Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 31, 2025
Abstract
The
rapid
advancement
of
battery
technology
has
driven
the
need
for
innovative
approaches
to
enhance
management
systems.
In
response,
concept
a
cognitive
digital
twin
been
developed
serve
as
sophisticated
virtual
model
that
dynamically
simulates,
predicts,
and
optimizes
behavior.
These
models
integrate
real‐time
data
with
in‐depth
physical
insights,
offering
comprehensive
solution
management.
Fundamental
this
development
are
advanced
characterization
techniques
such
microscopy,
spectroscopy,
tomography,
electrochemical
methods—that
provide
critical
insights
into
underlying
physics
batteries.
Additionally,
machine
learning
(ML)
extends
beyond
predictive
analytics
analytical
capabilities.
By
uncovering
deep
ML
significantly
improving
accuracy,
reliability,
interpretability
these
techniques.
This
review
explores
how
integrating
traditional
bridges
gap
between
data‐driven
analysis.
synergy
not
only
enhances
precision
computational
efficiency
but
also
minimizes
human
intervention,
thereby
paving
way
more
robust
transparent
technologies
in
research.
Genome Medicine,
Journal Year:
2025,
Volume and Issue:
17(1)
Published: Feb. 7, 2025
Abstract
Ineffective
medication
is
a
major
healthcare
problem
causing
significant
patient
suffering
and
economic
costs.
This
issue
stems
from
the
complex
nature
of
diseases,
which
involve
altered
interactions
among
thousands
genes
across
multiple
cell
types
organs.
Disease
progression
can
vary
between
patients
over
time,
influenced
by
genetic
environmental
factors.
To
address
this
challenge,
digital
twins
have
emerged
as
promising
approach,
led
to
international
initiatives
aiming
at
clinical
implementations.
Digital
are
virtual
representations
health
disease
processes
that
integrate
real-time
data
simulations
predict,
prevent,
personalize
treatments.
Early
applications
DTs
shown
potential
in
areas
like
artificial
organs,
cancer,
cardiology,
hospital
workflow
optimization.
However,
widespread
implementation
faces
several
challenges:
(1)
characterizing
dynamic
molecular
changes
biological
scales;
(2)
developing
computational
methods
into
DTs;
(3)
prioritizing
mechanisms
therapeutic
targets;
(4)
creating
interoperable
DT
systems
learn
each
other;
(5)
designing
user-friendly
interfaces
for
clinicians;
(6)
scaling
technology
globally
equitable
access;
(7)
addressing
ethical,
regulatory,
financial
considerations.
Overcoming
these
hurdles
could
pave
way
more
predictive,
preventive,
personalized
medicine,
potentially
transforming
delivery
improving
outcomes.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(1), P. 205 - 205
Published: Jan. 2, 2025
Objective:
In
this
paper,
we
explore
the
correlation
between
performance
reporting
and
development
of
inclusive
AI
solutions
for
biomedical
problems.
Our
study
examines
critical
aspects
bias
noise
in
context
medical
decision
support,
aiming
to
provide
actionable
solutions.
Contributions:
A
key
contribution
our
work
is
recognition
that
measurement
processes
introduce
arising
from
human
data
interpretation
selection.
We
concept
“noise-bias
cascade”
explain
their
interconnected
nature.
While
current
models
handle
well,
remains
a
significant
obstacle
achieving
practical
these
models.
analysis
spans
entire
lifecycle,
collection
model
deployment.
Recommendations:
To
effectively
mitigate
bias,
assert
need
implement
additional
measures
such
as
rigorous
design;
appropriate
statistical
analysis;
transparent
reporting;
diverse
research
representation.
Furthermore,
strongly
recommend
integration
uncertainty
during
deployment
ensure
utmost
fairness
inclusivity.
These
comprehensive
recommendations
aim
minimize
both
noise,
thereby
improving
future
support
systems.
Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 31, 2025
Abstract
The
rapid
advancement
of
battery
technology
has
driven
the
need
for
innovative
approaches
to
enhance
management
systems.
In
response,
concept
a
cognitive
digital
twin
been
developed
serve
as
sophisticated
virtual
model
that
dynamically
simulates,
predicts,
and
optimizes
behavior.
These
models
integrate
real‐time
data
with
in‐depth
physical
insights,
offering
comprehensive
solution
management.
Fundamental
this
development
are
advanced
characterization
techniques
such
microscopy,
spectroscopy,
tomography,
electrochemical
methods—that
provide
critical
insights
into
underlying
physics
batteries.
Additionally,
machine
learning
(ML)
extends
beyond
predictive
analytics
analytical
capabilities.
By
uncovering
deep
ML
significantly
improving
accuracy,
reliability,
interpretability
these
techniques.
This
review
explores
how
integrating
traditional
bridges
gap
between
data‐driven
analysis.
synergy
not
only
enhances
precision
computational
efficiency
but
also
minimizes
human
intervention,
thereby
paving
way
more
robust
transparent
technologies
in
research.
