>> Live from Las Vegas, it's theCUBE.
Covering AWS re:Invent 2018.
Brought to you by Amazon web services, Intel,
and their ecosystem partners.
>> Okay, welcome back everyone,
we're live here in Las Vegas, AWS,
Amazon web services re:Invent,
our sixth year,
I'm John Furrier with Dave Vellante.
Dave, six years, two sets,
people rolling out out of the keynote,
so much action, we've got another day coming tomorrow.
We got two great guests here,
we got Dr. Vasi Philomin, who's the general manager
of Machine Learning and AI at Amazon web services,
and Dr. Taha Kass-Hout,
Senior Leader at Healthcare and AI at Amazon.
Guys, welcome to theCube.
>> Thank you. >> Thank you.
>> I'm so excited that you're here because I've been waiting
to have this conversation.
Dave and I we just had an analysis of the extractions
and moving up the stack around Machine Learning.
So much value now coming on line that's been in the works,
around AI, really mainly Machine Learning that's bringing
AI-like benefits.
Andy Jassy spent a lot of time in his keynote,
say almost a third of it around AI-like capabilities
and how Amazon integrates in it,
from chip sets,
with elastic inference, beautiful, it's just good stuff.
So congratulations.
>> Thank you.
>> What does it mean?
What does it mean for customers right now who want to
kind of grock what's going on with Amazon and AI,
is it new sets of services coming online,
is it how long it's been in the works, explain.
>> Our mission at AWS has always been to take technologies
that have been traditionally available for a few
special technology companies and take that and make it
available to all developers.
I should say that we've done that fairly well when it comes
to computes, when it comes to storage,
when it comes to databases, analytics.
And we're doing the same thing for Machine Learning and AI.
What we're doing, because it's a new field,
is we've got an innovated three layers with our stack,
so the bottommost layer as you saw in the keynote earlier,
has to do with frameworks and infrastructure,
so this is more for the people that fully understand
how to deal with Machine Learning models and like to
go in and tweak these models.
The middle layer then is for everyday developers
and the data scientists and that's sort of
where Stagemaker fits in.
And finally the top layer of the stack is where we have
our applications services.
And this is meant for developers that don't want to get
into the weeds of Machine Learning but they still want to
make use of all of these technologies to make
their applications more smarter.
>> So they get the insight benefits,
get the insights out of the data,
without getting down in the weeds.
>> Exactly.
>> And people who want to get down in the weeds.
They can get down and dirty with all of this other stuff.
Isn't that right?
>> Yeah.
And typically what we do with the top layer of the stack,
is we try and solve really hard problems.
And so customers can now take advantage of it because we've
solved it for them and they can just take that and
integrate it into their applications.
>> Real quick, what was the hardest problem
that you guys solved?
>> I mean, traditionally speech recognition
is a very hard problem.
That's one of the hard problems.
The other one is NLP, natural language processing.
But I would say speech recognition is probably
a hard problem.
When we just launched streaming transcription,
so you can now transcribe live as somebody speaks,
and of course you can connect it to translate
and translate it as well live.
>> Well great for our CUBE biz,
look forward to having that online.
>> As a health care practitioner, how does this all apply
to that industry?
What kind of projects are you guys working on
in that regard?
>> To Vasi's point, we want to continue innovate
on behalf of the customers across all layers of the stack,
Machine Learning.
In particular this week we launched
Amazon Comprehend medical,
particularly in a heart problem where the majority
of healthcare data is captured,
conversation and observations in unstructured format.
So our petabytes of data is toward across
entire healthcare system that's in unstructured forms
to drive actionable insights and to be able to find
the right elements to treat patients or to manage
a population or even to do accurate billing.
It's been really important that we can empower
our customers with building blocks for them to build
the right solutions to take advantage of that.
So Amazon Comprehend Medical is able to understand
the medical language and the context
similar how clinicians understand the medical language
and context.
For example, if you are looking at a patient medical note,
Amazon Comprehend Medical's able to accuracy extract
medical conditions, medications, tests, procedures
being done on the patients as well as their relationship
between those and understanding that context.
That this condition and this treatment go together,
as well as the new onsets.
For example patient has no family history of x,
or there's no smoking history,
all those are things in relation in the past or
in the future, or other members and this is really
what we're really proud about,
launching Amazon Comprehend Medical.
>> Tell me about how it works, because healthcare has been
a great field around where AI's old-fashioned AI,
I'd say when I was doing it in the 80's, late 90's.
Ontologies were really popular, and it's linguistics
is kind of known.
But you need a linguistics guru to do that,
you mentioned streaming the transcribing, you got meta data.
How do you guys get this kind of benefit when
the ball's moving so fast around these rapidly changing
in verticals like healthcare,
because healthcare's got a big problem like other verticals
where there's too many notifications,
what do I pay attention to?
So much data, how do you put the puzzle together?
>> Let me first give you some context here.
As you are probably aware at last re:Invent,
we launched Amazon Comprehend.
Comprehend is a text analytic service.
It helps you look into text and understand what's in there.
We started out with general things that we could detect,
like people, places, things, sentiment, the language
the text is written in and so on.
When we started customers have picked on it,
they're using it a lot.
But as they keeping using it they came back to us and said,
"Hey it's great that you guys that you are giving us
"the capability to understand general language but some
"of our domains have some special language."
>> Like jargon.
>> Yeah like take the legal domain for example.
It's got judges and defendants and very particular things
that are very relevant to the legal domain,
so they were asking us for a capability to sort of extend
the comprehend to include their custom domain
of terms and phrases if you will.
So last week we actually launched a custom entities
feature that allows them to bring in their custom domain
into Comprehend so that Comprehend can be extended
to include their domain.
So legal language is difficult to understand.
But medical language on the other hand is even more
harder to understand.
>> Acronyms, jargon.
>> Absolutely.
>> What does an entity look like?
Extracting that.
>> Misspells.
>> Yeah misspells, right?
But relating those entities together is super important
because you could in one clinical note you could
have multiple drugs in there with different dosages,
different frequencies and so you need to be able to relate
those entities together and that's the sort of thing
that Comprehend Medical allows our customers to do.
>> So you're doing a lot of that entity extraction
under the covers, is that right?
How's it work, I mean how does Comprehend Medical work,
just out of the box,
do you have to train it?
>> There's no training needed.
No Machine Learning expertise needed so the algorithm
extracts these entities as well as the relationship
between those entities and then also extracts any attributes
that might be related such as negation,
or past and future or what's anatomy of the body
it relates and whatnot, all of that is done
out of the box.
>> And that's super important you want to know whether
the patient's stopped taking a medication,
so negation, things like that you want to know
because that gives you the context.
Just getting the terms alone doesn't really tell you much.
>> Andy Jassy had a great video about the F1 analytics,
imagine having that for a person.
>> You're not doing good right now, take a break.
>> So I feel like we're kind of of now scratching the surface
of healthcare transformation.
When you think about the healthcare industry,
for years it's been compliance driven,
whether it's HIPAA, Affordable Care Act,
EMR and meaningful use,
but the industry hasn't been dramatically transformed
and disrupted and it kind of needs to be.
How do you guys see that evolving?
I feel like you're now beginning to see change.
It's going to take awhile it's
a high-risk business obviously.
What's your prognosis for that transformation and what's
the vision as to the outcome?
>> Now that's a really great question,
one great thing's happened over the last decade
is the digitization of your medical record.
And that's really wonderful because before it was
all paper-based primarily,
unless you were in acute setting.
So now the majority of the U.S. for example and globally,
there's this huge adoption and propagation of these
electronic medical records.
The issue there remains now when the majority of that data
is observations and conversations as well as unstructured,
that creates a different kind of roadblock
for our customers and this is what we're hoping for
service like Amazon Comprehend Medical,
that's HIPAA eligible, meets a lot of the compliance
or help our customer meet their compliance.
Means that we'll be able to remove the heavy lifting
of this undifferentiated task about having large amount
of time being spent on analyzing the stacks and
extractin' very low,
where now with Amazon Comprehend Medical be able
to really fast track that and be able to elevate that.
>> You hit the nail on the head on
the undifferentiated heavy lifting.
That's the ethos of dev ops.
>> Exactly. >> yeah.
>> Let me give you some stats actually.
There are 1.2 billion medical documents
that are generated every year in the U.S.
And 80% of them it's unstructured text.
So to make sense of that is going to enable our customers
to do some really amazing things.
One of the use cases that we see is
clinical trial recruitment.
So Fred Hutchinson which is one of the nation's top...
>> Yeah I was going to to ask about that.
>> Nation's top cancer research centers,
they recruit patients for clinical trials.
If you go to clinicaltrials.gov,
you'd see like 290,450 clinical trials open,
and typically from history we know that most
of these clinical trials they don't end up meeting
their recruiting goals because it's very hard to figure out
which patients fit the clinical trial that you are actually
trying to perform.
So Comprehend Medical helps these customers
to very quickly narrow it down.
>> Expand on the involvement of people in the community.
You mentioned Fred Hutch, Roche has also been involved
from what I've heard.
Sounded like it was a collaboration,
Take a minute to explain that.
>> Right.
It's very similar to a lot of other services
that we put into the market.
We collaborate a lot with customers,
90% of what we do is really coming from customers.
So we've collaborated with people like Fred Hutch and
some of the nation's topic institutions to help us validate
the service that we built.
To actually make sure that it's meeting the requirements
for those use cases that they're thinking of.
So we collaborate closely with them to get the service
to where it is today.
And we announced it as generally available yesterday.
>> Go ahead.
>> I can expand a little bit on that.
Some of the customers as well,
the use cases, we're talking anywhere from hospital systems
that when I use or take advantage of their unstructured text
for things such as identify people for their
followup appointments or stopping treatments or
finding alternative routes to billers who are trying
to identify if accurate procedures were done,
did we account for all the procedures,
account for all the billing,
which often time is heading in those unstructured text
and require a lot of manual process and often time
the rules can't really scale.
To things such as clinical trials recruitment,
for example in Fred Hutchinson Cancer Institute,
use case to identify a patient and match him
to the right clinical trial,
these patients often time have Harry Potter's worth
of clinical notes done on them in their longitudinal journey
and to go from one institution to another and another.
And be able to really find it's no longer needle
in a haystack,
it's like a needle in the bottom of the Atlantic Ocean.
>> Yeah.
>> And then be able to really do that match
from hours and months down to few seconds
and that's really the beauty about this service.
>> John likes to talk about the 20 mile stare,
and I wonder if we could just look ahead.
How far can we take AI and Machine Learning in healthcare?
And how far should we take it?
And maybe a more specific question is,
as a practitioner when do you think machines might
make better diagnoses than doctors, if ever?
How do you feel about that?
Where do you see this all going?
>> The whole idea of Machine Learning, the beauty about it,
similar to how a stethoscope was introduced,
similar to how the thermometer was introduced in medicine,
these are tools that we use to our advantage to really
provide better care and better outcomes and that's
the mission that our health IT and customers want and
are really driving towards.
Machine Learning can do a lot of great things
for routine things that human beings can go and focus
attention to other things such as like Fred Hutchinson
instead of going and mining these diagnoses
and mounds and mounds of data.
Machine Learning will be able to identify that where
the clinical staff can focus on care.
Over the next decade and so we're going to see a lot
of these advancement in these building blocks
as well as what Amazon's offering from forecasting and
prediction, algorithm is one of,
we'll be able to fine tune our capabilities
to help customers achieve even precision medicine.
>> It's real world impact because you're changing
the workflow. >> Yeah.
>> I mean someone's in the wrong line or the wrong process,
based upon their history,
HIPAA requirements really cause a lot of this record sharing
thing to be a problem from what we've been reporting
over the years.
There's kind of a solution to that.
If I move to a medical service,
I've got all that records with me,
just how do you see goin',
and how does, are there regulations that are
holding you back that are blockers,
is that clear now,
how does that solve the industry challenges of privacy?
>> If you look at the healthcare system today
there are lots of inefficiencies in there,
in the end this is all about improving patient outcomes
and making sure that we reduce costs
and that's what this boils down to.
And these are tools that allow our customers
to do exactly that.
>> Well guys thanks for sharing this insight.
Comprehend Medical's really awesome opportunities.
I think it's early days, day one as you guys say.
>> Right.
>> I think there's so much more that could be there.
I'd love to see the industry just from a personal society
change and get out of the way of all these hurdles,
get the data out there,
expose the data.
Check the privacy box, we'd be good right?
This is going to change the game.
>> Maybe we should say a little bit about how we built
the service in terms of that, right?
As you know at AWS security and privacy is number one
for us.
This service is HIPAA eligible, it's a stateless service,
what that means is, nothing gets stored.
The data's not used to improve the models
or anything like that.
The only person that can actually see the data
is the customer.
He's got the keys, he's the only one that's sending the data
to the end point,
And what he gets back only he can decode it.
So we've taken care to make sure that we can remove some
of those hurdles that people have always been worried about.
>> Well doctors, thank you so much for sharing.
>> Thank you so much.
>> Great to have you.
>> We're bringing you all the action here
from AWS re:Invent
Again, as the compute power is increased,
as software's written with new apps,
AI's changing the game.
Of course theCUBE and a lot of video,
we're going to need some of these services
to make these transcribes on the fly.
Thanks for coming on, really appreciate it.
>> Thank you.
>> Be back with more after this short break.
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