather
than
other
or
adoption
AI.
To
comprehensively
evaluate
advantages
disadvantages
integrating
practice,
gathering
more
robust
research
evidence
on
stakeholder
perspectives
attitudes
is
essential.
Artificial
intelligence
(AI)
has
come
to
play
a
pivotal
role
in
revolutionizing
medical
practices,
particularly
the
field
of
pancreatic
cancer
detection
and
management.
As
leading
cause
cancer-related
deaths,
warrants
innovative
approaches
due
its
typically
advanced
stage
at
diagnosis
dismal
survival
rates.
Present
methods,
constrained
by
limitations
accuracy
efficiency,
underscore
necessity
for
novel
solutions.
AI-driven
methodologies
present
promising
avenues
enhancing
early
prognosis
forecasting.
Through
analysis
imaging
data,
biomarker
profiles,
clinical
information,
AI
algorithms
excel
discerning
subtle
abnormalities
indicative
with
remarkable
precision.
Moreover,
machine
learning
(ML)
facilitate
amalgamation
diverse
data
sources
optimize
patient
care.
However,
despite
huge
potential,
implementation
faces
various
challenges.
Issues
such
as
scarcity
comprehensive
datasets,
biases
algorithm
development,
concerns
regarding
privacy
security
necessitate
thorough
scrutiny.
While
offers
immense
promise
transforming
management,
ongoing
research
collaborative
efforts
are
indispensable
overcoming
technical
hurdles
ethical
dilemmas.
This
review
delves
into
evolution
AI,
application
detection,
challenges
considerations
inherent
integration.
Genes,
Год журнала:
2024,
Номер
15(4), С. 468 - 468
Опубликована: Апрель 8, 2024
In
recent
years,
the
FDA
has
approved
numerous
anti-cancer
drugs
that
are
mutation-based
for
clinical
use.
These
have
improved
precision
of
treatment
and
reduced
adverse
effects
side
effects.
Personalized
therapy
is
a
prominent
hot
topic
current
medicine
also
represents
future
direction
development.
With
continuous
advancements
in
gene
sequencing
high-throughput
screening,
research
development
strategies
personalized
developed
rapidly.
This
review
elaborates
strategies,
which
include
artificial
intelligence,
multi-omics
analysis,
chemical
proteomics,
computation-aided
drug
design.
technologies
rely
on
molecular
classification
diseases,
global
signaling
network
within
organisms,
new
models
all
targets,
significantly
support
medicine.
Meanwhile,
we
summarize
drugs,
such
as
lorlatinib,
osimertinib,
other
natural
products,
deliver
therapeutic
based
genetic
mutations.
highlights
potential
challenges
interpreting
mutations
combining
while
providing
ideas
pharmacogenomics
cancer
study.
