Transcriber: Vivien Wang Reviewer: Hiroko Kawano
Today, I'd like to know how many of you here, at this wonderful, wonderful event
have known someone who's been through
or is currently going through breast cancer?
Yeah, there's a fair share of you.
Well, turns out there are millions of people who share that experience.
And as bad as it is, at least you know,
at least you are definitively aware
that this person has or has had breast cancer.
But what I want to focus on today is the people who don't know,
the people who have had the diagnostic attempts,
the screenings, the symptoms,
but the breast cancer's gone undetected.
Because the scary part is: there is no publicly available data
on the misdiagnosis rates of breast cancer.
There's only estimates.
And those estimates show that over 12% of all breast cancer cases across Europe
can be false negative.
And that's huge.
That's roughly one in eight people who have the symptoms for breast cancer
being told by their doctor that they don't have breast cancer
when, in reality, they actually do.
So personally I got involved in this topic when, in my local newspaper,
where I read about a woman in my city who had died from breast cancer
because it had gone undetected for over a year.
And that was truly shocking to me.
It was my first exposure to the potential consequences of a misdiagnosis.
That was the first time I realised the cost of the mistakes of a doctor.
Basically, once I discovered the issue,
I researched deeper, mostly out of curiosity,
and found out that mistakes like these are especially common
when the patient's doctor doesn't actually specialise in breast cancer.
And that is a big issue.
Because over 4 billion people in the world
lack access to adequate radiology and pathology services,
meaning more than half of the people we live alongside with on our planet
don't have that fundamental diagnostic expertise,
which is so critical to effective rural healthcare delivery.
I found that it's only worsening
because the academia is neglecting it,
both the public and private sectors are ignoring
because it's just not good press.
So, now, at this point,
I really wanted to do something about this.
So I tried to brainstorm,
think of ways to somehow compensate for this inadequacy.
And then it hit me:
why don't we get computers to do it?
Why don't we get computers to be the expert, to be the specialist?
Why don't we leverage the computational intuition
of deep learning and artificial intelligence,
algorithms and models?
Because essentially, deep learning models
allow us to feed them lots and lots of data
from which they learn within the inputs that you specify
and actually detects subtle features and detect patterns between your inputs
within your data,
which are far too complex for even humans to comprehend.
So, in this case,
if we can build models to predict a probability of malignancy
from low cost, diagnostic tests
[where] normal primary care workers can administer,
who don't require any expertise to administer,
we potentially present a very low cost way
to transport the combined medical knowledge across labs here
and bring that directly to the point of care
in the most vulnerable of places.
I mean, not only could that overcome
the intrinsic limitations of human perception and bias,
but we could power clinical optimisation
in a way that radiological and pathological interpretation
is done with prognostic-based strategies and outcomes at the forefront.
So, yeah, that's exactly what I did.
I created a standardised and comprehensive breast cancer analytics platform
which can diagnose breast cancer
from a variety of different diagnostic procedures
including histograms, mammograms, fine-needle aspiration,
cytology examinations, biopsy results
so, basically, any primary care worker, nurse, clinician, practitioner,
whoever is seeing the patient,
can enter in their patient's data without any expertise
and get a predicted probability of malignancy
with an accuracy comparable to experts in the field -
comparable to what radiologists and pathologist currently achieve
at the global level.
Now, I mean, this ultimately shows us
that an AI solution can be used to power clinical optimisation
and bridge the gap between diagnosis and prognosis.
Now, I've presented this software at various different venus,
and the feedback I've gotten has been incredible and very positive.
And now it's just a matter of going through clinical trials
and passing regulatory bodies to get it implemented.
But what I really want you to take away from all this,
aside from the fact that AI is invaluable -
is an invaluable tool that adds invaluable value to a clinical setting -
aside from that, I want you to realise
that stuff like this doesn't take as much resources as you think.
I'm 16 years old, who also happens to be an immigrant,
who also happens to come from a low-income family.
And the fact that I, with very little resources,
was able to create something of such value
to both the medical and technical community
is a testament to how much we've progressed,
how much our history has given us,
how much it has propelled us to be at this point in time -
this very specific point in time -
where really anything can be accomplished in this new age of the Internet.
I truly think technology is an unparalleled medium.
It's the establishing force of a new social phenomenon,
one in which the only boundaries of global progress and global benefit
are motivated people who are willing to push boundaries.
I just really think all of us young people here today
owe it to the rest of the world
and to history
for what it's given us, for what it's led us to at this point,
and give back to it so that we can build our future.
Otherwise, why were we gifted with our very own mind that can think
or the very gift of living through this time
alongside such brilliant people
on this beautiful planet, right?
Thank you.
(Applause)