Name: AIと機械学習のイノベーションをヘルスケアにもたらす（Google I/O '18 (Bringing AI and machine learning innovations to healthcare (Google I/O '18))
Uploaded: 2021-01-14T08:02:35.000Z
Duration: 32 min 51 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

I'm a physician by training and I work on the Google medical--

And today we're going to talk to you about a couple of projects

that we have been working on in our group.

So first off, I think you'll get a lot of this,

so I'm not going to go over this too much.

to just define a few terms that get used quite a bit

So first off, artificial intelligence-- this

is a pretty broad term and it encompasses that grand project

of machine learning which you guys have probably

heard about quite a bit and will hear about quite a bit more.

So it's a modern reincarnation of artificial neural networks,

which actually was invented in the 1960s.

It's a collection of simple trainable units, organized

And they work together to solve or model complicated tasks.

So in general, with smaller data sets and limited compute,

which is what we had in the 1980s and '90s,

But with larger data sets and larger model sizes

and more compute power, we find that neural networks

that I want you guys to get from this slide.

One is that deep learning trains algorithms

that are very accurate when given enough data.

And that means without explicitly writing the rules.

that a machine should follow to make a certain prediction task.

we actually spend very little time in feature

Most of the time we spend in data preparation

and numerical optimization and model architecture.

And the question is, how much data is enough data

But there are diminishing returns beyond a certain point.

like to have about 5,000 positives per class.

But the key thing is good and relevant data--

The model will predict very well what you ask it to predict.

So when you think about where machine learning,

and especially deep learning, can make the biggest impact,

One of our directors, Greg Corrado, puts it best.

Deep learning is really good for tasks that you've done 10,000

times, and on the 10,001st time, you're just sick of it and you

So this is really great for health care in screening

applications where you see a lot of patients

It's also great where expertise is limited.

of the shortage of radiologists kind of worldwide.

And this is also true for other medical specialties,

And we basically see a worldwide shortage of medical expertise.

that our group has worked on is with diabetic retinopathy.

And it's the fastest growing cause of preventable blindness.

All 450 million people with diabetes are at risk and need

This is done by taking a picture of the back

of the eye with a special camera, as you see here.

And the picture looks a little bit like that.

And so what a doctor does when they get an image like this

is they grade it on a scale of one to five from no disease,

And when they do grading, they look for sometimes very subtle

findings, little things called micro aneurysms

that are outpouchings in the blood vessels of the eye.

So unfortunately in many parts of the world,

there are just not enough eye doctors to do this task.

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AIと機械学習のイノベーションをヘルスケアにもたらす（Google I/O '18 (Bringing AI and machine learning innovations to healthcare (Google I/O '18))

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