BACKGROUND
Digital
twins
(DTs)
are
digital
representations
of
real-world
systems,
enabling
advanced
simulations,
predictive
modeling,
and
real-time
optimization
in
various
fields,
including
health
care.
Despite
growing
interest,
the
integration
DTs
care
faces
challenges
such
as
fragmented
applications,
ethical
concerns,
barriers
to
adoption.
OBJECTIVE
This
study
systematically
reviews
existing
literature
on
DT
applications
with
three
objectives:
(1)
map
primary
(2)
identify
key
limitations,
(3)
highlight
gaps
that
can
guide
future
research.
METHODS
A
meta-review
was
conducted
a
systematic
fashion,
adhering
PRISMA-ScR
(Preferred
Reporting
Items
for
Systematic
Reviews
Meta-Analyses
extension
Scoping
Reviews)
guidelines,
included
25
published
between
2021
2024.
The
search
encompassed
5
databases:
PubMed,
CINAHL,
Web
Science,
Embase,
PsycINFO.
Thematic
synthesis
used
categorize
stakeholders,
RESULTS
total
3
were
identified:
personalized
medicine,
operational
efficiency,
medical
While
current
diagnostics,
patient-specific
treatment
hospital
resource
optimization,
remain
their
early
stages
development,
they
significant
potential
DTs.
Challenges
include
data
quality,
issues,
socioeconomic
barriers.
review
also
identified
scalability,
interoperability,
clinical
validation.
CONCLUSIONS
hold
transformative
care,
providing
individualized
accelerated
However,
adoption
is
hindered
by
technical,
ethical,
financial
Addressing
these
issues
requires
interdisciplinary
collaboration,
standardized
protocols,
inclusive
implementation
strategies
ensure
equitable
access
meaningful
impact.
CLINICALTRIAL
Journal of Medical Internet Research,
Год журнала:
2025,
Номер
27, С. e69544 - e69544
Опубликована: Янв. 24, 2025
Background
Digital
twins
(DTs)
are
digital
representations
of
real-world
systems,
enabling
advanced
simulations,
predictive
modeling,
and
real-time
optimization
in
various
fields,
including
health
care.
Despite
growing
interest,
the
integration
DTs
care
faces
challenges
such
as
fragmented
applications,
ethical
concerns,
barriers
to
adoption.
Objective
This
study
systematically
reviews
existing
literature
on
DT
applications
with
three
objectives:
(1)
map
primary
(2)
identify
key
limitations,
(3)
highlight
gaps
that
can
guide
future
research.
Methods
A
meta-review
was
conducted
a
systematic
fashion,
adhering
PRISMA-ScR
(Preferred
Reporting
Items
for
Systematic
Reviews
Meta-Analyses
extension
Scoping
Reviews)
guidelines,
included
25
published
between
2021
2024.
The
search
encompassed
5
databases:
PubMed,
CINAHL,
Web
Science,
Embase,
PsycINFO.
Thematic
synthesis
used
categorize
stakeholders,
Results
total
3
were
identified:
personalized
medicine,
operational
efficiency,
medical
While
current
diagnostics,
patient-specific
treatment
hospital
resource
optimization,
remain
their
early
stages
development,
they
significant
potential
DTs.
Challenges
include
data
quality,
issues,
socioeconomic
barriers.
review
also
identified
scalability,
interoperability,
clinical
validation.
Conclusions
hold
transformative
care,
providing
individualized
accelerated
However,
adoption
is
hindered
by
technical,
ethical,
financial
Addressing
these
issues
requires
interdisciplinary
collaboration,
standardized
protocols,
inclusive
implementation
strategies
ensure
equitable
access
meaningful
impact.
Current Pharmaceutical Biotechnology,
Год журнала:
2024,
Номер
25(16), С. 2078 - 2088
Опубликована: Янв. 29, 2024
Low
adherence
to
chronic
treatment
regimens
is
a
significant
barrier
improving
clinical
outcomes
in
patients
with
diseases.
result
of
multiple
factors.
Artificial
intelligence
(AI)
has
made
significant
progress
in
addressing
the
specific
obstacles
related
to
post-cancer
physical
rehabilitation.
This
article
examines
AI
technologies
such
as
Support
Vector
Machines
(SVM),
Bayesian
Inference,
Reinforcement
Learning,
and
Partially
Observable
Markov
Decision
Processes
(POMDPs),
focusing
on
their
potential
improve
effectiveness
adaptability
of
rehabilitation
strategies.
SVM
is
recognized
for
its
capability
analyze
high-dimensional
data
obtained
from
wearable
sensors,
thereby
enabling
realtime
patient
monitoring.
methods
facilitate
flexible
adjustment
treatment
plans,
enhancing
efficient
allocation
resources
healthcare
environments.
Learning
enables
realtime,
dynamic
optimization
robotic-assisted
physiotherapy,
yet
it
also
raises
ethical
concerns
regarding
automated
decision-making.
POMDPs
provide
a
mathematical
framework
effectively
uncertainties
involved
care.
have
personalized
adaptive
treatments.
However,
important
address
considerations
privacy,
informed
consent,
algorithmic
fairness
through
further
investigation.
emphasizes
importance
interdisciplinary
research
governance
maximizing
transforming
Brain Sciences,
Год журнала:
2024,
Номер
14(3), С. 209 - 209
Опубликована: Фев. 23, 2024
There
is
still
controversy
surrounding
the
definition
and
mechanisms
of
consciousness.
The
constrained
disorder
principle
(CDP)
defines
complex
systems
by
their
dynamic
borders,
limiting
inherent
disorder.
In
line
with
CDP,
brain
exhibits
a
bounded
borders
essential
for
proper
function,
efficient
energy
use,
life
support
under
continuous
perturbations.
brain’s
variability
contributes
to
its
adaptability
flexibility.
Neuronal
signal
challenges
association
structures
consciousness
methods
assessing
present
paper
discusses
some
theories
about
consciousness,
emphasizing
failure
explain
variability.
This
describes
how
CDP
accounts
consciousness’s
variability,
complexity,
entropy,
uncertainty.
Using
newly
developed
second-generation
artificial
intelligence
systems,
we
describe
CDP-based
platforms
may
improve
disorders
(DoC)
accounting
platform
could
be
used
response
current
interventions
develop
new
therapeutic
regimens
patients
DoC
in
future
studies.
ImmunoTargets and Therapy,
Год журнала:
2024,
Номер
Volume 13, С. 525 - 539
Опубликована: Окт. 1, 2024
Lack
of
response
to
immunotherapies
poses
a
significant
challenge
in
treating
immune-mediated
disorders
and
cancers.
While
the
mechanisms
associated
with
poor
responsiveness
are
not
well
defined
change
between
among
subjects,
current
methods
for
overcoming
loss
insufficient.
The
Constrained
Disorder
Principle
(CDP)
explains
biological
systems
based
on
their
inherent
variability,
bounded
by
dynamic
boundaries
that
internal
external
perturbations.
Inter
intra-subject
variability
characterize
immune
system,
making
it
difficult
provide
single
therapeutic
regimen
all
patients
even
same
over
time.
dynamicity
is
also
personalizing
immunotherapies.
CDP-based
second-generation
artificial
intelligence
system
an
outcome-based
platform
incorporates
personalized
signatures
into
may
improving
treatments.
offer
method
identifying
new
biomarkers
early
diagnosis,
monitoring
immune-related
disorders,
evaluating
Journal of Medicine and Life,
Год журнала:
2025,
Номер
18(1), С. 67 - 72
Опубликована: Янв. 1, 2025
Interactions
between
immune
system
constituents
are
mediated
through
direct
contact
or
the
transfer
of
mediators.
The
study
aimed
to
assess
correlation
components
and
out-of-body
signals
in
a
model
liver
inflammation.
In
first
experiment,
mice
injected
with
Concanavalin
A
(ConA)
were
housed
cage
tube
on
top
containing
healthy
livers
harvested
from
ConA.
second
that
contained
splenocytes
naïve
donors
treated
vitro
dexamethasone.
Mice
tested
for
serum
aspartate
aminotransferase
(AST)
alanine
(ALT)
levels.
External
whole
spleens
influenced
immune-mediated
inflammatory
response
mice.
When
ConA-injected
cages
tubes
mice,
ALT
levels
significantly
reduced.
elevated
when
kept
part
ConA
had
increased
Similarly,
dexamethasone-treated
also
showed
data
suggest
correlations
can
be
established
using
without
Biology,
Год журнала:
2024,
Номер
13(10), С. 830 - 830
Опубликована: Окт. 16, 2024
Uncertainty
in
biology
refers
to
situations
which
information
is
imperfect
or
unknown.
Variability,
on
the
other
hand,
measured
by
frequency
distribution
of
observed
data.
Biological
variability
adds
uncertainty.
The
Constrained
Disorder
Principle
(CDP)
defines
all
systems
universe
their
inherent
variability.
According
CDP,
exhibit
a
degree
necessary
for
proper
function,
allowing
them
adapt
changes
environments.
Per
while
differs
from
uncertainty,
it
can
be
viewed
as
regulated
mechanism
efficient
functionality
rather
than
This
paper
explores
various
aspects
un-certainties
biology.
It
focuses
using
CDP-based
platforms
refining
fuzzy
algorithms
address
some
challenges
associated
with
biological
and
medical
uncertainties.
Developing
decision
tree
that
considers
natural
help
minimize
method
reveal
previously
unidentified
classes,
reduce
number
unknowns,
improve
accuracy
modeling
results,
generate
algorithm
outputs
are
more
biologically
clinically
relevant.
Frontiers in Network Physiology,
Год журнала:
2024,
Номер
4
Опубликована: Дек. 18, 2024
The
Constrained
Disorder
Principle
(CDP)
defines
all
systems
in
nature
by
their
degree
of
inherent
variability.
Per
the
CDP,
intrinsic
variability
is
mandatory
for
proper
function
and
dynamically
changed
based
on
pressures.
CDP
boundaries
as
a
mechanism
continuous
adaptation
to
internal
external
perturbations,
enabling
survival
under
dynamic
conditions.
laws
govern
world's
natural
phenomena
underlie
systems.
Nevertheless,
physics
do
not
entirely
explain
systems'
functionality
pressure,
which
essential
determining
correct
operation
complex
nature.
Variability
noise
are
two
broad
sources
unpredictability
biology
technology.
This
paper
explores
how
provides
examples
from
various
areas
where
applies,
including
climate,
genetic,
biology,
human
behavioral
variabilities.
According
system
malfunction
results
inappropriate
performance
environment
influences
physiological
variability,
species
interactions
influence
eco-evolutionary
outcomes.
behavior
being
driven
randomness
accounts
malfunctions
corrections.
reviews
variability-based
algorithms
CDP-based
second-generation
artificial
intelligence
potential
improving
prediction
efficiency
using
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 48 - 68
Опубликована: Июнь 28, 2024
Digital
twin
is
the
virtual
representation
of
a
physical
system
that
processes
information
from
its
counterpart's
environment,
used
to
predict,
simulate,
and
validate
system's
future
behaviour.
system,
being
an
emergent
technology,
has
seen
implementations
in
wide
array
industries
such
as
smart
cities,
engineering,
etc.
In
healthcare,
digital
technology
shows
great
promise
improve
various
areas
patient
care,
virtualization
hospital
spaces,
There
are
concerns
regarding
data
confidentiality,
safety,
accuracy
reliability,
avoidance
bias,
These
can
be
combated
only
through
thorough
feedback
experts,
i.e.,
healthcare
professionals.
This
chapter
aims
provide
valuable
insights
into
different
needs
professionals
while
implementing
systems
aiding
diagnosis,
treatment
planning,
monitoring,
collaboration
among
specialty
teams
all
dealing
with
security
sampling
bias.
