How AI is making it easier to diagnose disease Pratik Shah
Computer algorithms today
are performing incredible tasks
with high accuracies, at a massive scale,
using human-like intelligence.
And this intelligence of computers
is often referred to as AI
or artificial intelligence.
AI is poised to make an incredible impact
on our lives in the future.
Today, however,
we still face massive challenges
in detecting and diagnosing
several life-threatening illnesses,
such as infectious diseases and cancer.
Thousands of patients every year
lose their lives
due to liver and oral cancer.
Our best way to help these patients
is to perform early detection
and diagnoses of these diseases.
So how do we detect these diseases today,
and can artificial intelligence help?
In patients who, unfortunately,
are suspected of these diseases,
an expert physician first orders
very expensive
medical imaging technologies
such as fluorescent imaging,
CTs, MRIs, to be performed.
Once those images are collected,
another expert physician then diagnoses
those images and talks to the patient.
As you can see, this is
a very resource-intensive process,
requiring both expert physicians,
expensive medical imaging technologies,
and is not considered practical
for the developing world.
And in fact, in many
industrialized nations, as well.
So, can we solve this problem
using artificial intelligence?
Today, if I were to use traditional
artificial intelligence architectures
to solve this problem,
I would require 10,000 –
I repeat, on an order of 10,000
of these very expensive medical images
first to be generated.
After that, I would then go
to an expert physician,
who would then analyze
those images for me.
And using those two pieces of information,
I can train a standard deep neural network
or a deep learning network
to provide patient’s diagnosis.
Similar to the first approach,
traditional artificial
intelligence approaches
suffer from the same problem.
Large amounts of data, expert physicians
and expert medical imaging technologies.
So, can we invent more scalable, effective
and more valuable artificial
intelligence architectures
to solve these very important
problems facing us today?
And this is exactly
what my group at MIT Media Lab does.
We have invented a variety
of unorthodox AI architectures
to solve some of the most important
challenges facing us today
in medical imaging and clinical trials.
In the example I shared
with you today, we had two goals.
Our first goal was to reduce
the number of images
required to train
artificial intelligence algorithms.
Our second goal – we were more ambitious,
we wanted to reduce the use
of expensive medical imaging technologies
to screen patients.
So how did we do it?
For our first goal,
instead of starting
with tens and thousands
of these very expensive medical images,
like traditional AI,
we started with a single medical image.
From this image, my team and I
figured out a very clever way
to extract billions
of information packets.
These information packets
included colors, pixels, geometry
and rendering of the disease
on the medical image.
In a sense, we converted one image
into billions of training data points,
massively reducing the amount of data
needed for training.
For our second goal,
to reduce the use of expensive medical
imaging technologies to screen patients,
we started with a standard,
white light photograph,
acquired either from a DSLR camera
or a mobile phone, for the patient.
Then remember those
billions of information packets?
We overlaid those from
the medical image onto this image,
creating something
that we call a composite image.
Much to our surprise,
we only required 50 –
I repeat, only 50 –
of these composite images to train
our algorithms to high efficiencies.
To summarize our approach,
instead of using 10,000
very expensive medical images,
we can now train the AI algorithms
in an unorthodox way,
using only 50 of these high-resolution,
but standard photographs,
acquired from DSLR cameras
and mobile phones,
and provide diagnosis.
More importantly,
our algorithms can accept,
in the future and even right now,
some very simple, white light
photographs from the patient,
instead of expensive
medical imaging technologies.
I believe that we are poised
to enter an era
where artificial intelligence
is going to make an incredible
impact on our future.
And I think that thinking
about traditional AI,
which is data-rich but application-poor,
we should also continue thinking
about unorthodox artificial
intelligence architectures,
which can accept small amounts of data
and solve some of the most important
problems facing us today,
especially in health care.
Thank you very much.
(Applause)