JMIR AI,
Journal Year:
2023,
Volume and Issue:
2, P. e44191 - e44191
Published: May 22, 2023
Background
Aspirin-exacerbated
respiratory
disease
(AERD)
is
an
acquired
inflammatory
condition
characterized
by
the
presence
of
asthma,
chronic
rhinosinusitis
with
nasal
polyposis,
and
hypersensitivity
reactions
on
ingestion
aspirin
or
other
nonsteroidal
anti-inflammatory
drugs
(NSAIDs).
Despite
AERD
having
a
classic
constellation
symptoms,
diagnosis
often
overlooked,
average
greater
than
10
years
between
onset
symptoms
AERD.
Without
diagnosis,
individuals
will
lack
opportunities
to
receive
effective
treatments,
such
as
desensitization
biologic
medications.
Objective
Our
aim
was
develop
combined
algorithm
that
integrates
both
natural
language
processing
(NLP)
machine
learning
(ML)
techniques
identify
patients
from
electronic
health
record
(EHR).
Methods
A
rule-based
decision
tree
incorporating
NLP-based
features
developed
using
clinical
documents
EHR
at
Mayo
Clinic.
From
notes,
NLP
techniques,
7
were
extracted
included
following:
AERD,
NSAID
allergy,
polyps,
sinusitis,
elevated
urine
leukotriene
E4
level,
documented
no-NSAID
allergy.
MedTagger
used
extract
these
unstructured
text
given
set
keywords
patterns
based
chart
review
2
allergy
immunology
experts
for
The
status
each
feature
quantified
assigning
frequency
its
occurrence
in
per
subject.
We
optimized
classifier’s
hyperparameters
cutoff
threshold
training
determine
representative
combination
discriminate
then
evaluated
resulting
model
test
set.
Results
algorithm,
which
combines
ML
achieved
area
under
receiver
operating
characteristic
curve
score,
sensitivity,
specificity
0.86
(95%
CI
0.78-0.94),
80.00
70.82-87.33),
88.00
79.98-93.64)
set,
respectively.
Conclusions
promising
needs
further
refinement
improve
diagnosis.
Continued
development
technologies
has
potential
reduce
diagnostic
delays
our
patients.
Diagnostics,
Journal Year:
2023,
Volume and Issue:
13(12), P. 1995 - 1995
Published: June 7, 2023
Artificial
intelligence
(AI)
plays
a
more
and
important
role
in
our
everyday
life
due
to
the
advantages
that
it
brings
when
used,
such
as
24/7
availability,
very
low
percentage
of
errors,
ability
provide
real
time
insights,
or
performing
fast
analysis.
AI
is
increasingly
being
used
clinical
medical
dental
healthcare
analyses,
with
valuable
applications,
which
include
disease
diagnosis,
risk
assessment,
treatment
planning,
drug
discovery.
This
paper
presents
narrative
literature
review
use
from
multi-disciplinary
perspective,
specifically
cardiology,
allergology,
endocrinology,
fields.
The
highlights
data
recent
research
development
efforts
for
healthcare,
well
challenges
limitations
associated
implementation,
privacy
security
considerations,
along
ethical
legal
concerns.
regulation
responsible
design,
development,
still
early
stages
rapid
evolution
field.
However,
duty
carefully
consider
implications
implementing
respond
appropriately.
With
potential
reshape
delivery
enhance
patient
outcomes,
systems
continue
reveal
their
capabilities.
Informatics and Health,
Journal Year:
2024,
Volume and Issue:
1(2), P. 123 - 148
Published: June 13, 2024
Health
informatics
is
a
fast-growing
area
in
the
healthcare
sector.
It
concerns
technologies,
tools,
equipment,
and
procedures
required
to
gather,
store,
retrieve,
use
health
data
medical
data.
Healthcare
provides
patients,
nurses,
hospital
administrators,
physicians,
insurance
providers,
other
stakeholders
with
electronic
access
records
through
information
technologies
(HIT).
combines
nursing
science
analytical
disciplines
handle,
interpret,
convey
data,
bringing
together
specialists
making
accessible
meaningful.
This
research
an
outcome
of
extensive
scopic
review,
which
has
been
conducted
by
identifying
development
search
keywords
such
as
"Health
informatics,"
"Technologies,"
"Healthcare"
from
databases
Scopus,
PubMed,
Google
Scholar,
ResearchGate,
platforms.
Further,
most
relevant
papers
are
identified
studied.
paper
explores
informatics,
its
their
need
present
domain.
also
identifies
vital
aspects,
characteristics,
versatile
contributions
discusses
significant
applications
field.
Patients'
can
be
effectively
analysed
individually
or
groups
using
meet
diverse
requirements.
Effective
improves
practice
management
quickly
shared
among
professionals,
patients
stakeholders.
specialists'
knowledge
utilising
assist
choice-making
creating
best
practices.
enables
organisations
identify
specific
offering
appropriate
for
given
therapy,
procedure,
training.
Informatics
addresses
issues
at
macro
level
organisation
personal
patient
care
via
innovative
Bioinformation,
Journal Year:
2024,
Volume and Issue:
20(1), P. 29 - 35
Published: Jan. 31, 2024
Rapid
advancements
in
the
field
of
artificial
intelligence
(AI)
have
opened
up
unprecedented
opportunities
to
revolutionize
various
scientific
domains,
including
immunology
and
genetics.
Therefore,
it
is
interest
explore
emerging
applications
AI
genetics,
with
objective
enhancing
our
understanding
dynamic
intricacies
immune
system,
disease
etiology,
genetic
variations.
Hence,
use
methodologies
immunological
datasets,
thereby
facilitating
development
innovative
approaches
realms
diagnosis,
treatment,
personalized
medicine
reviewed.
Journal of Allergy and Clinical Immunology Global,
Journal Year:
2024,
Volume and Issue:
3(2), P. 100230 - 100230
Published: Feb. 19, 2024
Access
to
the
molecular
culprits
of
allergic
reactions
allows
for
leveraging
allergology
as
a
new
precision
medicine
approach-one
built
on
interdisciplinary,
basic,
and
clinical
knowledge.
Molecular
relies
use
allergen
molecules
Bioengineering,
Journal Year:
2025,
Volume and Issue:
12(2), P. 180 - 180
Published: Feb. 13, 2025
Artificial
Intelligence
(AI)
is
reshaping
healthcare
through
advancements
in
clinical
decision
support
and
diagnostic
capabilities.
While
human
expertise
remains
foundational
to
medical
practice,
AI-powered
tools
are
increasingly
matching
or
exceeding
specialist-level
performance
across
multiple
domains,
paving
the
way
for
a
new
era
of
democratized
access.
These
systems
promise
reduce
disparities
care
delivery
demographic,
racial,
socioeconomic
boundaries
by
providing
high-quality
at
scale.
As
result,
advanced
services
can
be
affordable
all
populations,
irrespective
demographics,
race,
background.
The
democratization
such
AI
cost
care,
optimize
resource
allocation,
improve
quality
care.
In
contrast
humans,
potentially
uncover
complex
relationships
data
from
large
set
inputs
generate
evidence-based
knowledge
medicine.
However,
integrating
into
raises
several
ethical
philosophical
concerns,
as
bias,
transparency,
autonomy,
responsibility,
accountability.
this
study,
we
examine
recent
advances
AI-enabled
image
analysis,
current
regulatory
frameworks,
emerging
best
practices
integration.
We
analyze
both
technical
challenges
inherent
deploying
institutions,
with
particular
attention
privacy,
algorithmic
fairness,
system
transparency.
Furthermore,
propose
practical
solutions
address
key
challenges,
including
scarcity,
racial
bias
training
datasets,
limited
model
interpretability,
systematic
biases.
Finally,
outline
conceptual
algorithm
responsible
implementations
identify
promising
future
research
development
directions.
Allergy,
Journal Year:
2023,
Volume and Issue:
78(10), P. 2623 - 2643
Published: Aug. 16, 2023
Abstract
The
field
of
medicine
is
witnessing
an
exponential
growth
interest
in
artificial
intelligence
(AI),
which
enables
new
research
questions
and
the
analysis
larger
types
data.
Nevertheless,
applications
that
go
beyond
proof
concepts
deliver
clinical
value
remain
rare,
especially
allergy.
This
narrative
review
provides
a
fundamental
understanding
core
AI
critically
discusses
its
limitations
open
challenges,
such
as
data
availability
bias,
along
with
potential
directions
to
surmount
them.
We
provide
conceptual
framework
structure
within
this
discuss
forefront
case
examples.
Most
these
machine
learning
allergy
concern
supervised
unsupervised
clustering,
strong
emphasis
on
diagnosis
subtyping.
A
perspective
shared
guidelines
for
good
practice
guide
readers
applying
it
effectively
safely,
prospects
advancement
initiatives
increase
impact.
anticipate
can
further
deepen
our
knowledge
disease
mechanisms
contribute
precision
Life,
Journal Year:
2024,
Volume and Issue:
14(4), P. 516 - 516
Published: April 16, 2024
Immuno-correlated
dermatological
pathologies
refer
to
skin
disorders
that
are
closely
associated
with
immune
system
dysfunction
or
abnormal
responses.
Advancements
in
the
field
of
artificial
intelligence
(AI)
have
shown
promise
enhancing
diagnosis,
management,
and
assessment
immuno-correlated
pathologies.
This
intersection
dermatology
immunology
plays
a
pivotal
role
comprehending
addressing
complex
involvement.
The
paper
explores
knowledge
known
so
far
evolution
achievements
AI
diagnosis;
discusses
segmentation
classification
medical
images;
reviews
existing
challenges,
immunological-related
diseases.
From
our
review,
has
emerged,
especially
analysis
images
for
both
diagnostic
severity
purposes.
Furthermore,
possibility
predicting
patients’
response
therapies
is
emerging,
order
create
tailored
therapies.
