MARK MIRCHANDANI: On this episode,
we talked to doc.ai about how they
use Federated Learning to preserve privacy while training
machine learning models.
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So going back to your application,
you kind of mentioned that AI was used for the patients that
are using it.
Tell us a little bit more about the actual app.
AKSHAY SHARMA: Yeah, so we have the medical research companion
app.
And what it allows you to do is basically be
able to collect some important omics information.
And we have reduced the friction to be able to collect that.
Like I mentioned earlier, you can take a medical selfie which
is just a selfie, but it extracts medical information
out of that.
We can extract mood and behavioral data out
of that selfie.
But we can also take pictures of your pill bottles
and understand what medication and dosage and instructions
that exist on those.
We can do that pretty much for lab
results and medical records very soon.
We can also collect your genetic information.
So we don't do wet sequencing, meaning,
that we don't get you sequenced.
But we can collect any of the raw data
that you have sequenced yourself, for example,
from 23andMe or Ancestry, and be able to bring that into the app
and keep it in the app and then understand what's in the data.
So one is it's all about polyomics data collection
that the app allows you to do.
But on the other side, we basically
run what we call as data trials, which
is the new version of what medical research looks
like where we can actually take users vertically,
based on their disease or conditions,
and use the data that they've collected so far to lead them
towards this research with the right consent
and the right options so that they can participate and get
some rewards.
The rewards could be financial, but it will also
be more than that, like insights and other predictions
that we can give on top of that.
So largely, the medical companion app
is a medical research app that allows patients to understand
and see what are the newer ways that they can find solutions
for their diseases today.
MARK MIRCHANDANI: How does the cloud
enable that collection and then actual using of that data?
AKSHAY SHARMA: Right, so we see that in many ways.
So first of all, for some something
like medical selfie, which is basically a deep learning
model that we have built, we have built it
on the cloud across a lot of data
that we were able to collect painstakingly
through different techniques.
So the medical selfie is a deep learning model
that can take an image or a selfie of your face
and be able to predict your sex, height, weight,
and age as parameters out of that,
but then also creates further predictions out of that.
So now that's a model that we built on the cloud--
on Google Cloud-- using a lot of computational resources.
Then we were able to convert that model
to run on a mobile device.
So we were able to take that and made it do edge inferences.
So we can run it on a phone, and it can actually
predict those variables just sitting on the phone
without really revealing or taking any of the images
that the patient or the member is taking back to the server.
So one thing that I see there is that cloud enables
us to build models like this that today
is much harder to build just on the phone.
But with newer approaches like Federated Learning,
we can actually also train and build models on the phone
across many, many, many users, like hundreds of thousands
of users.
So we may not need cloud to even centralize the data
at some point in the future.
MARK MIRCHANDANI: So what exactly does Federated Learning
enable for the users who are using the application?
AKSHAY SHARMA: That's a great question.
So I think health care data in general
is a very valuable asset.
And I think it's highly secure and private data
that every user and every patient
should actually care for.
So we think the future of where all of the health information
will exist is only on the edge, specifically
on the mobile phones for the users.
And I think it's easy for them to lose that information
and send it to a cloud if they're not aware of how
that data is being used.
So what we can do with Federated Learning now
is provide guarantees for users that it can stay on the device.
But then we don't lose the aspect of learning something
from the data, wherein we can now build models
that can train on the device.
And we can build a better model on the server
by just sort of sharing the device-trained information,
which is usually like bits of information, like numbers,
but not the real data itself.
And imagine now if we can do that
across a population of users like hundreds of thousands
of users, then what we get behind the scenes
is a much more robust model that's much more diverse.
And hopefully, it's much more generalized than as
if it was trained on a single user.
So that's what Federated Learning can do.
And then we can add layers of security and tools
like differential privacy where we don't even
learn anything about the person that we are training on.
So in combination with differential privacy
and Federated Learning, I think we
have a very important solution where
we can keep the data on the edge and still
be able to learn something from the data
without knowing who the user is or what's in the data.
MARK MIRCHANDANI: Stay tuned for more conversations with doc.ai.
If you enjoyed this episode, check out the Stack Chat
playlist for more great videos.
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