Deleted Journal,
Journal Year:
2024,
Volume and Issue:
3
Published: Dec. 30, 2024
Dentistry
is
one
of
the
youngest
medical
applications
artificial
intelligence.
Here,
intelligence
and
its
various
components
(machine
learning,
deep
neural
networks)
are
applied
at
a
number
stages,
such
as
diagnosis,
decision-making,
treatment
planning,
prediction.Dental
radiology,
maxillofacial
surgery,
orthopedic
dentistry
some
application
areas
in
dentistry.
Despite
advantages,
there
problems
(security,
legal
ethical
during
etc.).
Their
solution
also
related
to
development
application.
This
updates
improves
future
directions.In
article,
application,
management,
problems,
directions
mentioned.Keywords:
Dentistry,
Artificial
Intelligence,
Machine
Learning,
Advantages,
Challenges.
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(3), P. 273 - 273
Published: Jan. 24, 2025
The
adoption
of
automated
machine
learning
(AutoML)
in
dentistry
is
transforming
clinical
practices
by
enabling
clinicians
to
harness
(ML)
models
without
requiring
extensive
technical
expertise.
This
narrative
review
aims
explore
the
impact
autoML
dental
applications.
A
comprehensive
search
PubMed,
Scopus,
and
Google
Scholar
was
conducted
time
language
restrictions.
Inclusion
criteria
focused
on
studies
evaluating
applications
performance
for
tasks.
Exclusion
included
non-dental
studies,
single-case
reports,
conference
abstracts.
highlights
multiple
promising
dentistry.
Diagnostic
tasks
showed
high
accuracy,
such
as
95.4%
precision
implant
classification
92%
accuracy
paranasal
sinus
disease
detection.
Predictive
also
demonstrated
promise,
including
84%
ICU
admissions
due
infections
93.9%
orthodontic
extraction
predictions.
AutoML
frameworks
like
Vertex
AI
H2O
emerged
key
tools
these
shows
great
promise
facilitating
data-driven
decision-making
improving
patient
care
quality
through
accessible,
solutions.
Future
advancements
should
focus
enhancing
model
interpretability,
developing
large
annotated
datasets,
creating
pipelines
tailored
Educating
integrating
domain-specific
knowledge
into
platforms
could
further
bridge
gap
between
complex
ML
technology
practical
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(2), P. 231 - 231
Published: Jan. 20, 2025
Background:
Oral
diseases
such
as
caries,
gingivitis,
and
periodontitis
are
highly
prevalent
worldwide
often
arise
from
plaque.
This
study
focuses
on
detecting
three
plaque
stages—new,
mature,
over-mature—using
state-of-the-art
YOLO
architectures
to
enhance
early
intervention
reduce
reliance
manual
visual
assessments.
Methods:
We
compiled
a
dataset
of
531
RGB
images
177
individuals,
captured
via
multiple
mobile
devices.
Each
sample
was
treated
with
disclosing
gel
highlight
types,
then
preprocessed
for
lighting
color
normalization.
YOLOv9,
YOLOv10,
YOLOv11,
in
various
scales,
were
trained
detect
categories,
their
performance
evaluated
using
precision,
recall,
mean
Average
Precision
(mAP@50).
Results:
Among
the
tested
models,
YOLOv11m
achieved
highest
mAP@50
(0.713),
displaying
superior
detection
over-mature
Across
all
variants,
older
generally
easier
than
newer
plaque,
which
can
blend
gingival
tissue.
Applying
O’Leary
index
indicated
that
over
half
population
exhibited
severe
levels.
Conclusions:
Our
findings
demonstrate
feasibility
automated
advanced
models
varied
imaging
conditions.
approach
offers
potential
optimize
clinical
workflows,
support
diagnoses,
mitigate
oral
health
burdens
low-resource
communities.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(1), P. e0316635 - e0316635
Published: Jan. 2, 2025
Background
and
purpose
The
most
widely
used
social
media
platform
for
video
content
is
YouTube
TM
.
present
study
evaluated
the
quality
of
information
on
artificial
intelligence
(AI)
in
dentistry.
Methods
This
cross-sectional
(
https://www.youtube.com
)
searching
videos.
terms
search
were
"artificial
dentistry,"
"machine
learning
dental
care,"
"deep
dentistry."
accuracy
reliability
source
assessed
using
DISCERN
score.
videos
was
modified
Global
Quality
Score
(mGQS)
Journal
American
Medical
Association
(JAMA)
Results
analysis
91
YouTube™
AI
dentistry
revealed
insights
into
characteristics,
content,
quality.
On
average,
22.45
minutes
received
1715.58
views
23.79
likes.
topics
mainly
centered
general
(66%),
with
radiology
(18%),
orthodontics
(9%),
prosthodontics
(4%),
implants
(3%).
mGQS
scores
higher
uploaded
by
healthcare
professionals
educational
videos(P<0.05).
exhibited
a
strong
correlation
(0.75)
JAMA
(0.77).
video’s
mGQS,
0.66
indicated
moderate
correlation.
Conclusion
has
informative
moderately
reliable
Dental
students,
dentists
patients
can
use
these
to
learn
educate
about
Professionals
should
upload
more
enhance
content.
Cureus,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 7, 2025
Integrating
the
artificial
intelligence
(AI)
cloud
into
dental
clinics
can
enhance
diagnostics,
streamline
operations,
and
improve
patient
care.
This
article
explores
adoption
of
AI-powered
solutions
in
clinics,
focusing
on
infrastructure
requirements,
software
licensing,
staff
training,
system
optimization,
challenges
faced
during
implementation.
It
provides
a
detailed
guide
for
practices
to
transition
AI
systems.
We
reviewed
existing
literature,
technological
guidelines,
practical
implementation
strategies
integrating
practices.
The
methodology
includes
step-by-step
approach
understanding
clinic
needs,
selecting
appropriate
software,
training
staff,
ensuring
optimization
maintenance.
drastically
clinical
outcomes
operational
efficiency.
Despite
challenges,
proper
planning,
investment,
continuous
ensure
smooth
maximize
benefits
technologies
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 197 - 216
Published: March 7, 2025
This
chapter
examines
how
artificial
intelligence
is
revolutionizing
healthcare
by
improving
patient
autonomy
and
engagement.
In
order
to
empower
patients
take
charge
of
their
health
information
make
educated
decisions
about
care
it
looks
at
AI-driven
digital
tools
can
support
personalized
data
management.
The
focuses
on
important
ethical
issues
such
as
informed
consent
privacy
the
requirement
for
AI
algorithms
be
transparent
making
sure
that
patients'
rights
are
given
top
priority
when
these
technologies
implemented.
It
also
discusses
difficulties
in
incorporating
into
current
systems
highlighting
significance
stakeholder
cooperation
between
legislators'
technology
developers
providers.
uses
case
studies
demonstrate
successfully
implemented
improve
empowerment
outcomes.
Medicina,
Journal Year:
2025,
Volume and Issue:
61(4), P. 572 - 572
Published: March 23, 2025
Artificial
intelligence
(AI)
is
increasingly
used
in
healthcare,
including
dental
and
periodontal
diagnostics,
due
to
its
ability
analyze
complex
datasets
with
speed
precision.
Backgrounds
Objectives:
This
study
aimed
evaluate
the
reliability
of
AI-assisted
dental–periodontal
diagnoses
compared
made
by
senior
specialists,
general
dentists.
Material
Methods:
A
comparative
was
conducted
involving
60
practitioners
divided
into
three
groups—general
dentists,
specialists—along
an
AI
diagnostic
system
(Planmeca
Romexis
6.4.7.software).
Participants
evaluated
six
high-quality
panoramic
radiographic
images
representing
various
conditions.
Diagnoses
were
against
a
reference
“gold
standard”
validated
imaging
expert
clinician.
statistical
analysis
performed
using
SPSS
26.0,
applying
chi-square
tests,
ANOVA,
Bonferroni
correction
ensure
robust
results.
Results:
AI’s
consistency
identifying
subtle
conditions
comparable
that
while
dentists
showed
greater
variability
their
evaluations.
The
key
findings
revealed
specialists
consistently
demonstrated
highest
performance
detecting
attachment
loss
alveolar
bone
loss,
achieving
mean
score
6.12
teeth
5.43
for
4.58
3.65
ANOVA
highlighted
statistically
significant
differences
between
groups,
particularly
detection
on
maxillary
arch
(F
=
3.820,
p
0.014).
Additionally,
high
specialists.
Conclusions:
systems
exhibit
potential
as
reliable
tools
assessment,
complementing
expertise
human
practitioners.
However,
further
validation
clinical
settings
necessary
address
limitations
such
algorithmic
bias
atypical
cases.
integration
dentistry
can
enhance
precision
patient
outcomes
reducing
assessments.
Oral,
Journal Year:
2025,
Volume and Issue:
5(2), P. 26 - 26
Published: April 9, 2025
Introduction:
Salivary
biomarkers
have
been
extensively
studied
in
relation
to
oral
disease,
such
as
periodontal
cancer,
and
dental
caries,
well
systemic
conditions
including
diabetes,
cardiovascular
diseases,
neurological
disorders.
Literature
Review:
A
systematic
literature
review
was
conducted,
analyzing
recent
advancements
salivary
biomarker
research.
Databases
PubMed,
Scopus,
Web
of
Science
were
searched
for
relevant
studies
published
the
last
decade.
The
selection
criteria
included
focusing
on
identification,
validation,
clinical
application
diagnosing
diseases.
Various
detection
techniques,
enzyme-linked
immunosorbent
assay
(ELISA),
polymerase
chain
reaction
(PCR),
mass
spectrometry,
biosensor
technologies,
reviewed
assess
their
effectiveness
analysis.
Specific
biomarkers,
inflammatory
cytokines,
oxidative
stress
markers,
microRNAs,
identified
reliable
indicators
disease
progression.
Current
Trends
Future
Perspectives:
Advances
proteomics,
genomics,
metabolomics
significantly
enhanced
ability
analyze
with
high
sensitivity
specificity.
Despite
promising
findings,
challenges
remain
standardizing
sample
collection,
processing,
analysis
ensure
reproducibility
applicability.
Conclusions:
research
should
focus
developing
point-of-care
diagnostic
tools
integrating
artificial
intelligence
improve
predictive
accuracy
biomarkers.
