Biomolecules and Biomedicine,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 14, 2025
Artificial
intelligence
(AI)
has
become
a
powerful
tool
in
biochemistry,
greatly
enhancing
research
capabilities
by
enabling
the
analysis
of
complex
datasets,
predicting
molecular
interactions,
and
accelerating
drug
discovery.
As
AI
continues
to
evolve,
its
applications
biochemistry
are
poised
expand,
revolutionizing
both
theoretical
applied
research.
This
review
explores
current
potential
with
focus
on
data
analysis,
modeling,
enzyme
engineering,
metabolic
pathway
studies.
Key
techniques—such
as
machine
learning
algorithms,
natural
language
processing,
AI-based
modeling—are
discussed.
The
also
highlights
emerging
areas
benefiting
from
AI,
including
personalized
medicine
synthetic
biology.
methodology
involves
an
extensive
existing
literature,
particularly
peer-reviewed
studies
biochemistry.
AI-driven
tools
like
AlphaFold,
which
have
significantly
advanced
protein
structure
prediction,
evaluated
alongside
AI’s
role
expediting
addresses
challenges
such
quality,
model
interpretability,
ethical
considerations.
Results
indicate
that
expanded
scope
biochemical
facilitating
large-scale
simulations,
opening
new
avenues
inquiry.
However,
remain,
handling
concerns.
In
conclusion,
is
transforming
driving
innovation
expanding
possibilities.
Future
advancements
interdisciplinary
collaboration,
integration
automated
techniques
will
be
crucial
fully
unlocking
advancing
PLOS Digital Health,
Journal Year:
2024,
Volume and Issue:
3(11), P. e0000651 - e0000651
Published: Nov. 7, 2024
Biases
in
medical
artificial
intelligence
(AI)
arise
and
compound
throughout
the
AI
lifecycle.
These
biases
can
have
significant
clinical
consequences,
especially
applications
that
involve
decision-making.
Left
unaddressed,
biased
lead
to
substandard
decisions
perpetuation
exacerbation
of
longstanding
healthcare
disparities.
We
discuss
potential
at
different
stages
development
pipeline
how
they
affect
algorithms
Bias
occur
data
features
labels,
model
evaluation,
deployment,
publication.
Insufficient
sample
sizes
for
certain
patient
groups
result
suboptimal
performance,
algorithm
underestimation,
clinically
unmeaningful
predictions.
Missing
findings
also
produce
behavior,
including
capturable
but
nonrandomly
missing
data,
such
as
diagnosis
codes,
is
not
usually
or
easily
captured,
social
determinants
health.
Expertly
annotated
labels
used
train
supervised
learning
models
may
reflect
implicit
cognitive
care
practices.
Overreliance
on
performance
metrics
during
obscure
bias
diminish
a
model's
utility.
When
applied
outside
training
cohort,
deteriorate
from
previous
validation
do
so
differentially
across
subgroups.
How
end
users
interact
with
deployed
solutions
introduce
bias.
Finally,
where
are
developed
published,
by
whom,
impacts
trajectories
priorities
future
development.
Solutions
mitigate
must
be
implemented
care,
which
include
collection
large
diverse
sets,
statistical
debiasing
methods,
thorough
emphasis
interpretability,
standardized
reporting
transparency
requirements.
Prior
real-world
implementation
settings,
rigorous
through
trials
critical
demonstrate
unbiased
application.
Addressing
crucial
ensuring
all
patients
benefit
equitably
AI.
BMC Oral Health,
Journal Year:
2024,
Volume and Issue:
24(1)
Published: May 13, 2024
Abstract
Background
Dental
resin-based
composites
are
widely
recognized
for
their
aesthetic
appeal
and
adhesive
properties,
which
make
them
integral
to
modern
restorative
dentistry.
Despite
advantages,
adhesion
biomechanical
performance
challenges
persist,
necessitating
innovative
strategies
improvement.
This
study
addressed
the
associated
with
properties
in
dental
by
employing
molecular
docking
dynamics
simulation.
Methods
Molecular
assesses
binding
energies
provides
valuable
insights
into
interactions
between
monomers,
fillers,
coupling
agents.
investigation
prioritizes
SiO
2
TRIS,
considering
consistent
influence.
simulations,
executed
Forcite
module
COMPASS
II
force
field,
extend
analysis
mechanical
of
composite
complexes.
The
simulations
encompassed
energy
minimization,
controlled
NVT
NPT
ensemble
equilibration
stages.
Notably,
spanned
a
duration
50
ns.
Results
TRIS
consistently
emerged
as
influential
components,
showcasing
versatility
promoting
solid
interactions.
A
correlation
matrix
underscores
significant
roles
van
der
Waals
desolvation
determining
overall
energy.
provide
in-depth
HEMA-SiO
-TRIS
excelled
stiffness,
BisGMA-SiO
prevailed
terms
flexural
strength,
EBPADMA-SiO
offered
balanced
combination
properties.
Conclusion
These
findings
optimizing
tailored
diverse
clinical
requirements.
While
demonstrates
distinct
strengths,
this
emphasizes
need
further
research.
Future
investigations
should
validate
computational
experimentally
assess
material's
response
dynamic
environmental
factors.
Modern Pathology,
Journal Year:
2025,
Volume and Issue:
38(4), P. 100705 - 100705
Published: Jan. 5, 2025
Artificial
intelligence
(AI)
and
machine
learning
(ML)
are
transforming
the
field
of
medicine.
Health
care
organizations
now
starting
to
establish
management
strategies
for
integrating
such
platforms
(AI-ML
toolsets)
that
leverage
computational
power
advanced
algorithms
analyze
data
provide
better
insights
ultimately
translate
enhanced
clinical
decision-making
improved
patient
outcomes.
Emerging
AI-ML
trends
in
pathology
medicine
reshaping
by
offering
innovative
solutions
enhance
diagnostic
accuracy,
operational
workflows,
decision
support,
These
tools
also
increasingly
valuable
research
which
they
contribute
automated
image
analysis,
biomarker
discovery,
drug
development,
trials,
productive
analytics.
Other
related
include
adoption
ML
operations
managing
models
settings,
application
multimodal
multiagent
AI
utilize
diverse
sources,
expedited
translational
research,
virtualized
education
training
simulation.
As
final
chapter
our
educational
series,
this
review
article
delves
into
current
adoption,
future
directions,
transformative
potential
medicine,
discussing
their
applications,
benefits,
challenges,
perspectives.
Journal of Global Health,
Journal Year:
2025,
Volume and Issue:
15
Published: Jan. 10, 2025
The
emergence
of
artificial
intelligence
(AI)
in
drug
discovery
represents
a
transformative
development
addressing
neglected
diseases,
particularly
the
context
developing
world.
Neglected
often
overlooked
by
traditional
pharmaceutical
research
due
to
limited
commercial
profitability,
pose
significant
public
health
challenges
low-
and
middle-income
countries.
AI-powered
offers
promising
solution
accelerating
identification
potential
candidates,
optimising
process,
reducing
time
cost
associated
with
bringing
new
treatments
market.
However,
while
AI
shows
promise,
many
its
applications
are
still
their
early
stages
require
human
validation
ensure
accuracy
reliability
predictions.
Additionally,
models
availability
high-quality
data,
which
is
sparse
regions
where
diseases
most
prevalent.
This
viewpoint
explores
application
for
examining
current
impact,
related
ethical
considerations,
broader
implications
It
also
highlights
opportunities
presented
this
context,
emphasising
need
ongoing
research,
oversight,
collaboration
between
stakeholders
fully
realise
transforming
global
outcomes.
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.
