Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 13 - 31
Published: June 28, 2024
As
advancements
in
artificial
intelligence
(AI)
continue
to
revolutionize
various
industries,
personalized
nutritional
planning
emerges
as
a
promising
application
the
realm
of
healthcare
and
wellness.
This
chapter
delves
into
intersection
AI-driven
regulatory
landscape
governing
food
regulations,
laws,
potential
scams.
By
leveraging
AI
algorithms,
individuals
can
receive
tailored
dietary
recommendations
based
on
their
unique
health
profiles,
genetic
makeup,
lifestyle
factors.
However,
amidst
this
innovation,
ensuring
compliance
with
regulations
laws
becomes
paramount
safeguard
consumer
prevent
deceptive
practices.
Moreover,
explores
challenges
posed
by
scams
fraudulent
claims
burgeoning
market
nutrition,
emphasizing
importance
robust
frameworks
education
initiatives.
Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 276 - 307
Published: Aug. 9, 2024
Artificial
intelligence
(AI)
is
increasingly
becoming
a
pivotal
tool
in
the
field
of
dietary
management,
offering
innovative
solutions
for
personalized
nutrition
and
health
optimization.
This
chapter
examines
application
AI
technologies
managing
habits
improving
nutritional
outcomes.
It
covers
various
techniques,
including
machine
learning,
natural
language
processing,
computer
vision,
used
to
analyze
interpret
vast
amounts
data.
The
authors
discuss
how
can
provide
tailored
recommendations,
monitor
eating
behaviors,
predict
deficiencies.
Real-world
examples
case
studies
are
presented
demonstrate
efficacy
potential
AI-driven
management
systems.
By
integrating
into
this
highlights
transformative
intelligent
systems
enhancing
individual
preventing
diet-related
diseases.
European Geriatric Medicine,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 27, 2025
This
position
paper
aims
to
address
the
challenges
of
managing
type
2
diabetes
mellitus
(T2DM)
in
frail
older
adults,
a
diverse
and
growing
demographic
with
significant
variability
health
status.
The
primary
research
questions
are:
How
can
frailty
assessment
be
effectively
integrated
into
care?
What
strategies
optimize
glycaemic
control
outcomes
for
adults?
innovative
tools
technologies,
including
artificial
intelligence
(AI),
improve
management
this
population?
uses
5
I's
framework
(Identification,
Innovation,
Individualization,
Integration,
Intelligence)
integrate
care,
proposing
such
as
tools,
novel
therapies,
digital
AI
systems.
It
also
examines
metabolic
heterogeneity,
highlighting
anorexic-malnourished
sarcopenic-obese
phenotypes.
proposed
highlights
importance
tailoring
targets
levels,
prioritizing
quality
life,
minimizing
treatment
burden.
Strategies
leveraging
are
emphasized
their
potential
enhance
personalized
care.
distinct
needs
two
phenotypes
outlined,
specific
recommendations
each
group.
calls
holistic,
patient-centered
approach
care
ensuring
equity
access
innovations
life.
need
fill
evidence
gaps,
refine
healthcare
integration
better
vulnerable
Hospital Topics,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 14
Published: April 10, 2025
Diabetes
mellitus,
a
non-communicable
metabolic
disorder,
is
significant
global
health
concern,
with
rising
prevalence
rates
resulting
in
increased
economic
burdens
on
healthcare
systems.
Early
detection
and
diagnosis
are
crucial
for
preventing
severe
complications.
Artificial
Intelligence
(AI)
offers
immense
potential
to
revolutionize
diabetes
management
early
detection.
This
study
aims
understand
the
factors
influencing
medical
professionals'
adoption
of
AI-based
tools
intervention,
develop
predictive
models
identify
adopters
propose
Hub-and-Spoke
model
screening
South
India,
particularly
segments
predominantly
rice-based
diet.
By
leveraging
machine
learning
techniques,
identifies
key
demographic
professional
that
predict
AI
intent.
The
proposed
addresses
logistical
challenges
screening,
underserved
regions.
research
contributes
effort
combat
diabetes,
improve
outcomes,
optimize
resource
allocation.
Journal of Diabetes Science and Technology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 22, 2024
Artificial
intelligence
(AI)
is
increasingly
being
used
to
diagnose
complications
of
diabetes.
technology
that
enables
computers
and
machines
simulate
human
solve
complicated
problems.
In
this
article,
we
address
current
likely
future
applications
for
AI
be
applied
diabetes
its
complications,
including
pharmacoadherence
therapy,
diagnosis
hypoglycemia,
diabetic
eye
disease,
kidney
diseases,
neuropathy,
foot
ulcers,
heart
failure
in
advantageous
because
it
can
handle
large
complex
datasets
from
a
variety
sources.
With
each
additional
type
data
incorporated
into
clinical
picture
patient,
the
calculation
becomes
specific.
foundation
emerging
medical
technologies;
will
power
diagnosing
complications.
BACKGROUND
The
global
prevalence
of
diabetes
is
one
the
most
pressing
health
concerns
worldwide.
Early
detection
crucial
for
effective
management
and
prevention
complications.
Various
artificial
intelligence
(AI)
techniques,
including
machine
learning
deep
learning,
are
employed
detection.
OBJECTIVE
This
systematic
review
meta-analysis
aimed
to
evaluate
effectiveness
feasibility
AI-driven
approaches
early
in
primary
preventive
care
settings.
METHODS
We
followed
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
model
minimize
bias
enhance
accuracy.
In
October
2024,
we
searched
two
databases,
PubMed
Google
Scholar,
using
keywords
such
as
("Artificial
intelligence"
OR
"machine
learning")
AND
("early
detection"
"diabetes
prediction").
Data
extraction
focused
on
study
design,
population
characteristics,
AI
type,
accuracy,
comparison
groups,
outcomes
(e.g.,
diagnostic
accuracy),
follow-up
periods.
A
was
performed
RevMan
assess
precision,
predictive
value,
risk
stratification
capability.
RESULTS
included
studies
improving
prediction
through
advanced
algorithms,
achieving
up
96.75%
datasets
used
were
diverse,
demographic,
clinical,
behavioral
variables.
However,
also
highlighted
limitations,
gaps
data
completeness
external
validation,
with
missing
being
a
recurrent
issue.
CONCLUSIONS
methods
show
substantial
promise
precision
patient
management.
Future
research
should
address
methodological
gaps,
ensure
robust
prioritize
long-term
real-world
implementation.
D Y Patil Journal of Health Sciences,
Journal Year:
2025,
Volume and Issue:
13(1), P. 8 - 16
Published: Jan. 1, 2025
Abstract
Artificial
intelligence
(AI)
is
transforming
nursing
care
with
innovative
solutions
for
managing
diabetic
patients.
Integrating
AI
allows
nurses
to
improve
patient
assessment
accuracy,
personalize
plans,
and
streamline
management,
enhancing
outcomes
quality.
Therefore,
this
study
aims
assess
diabetes
management
through
AI,
including
its
limitations,
disadvantages,
benefits
of
integrating
patient-centered
(PCC).
This
review
article
analyzes
existing
literature
on
the
integration
in
patients
diabetes,
evaluating
studies
that
demonstrate
improvements
personalized
efficiency.
offers
insights
into
how
contributes
achieving
quality
However,
challenges
such
as
data
privacy
concerns,
high
implementation
costs,
need
specialized
training
hinder
widespread
adoption.
In
conclusion,
incorporation
signifies
a
promising
advancement
healthcare
delivery
system.
Despite
challenges,
potential
research
value
practice
are
undeniable,
applications
continue
expand
rapidly.
Future
programs
utilize
comprehensive
integrated
technologies
promise
deliver
improved
