are vaguely based on biological neural
networks, in which a collection of
interconnected nodes processes the
data like how neurons communicate
in a human brain. The potential of
NN has been multiplied manifold,
thanks to the advent of Deep Learning
which is an evolved form of NN, it uses
multiple hidden layers that can be used
to process complex multidimensional
data like a human brain. A huge huge volumes of data is a tough task
for humans, but that’s what Shinjini
Kundu, a physician at the University
of Pittsburgh Medical Center has
been doing. Her AI algorithms
examine images like MRI scans for
subtle differences which may not be
perceptible to the human eye, and she
has employed this to study osteoarthritis
and to predict its development way
before it’s diagnosis with a whopping
information and make it available for
doctors to make smart decisions about
their patients. Neural Networks, on the other hand,
form another major chunk of AI
algorithm in healthcare. NN algorithms Some areas where artificial intelligence
surpasses humans is in looking
for patterns in data and in making
predictions about that data. Processing
thousands of images and looking for
a subtle discernible pattern within
portion of NN algorithms is used for
diagnostic imaging. Early last year, a
study published in Nature used CNN,
a type of deep learning NN algorithm
to identify skin cancer from clinical
images. The algorithm which was
trained on 29,450 clinical images, was
highly specific and sensitive to detection
and was on par with the performance of
an expert dermatologist with over 90%
accuracy. A 2016 study used a variant of
deep learning NN to identify interstitial
lung disease using CT scan images
with 85.5% accuracy. Google’s artificial
intelligence team employed deep
learning algorithms to study pictures
of the back of the eye, for the detection
of diabetic retinopathy, a blinding
disorder in diabetic patients. Their
results showed above 90% accuracy
in both sensitivity and specificity of
detection, which is at par with a skilled
ophthalmologist.
from electrography of the heart, brain,
and other body parts. Machine learning
plays a major role owing to its ability to
‘learn’ and make predictions from data
without explicit programming. Of the
many machine learning algorithms,
two such algorithms have been used
extensively in both research and
healthcare, namely Support Vector
Machine (SVM) and Neural Networks
(NN), both use supervised learning
models. SVM, in particular, has been
useful in tasks involving classification
and for novelty detection. For example,
a 2012 study used SVM to identify
imaging biomarkers of neurological
and psychiatric disease. SVM has been
used as prediction models for diabetic
and prediabetic patients. In 2010, a
research group from Korea applied
SVM to make predictions about heart
failure patients and their adherence rate
to their medication. Two researchers
from Australia used SVM for diagnosis
of cerebral palsy gait with an accuracy
rate of 96.8%. Language Processing (NLP), a form of
AI which identifies key information
from spoken or written human input,
such as physical examination records,
handwritten lab notes, discharge
summaries etc. The promise of NLP lies
in its ability to turn this big data into
smart data. It can be applied to mine
big blocks of clinical data and convert
that into organized curated easy-for-
retrieval information, which can make
documentation of clinical information
more manageable. In 2014, IBM’s
Watson collaborated with Epic Systems
and Carilion Clinic to analyze massive
21 million records in just six weeks
and pulled important information
about risk factors and other features
from examination notes written by
physicians and clinical laboratory
results into organized EHR templates,
and further used predictive modeling
to identify patients at risk to congestive
heart failure with an assuring 85%
accuracy rate. Similar efforts of using
NLP to tackle cancer and genomics
datasets are in process. NLP algorithms
thus can be employed with much
effectiveness to unlock healthcare’s big
data crisis to extract clinically relevant
Volume 4 | Issue 1 | January-March 2019
13
CAN AI REPLACE A DOCTOR?
Another facet of healthcare where
artificial intelligence can find use is
analyzing structured data namely
genetic data, imaging data from
X-ray scans, CT scans, MRIs, etc. and
electrophysiological data obtained