Transcriber: Elisabeth Buffard Reviewer: Anna Sobota
Take a look at this x-ray here.
I’m going to give you a medical pop quiz.
Does anything look out of place with this x-ray that you can see?
What if I told you there was actually a fracture somewhere in this x-ray?
Would you believe me?
Well, it turns out that is the case as shown by this pink box here.
That little crinkle there that you see
is actually what’s called a Taurus fracture.
See, this is a child’s hand, this x-ray,
and children’s bones are more malleable
than adult bones.
So sometimes when they break their wrist,
it’s more of a dent or a bend than it is an actual clean break.
Now, you might not know to look for things like this,
but I only happen to know because I went to medical school.
I’m the person that built this AI.
My name is Logan Nye,
I am a research physician at Harvard Medical School,
where I develop intelligent systems for health care
or in other words, I’m a physician who codes.
And that makes me unique and in a couple of ways.
One, it makes it so that
I’m probably the biggest nerd that you’ve ever met in your life.
That’s quite the overlap.
But also it gives me a unique vantage point
for creating artificial intelligence for medical diagnostics and health care.
Usually when you develop artificial intelligence,
you have developers and you have domain experts and there’s some crosstalk
and they develop these sorts of algorithms.
But I want to show you some insights as to why it’s helped me
to be someone with scientific domain knowledge
that just learns how to develop artificial intelligence directly.
So to do so, I’m going to share some examples from my research at MGH
from the past year.
Here we have two images of histology,
meaning the cells under a microscope.
They look pretty similar, right?
These are both showing cartilage tissue, but not just any cartilage tissue.
They’re both tumors.
On the left we have enchondroma, which is a benign condition.
It’s just a clump of cells
that you don’t need any particular medical care for.
You just keep an eye on it for the rest of your life.
But on the right-hand side, we have low-grade chondrosarcoma.
It looks almost identical,
but it’s vastly different results.
Low-grade chondrosarcoma is very aggressive
and needs to be treated aggressively.
It requires surgery, often amputation
and cautery of the wound that you scrape out.
So obviously, it's important to know the difference between these two.
But historically,
we’ve been about 50% reliable in telling them apart on histology.
I know about this because I’m in medicine
and it’s a widely known problem.
But as I also happen to know how to build artificial intelligence systems,
I realize that this is great for image classification,
a computer vision problem.
And so over the course of an afternoon,
we built a computer vision program
that took our diagnostic accuracy between these two different tumor types
from around 50% to 75 to 90%.
It was a long-standing problem for several decades
but if we apply AI and know how to apply it in a clinical context,
we can solve those long-standing problems in a short course of time.
And there’s other applications as well.
Take these knee MRIs.
These are showing different ACLs or anterior cruciate ligament
that get torn.
Using computer vision,
we can automatically find where and when they’re torn.
On the left-hand side we have a healthy ACL,
in the middle a completely ruptured ACL,
on the right the AI systems that we develop
are able to tell that it’s only partially torn.
This is another way that computer vision can be applied within health care.
That’s just the tip of the iceberg.
We have scaphoid fractures.
We’re able to detect lumbar disc herniations when people have back pain
and need to localize where their nervous system is being pressed on.
We’re able to get fractures in arm areas,
foot and ankle, hips,
and we’re even able to quantify osteoarthritis in knees
to tell people when they should be considering getting a knee replacement.
All this is possible because we apply artificial intelligence
from a clinical standpoint.
In our lab, we’ve also worked on cancer diagnostic systems
similar to the enchondroma problem,
but applied to over 30 different other soft tissue tumors.
And not just soft tissues, but 15 or more bone tumors as well.
We’re continuing to expand experiment with these different applications
in the clinical context.
Again made possible because we know what to look for
and thus how to build AI.
And in some cases, when you have rare diseases,
you lack the data
to create these useful artificial intelligence programs.
Enchondroma is an example of this, that first tumor that we looked at.
But thanks to generative AI, we can bridge that gap
and the scarcity of medical data that exists.
This histology image of enchondroma actually doesn’t exist.
It was generated by AI,
but because we know that that’s a gap that exists in clinical care,
we can leverage AI to fill those needs.
Now, you might ask why is this important?
I can tell you why it’s personally important
and why I became interested in developing artificial intelligence.
During medical school, I had the opportunity
to participate in global health trips.
One of them was to sub-Saharan Africa.
The people there are incredible and do awesome things with what they have,
but there’s no denying that they lack resources.
We drived all the way out into the bush
and we would spend all day working with
the types of supplies that you’d find at OfficeMax or Home Depot
to care for patients.
But one thing that I noticed was that
the medical staff used iPads to record all their patient data.
They had access to smartphones.
And again, when I went to the Himalayas,
we treated all sorts of patients in far reaches
across the Himalayan mountain range
and they lacked those resources, but they all had smartphones.
So anybody that has a smartphone
is able to access artificial intelligence systems
and thus get proper care.
And so that’s what we’re working on.
We’re working on apps and APIs
that we can open source, make available to anybody anywhere.
If you have Internet access and if you have a smart device,
we can democratize our medical expertise to those who most need it.
Preventing the need to travel for days or even weeks
to get the care that they need.
And so, I would encourage other people that are domain experts,
whether it be in medicine, law, education, whatever,
to try to find ways to use artificial intelligence
to amplify your expertise and make it available to other people.
Who knows, it might just save lives.
Thank you.
(Applause)