IP International Journal of Ocular Oncology and Oculoplasty,
Journal Year:
2025,
Volume and Issue:
10(4), P. 196 - 207
Published: Jan. 14, 2025
In
the
domains
of
ocular
oncology
and
oculoplasty,
machine
learning
(ML)
has
become
a
game-changing
technology,
providing
previously
unheard-of
levels
precision
in
diagnosis,
treatment
planning,
outcome
prediction.
Using
imaging
modalities,
genomic
data,
clinical
characteristics,
this
chapter
investigates
integration
algorithms
detection
tumours,
including
retinoblastoma
uveal
melanoma.
Through
predictive
modelling
real-time
decision-making,
it
also
emphasises
how
ML
might
improve
surgical
outcomes
orbital
reconstruction
eyelid
correction.
Automated
examination
fundus
photographs,
histological
slides,
3D
been
made
possible
by
methods
like
deep
natural
language
processing,
which
have
improved
individualised
therapeutic
approaches
decreased
diagnostic
errors.
Additionally,
use
augmented
reality
robotics
surgery
is
significant
development
oculoplasty.
Notwithstanding
its
potential,
issues
data
heterogeneity,
algorithm
interpretability,
ethical
considerations
are
roadblocks
that
need
to
be
addressed.
This
explores
cutting-edge
developments,
real-world
uses,
potential
future
paths,
offering
researchers
doctors
thorough
resource.
Dipali
Vikas
Mane,
Associate
Professor,
Shriram
Shikshan
Sanstha’s
College
Pharmacy,
Paniv-413113
International Medical Science Research Journal,
Journal Year:
2024,
Volume and Issue:
4(3), P. 341 - 360
Published: March 22, 2024
The
intersection
of
big
data
and
healthcare
product
development
has
catalyzed
transformative
shifts
in
the
industry,
revolutionizing
how
medical
solutions
are
conceptualized,
designed,
deployed.
This
theoretical
analytical
review
explores
profound
impact
on
development,
elucidating
its
implications
across
various
facets
landscape.
Utilizing
analytics,
stakeholders
can
harness
vast
volumes
structured
unstructured
to
derive
actionable
insights.
These
insights
inform
evidence-based
decision-making
processes,
driving
innovation
pipelines.
By
analyzing
real-time
patient
data,
trends,
treatment
outcomes,
developers
gain
invaluable
into
disease
progression,
efficacy,
preferences,
thus
facilitating
creation
tailored,
patient-centric
solutions.
Moreover,
analytics
play
a
pivotal
role
improving
outcomes
quality
care.
Through
predictive
machine
learning
algorithms,
providers
identify
at-risk
populations,
predict
outbreaks,
personalize
plans.
proactive
approach
enhances
preventive
care
strategies
minimizes
costs
by
averting
complications
hospital
readmissions.
However,
integration
is
not
without
challenges.
Data
privacy
security
concerns
necessitate
robust
frameworks
safeguard
sensitive
information.
regulatory
compliance
must
evolve
accommodate
complexities
while
ensuring
safety
integrity.
Despite
these
challenges,
potential
vast.
leveraging
bridge
gaps
access
equity,
tailor
interventions
underserved
optimize
resource
allocation.
In
conclusion,
this
underscores
development.
embracing
data-driven
approaches,
drive
innovation,
enhance
navigate
evolving
landscape
with
agility
efficacy.
Keywords:
Big
Data,
Healthcare
Product
Development,
Innovation,
Patient
Outcomes,
Analytics,
Regulatory
Compliance.
Asian Journal of Research in Computer Science,
Journal Year:
2024,
Volume and Issue:
17(5), P. 85 - 107
Published: March 8, 2024
The
study
investigates
data
governance
challenges
within
AI-enabled
healthcare
systems,
focusing
on
Project
Nightingale
as
a
case
to
elucidate
the
complexities
of
balancing
technological
advancements
with
patient
privacy
and
trust.
Utilizing
survey
methodology,
were
collected
from
843
service
users
employing
structured
questionnaire
designed
measure
perceptions
AI
in
healthcare,
trust
providers,
concerns
about
privacy,
impact
regulatory
frameworks
adoption
technologies.
reliability
instrument
was
confirmed
Cronbach's
Alpha
0.81,
indicating
high
internal
consistency.
multiple
regression
analysis
revealed
significant
findings:
positive
relationship
between
awareness
projects
countered
by
negative
Additionally,
familiarity
perceived
effectiveness
positively
correlated
data,
while
constraints
issues
identified
barriers
effective
technologies
healthcare.
highlights
critical
need
for
enhanced
transparency,
public
awareness,
robust
navigate
ethical
associated
recommends
adopting
flexible,
principle-based
approaches
fostering
multi-stakeholder
collaboration
ensure
deployment
that
prioritize
welfare
International Journal of Science and Research Archive,
Journal Year:
2024,
Volume and Issue:
11(1), P. 2101 - 2110
Published: Feb. 18, 2024
This
review
explores
the
pivotal
role
of
Artificial
Intelligence
(AI)
in
revolutionizing
fraud
detection
and
prevention
within
realm
financial
services.
As
crimes
become
increasingly
sophisticated,
traditional
methods
fall
short,
necessitating
integration
advanced
technologies.
AI
emerges
as
a
transformative
force,
employing
machine
learning
algorithms,
predictive
analytics,
anomaly
to
fortify
defenses
against
fraudulent
activities.
The
provides
an
in-depth
examination
historical
context,
tracing
evolution
from
manual
contemporary
AI-driven
approaches.
It
delves
into
diverse
models
utilized
prevention,
including
supervised
unsupervised
learning,
deep
natural
language
processing.
nuanced
analysis
encompasses
effectiveness
identifying
intricate
patterns
indicative
behavior,
demonstrating
its
superiority
discerning
anomalies
vast
dynamic
datasets.
Moreover,
elucidates
real-world
implications
detection,
spotlighting
instances
where
technology
has
successfully
thwarted
schemes.
ethical
considerations
inherent
are
also
scrutinized,
emphasizing
importance
responsible
transparent
practices
mitigate
biases
ensure
fairness
decision-making
processes.
landscape
navigates
era
digital
transformation,
sheds
light
on
future
trends
innovations
detection.
Anticipated
developments
include
Explainable
(XAI),
federated
continuous
adaptation
emerging
threats.
discussion
extends
collaborative
efforts
between
institutions,
regulatory
bodies,
providers
create
robust
ecosystem
capable
staying
ahead
evolving
tactics.
In
conclusion,
this
encapsulates
underscores
impact
AI,
not
only
bolstering
security
measures
but
fostering
proactive
adaptive
approach
counter
ever-evolving
nature
fraud.
synthesis
perspectives,
current
applications,
trajectories
comprehensive
understanding
how
is
reshaping
paradigm
domain.
Frontiers in Medicine,
Journal Year:
2025,
Volume and Issue:
11
Published: Jan. 7, 2025
With
the
rising
global
burden
of
chronic
diseases,
traditional
health
management
models
are
encountering
significant
challenges.
The
integration
artificial
intelligence
(AI)
into
disease
has
enhanced
patient
care
efficiency,
optimized
treatment
strategies,
and
reduced
healthcare
costs,
providing
innovative
solutions
in
this
field.
However,
current
research
remains
fragmented
lacks
systematic,
comprehensive
analysis.
This
study
conducts
a
bibliometric
analysis
AI
applications
management,
aiming
to
identify
trends,
highlight
key
areas,
provide
valuable
insights
state
Hoping
our
findings
will
serve
as
useful
reference
for
guiding
further
fostering
effective
application
healthcare.
Web
Science
Core
Collection
database
was
utilized
source.
All
relevant
publications
from
inception
August
2024
were
retrieved.
external
characteristics
summarized
using
HistCite.
Keyword
co-occurrences
among
countries,
authors,
institutions
analyzed
with
Vosviewer,
while
CiteSpace
employed
assess
keyword
frequencies
trends.
A
total
341
retrieved,
originating
775
across
55
published
175
journals
by
2,128
authors.
notable
surge
occurred
between
2013
2024,
accounting
95.31%
(325/341)
output.
United
States
Journal
Medical
Internet
Research
leading
contributors
Our
revealed
four
primary
clusters:
diagnosis,
care,
telemedicine,
technology.
Recent
trends
indicate
that
mobile
technologies
machine
learning
have
emerged
focal
points
field
management.
Despite
advancements
several
critical
challenges
persist.
These
include
improving
quality,
greater
international
inter-institutional
collaboration,
standardizing
data-sharing
practices,
addressing
ethical
legal
concerns.
Future
should
prioritize
strengthening
partnerships
facilitate
cross-disciplinary
cross-regional
knowledge
exchange,
optimizing
more
precise
ensuring
their
seamless
clinical
practice.
Journal of drug targeting,
Journal Year:
2024,
Volume and Issue:
32(10), P. 1247 - 1266
Published: Aug. 19, 2024
Nano-based
drug
delivery
systems
(DDSs)
have
demonstrated
the
ability
to
address
challenges
posed
by
therapeutic
agents,
enhancing
efficiency
and
reducing
side
effects.
Various
nanoparticles
(NPs)
are
utilised
as
DDSs
with
unique
characteristics,
leading
diverse
applications
across
different
diseases.
However,
complexity,
cost
time-consuming
nature
of
laboratory
processes,
large
volume
data,
in
data
analysis
prompted
integration
artificial
intelligence
(AI)
tools.
AI
has
been
employed
designing,
characterising
manufacturing
nanosystems,
well
predicting
treatment
efficiency.
AI's
potential
personalise
based
on
individual
patient
factors,
optimise
formulation
design
predict
properties
highlighted.
By
leveraging
datasets,
developing
safe
effective
can
be
accelerated,
ultimately
improving
outcomes
advancing
pharmaceutical
sciences.
This
review
article
investigates
role
development
nano-DDSs,
a
focus
their
applications.
The
use
revolutionise
optimisation
improve
care.
Microorganisms,
Journal Year:
2024,
Volume and Issue:
12(6), P. 1051 - 1051
Published: May 23, 2024
Traditional
microbial
diagnostic
methods
face
many
obstacles
such
as
sample
handling,
culture
difficulties,
misidentification,
and
delays
in
determining
susceptibility.
The
advent
of
artificial
intelligence
(AI)
has
markedly
transformed
diagnostics
with
rapid
precise
analyses.
Nonetheless,
ethical
considerations
accompany
AI
adoption,
necessitating
measures
to
uphold
patient
privacy,
mitigate
biases,
ensure
data
integrity.
This
review
examines
conventional
hurdles,
stressing
the
significance
standardized
procedures
processing.
It
underscores
AI’s
significant
impact,
particularly
through
machine
learning
(ML),
diagnostics.
Recent
progressions
AI,
ML
methodologies,
are
explored,
showcasing
their
influence
on
categorization,
comprehension
microorganism
interactions,
augmentation
microscopy
capabilities.
furnishes
a
comprehensive
evaluation
utility
diagnostics,
addressing
both
advantages
challenges.
A
few
case
studies
including
SARS-CoV-2,
malaria,
mycobacteria
serve
illustrate
potential
for
swift
diagnosis.
Utilization
convolutional
neural
networks
(CNNs)
digital
pathology,
automated
bacterial
classification,
colony
counting
further
versatility.
Additionally,
improves
antimicrobial
susceptibility
assessment
contributes
disease
surveillance,
outbreak
forecasting,
real-time
monitoring.
Despite
limitations,
integration
microbiology
presents
robust
solutions,
user-friendly
algorithms,
training,
promising
paradigm-shifting
advancements
healthcare.
Journal of Personalized Medicine,
Journal Year:
2024,
Volume and Issue:
14(4), P. 354 - 354
Published: March 28, 2024
The
integration
of
Artificial
Intelligence
(AI)
into
healthcare
has
the
potential
to
revolutionize
medical
diagnostics,
particularly
in
specialized
fields
such
as
Ear,
Nose,
and
Throat
(ENT)
medicine.
However,
successful
adoption
AI-assisted
diagnostic
tools
ENT
practice
depends
on
understanding
various
factors;
these
include
influences
their
effectiveness
acceptance
among
professionals.
This
cross-sectional
study
aimed
assess
usability
AI
practice,
determine
clinical
impact
accuracy
diagnostics
ENT,
measure
trust
confidence
professionals
tools,
gauge
overall
satisfaction
outlook
future
identify
challenges,
limitations,
areas
for
improvement
diagnostics.
A
structured
online
questionnaire
was
distributed
600
certified
with
at
least
one
year
experience
field.
assessed
participants’
familiarity
usability,
impact,
trust,
satisfaction,
identified
challenges.
total
458
respondents
completed
questionnaire,
resulting
a
response
rate
91.7%.
majority
reported
(60.7%)
perceived
them
generally
usable
clinically
impactful.
challenges
existing
systems,
user-friendliness,
accuracy,
cost
were
identified.
Trust
levels
varied
participants,
concerns
regarding
data
privacy
support.
Geographic
setting
differences
influenced
perceptions
experiences.
highlights
diverse
experiences
While
there
is
general
enthusiasm
related
integration,
need
be
addressed
widespread
adoption.
These
findings
provide
valuable
insights
developers,
policymakers,
providers
aiming
enhance
role
practice.
