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 what 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 healthcare.
Thank you very much.
(Applause)