[soft music]
Steer: I've come from a family
where the females have definitely had breast cancer,
and when I got an abnormal mammogram,
I thought, "Yes, I've got it."
I was really surprised how emotional I became
on the day of the biopsy.
And when the doctor was actually doing the biopsy,
I just broke down into tears.
And it was really a difficult day for me.
A few days later, I was told that I had
metastatic breast cancer, grade three.
My treatment involved two operations, six cycles of chemotherapy,
and a course of radiotherapy.
I had so many scans and procedures
that it could actually fill a book.
After I had finished the radiotherapy,
I felt so detached.
Fortunately, my breast care nurse,
she realized that I had post-traumatic stress
and referred me to see a clinical psychologist
who specializes in cancer.
Karthikesalingam: Unfortunately, Suzanne's condition is not unique.
Breast cancer affects about one in eight women
at some point in their lifetime,
and right now, the treatment that follows
can often be long and stressful.
In many countries, X-ray screening is done for breast cancer with mammography,
but the number of clinicians required to keep this going
is difficult to sustain.
To tackle these problems, we worked with patients like Suzanne
to explore how artificial intelligence might be able to detect breast cancer
in screening mammograms.
Our researchers are also looking into the applications
of artificial intelligence for cancer treatment.
And that work begins in partnership with Mayo Clinic.
We're really excited to investigate
whether artificial intelligence can help in these complex processes
like planning of radiotherapy.
Beltran: Over half of all cancer patients receive radiation
as part of their overall cancer therapy.
Radiation oncology itself is a very technology-driven field
and it's ripe, if not actually overdue, for AI implementation.
So when Google Health approached us
about collaborating on AI radiation therapy,
we said, "Absolutely."
Radiation therapy is used to kill cancer cells.
The first thing we do is take images or scans of the patient.
That way, we can identify critical structures in the tumor.
The radiation oncologist, based on these scans,
painstakingly goes and draws all these critical structures.
These would be things like your optic nerves,
your brain stem, your spinal cord.
And that way, when we upload these to our computer models,
we can avoid these critical structures and just target the tumor.
Karthikesalingam: So this process of outlining the normal tissues
as well as the cancerous tissues is called contouring.
It has to be done very meticulously,
and that can take expert clinicians six or seven hours per case.
We think artificial intelligence might have the potential
to really streamline this process.
Beltran: We're optimistic that AI will help increase
the amount of time we can spend with the treatment plan itself
and for the doctors to spend with their patients.
Ideally, this would eliminate that agonizing amount of time
patients wait to get their cancer therapy.
Steer: The hospital treatment took nine months,
and obviously that involved a lot of waiting
for new appointments to come,
waiting for the results of procedures to come.
It just seemed endless, and I'm sure it would've been easier
if it hadn't been so long.
I was very fortunate in having a breast care nurse assigned to me
who was always able to give me
the time that I needed,
but there were certain times when I wished
I had been able to spend a little bit longer
with my consultants.
Karthikesalingam: Patient participation really empowers our research.
It brings clarity to our mission
and makes sure that we're learning from those with lived experience.
This new study with Mayo Clinic will not actually take place
in a clinical setting.
We'll be studying the use of algorithms on deidentified data,
images which you cannot trace back to an individual patient.
We'll be beginning by trying to assess contouring in the head and neck.
We're starting with this area of the body
because there are many anatomic structures
squeezed into tight spaces.
If we can perform this delicate and difficult task
in head and neck structures,
we think it should be feasible to scale up the technology
to other areas of the body
and benefit as many patients as possible.
Steer: I'd always been aware
that it is absolutely vital and crucial
that patients are involved in cancer research,
specifically in terms of partaking of clinical trials.
Suddenly, I found myself being involved actively
in helping with the research,
and I found it, actually,
really cathartic.
It was the cancer, instead of being so much in me,
came out,
and I was able to use my experience
to really be of use, I think,
and that, I think, is empowering
when you realize that you've got something to offer.
Beltran: Patients come to Mayo Clinic to receive the best quality care.
Our collaboration with Google Health
will help us increase and continue
that great quality care for our community,
but also for everyone around the world.
We hope that with this partnership,
we'll be able to not only increase the quality, but increase the access
of radiation therapy to patients around the world,
which will improve cancer outcomes.
Karthikesalingam: Google works at the cutting edge of artificial intelligence.
And we believe that its applications in health care
could assist doctors and caregivers
and improve the accuracy and availability
of many investigations and treatments.
Our north star is helping everyone live healthier lives,
and we believe that with projects like this partnership with Mayo Clinic,
we can do just that:
improve treatments for cancer patients by making the process faster,
more efficient, and more precise.
Accurate treatment plans can be accessible
for far more people around the world.
[inspiring music]