- Hi, I'm David Niewolny.
And on behalf of the entire
NVIDIA Healthcare and Life Sciences Team,
welcome to RSNA 2022.
The opportunity for accelerated computing
and AI in healthcare has never been greater.
And the adoption of these technologies
in medical imaging continues to increase.
Today, we're gonna discuss innovations in medical imaging,
share the value that NVIDIA is bringing
to the entire healthcare AI development
and deployment ecosystem,
discuss challenges to widespread clinical use,
and, finally, we're gonna propose some solutions
to help turbocharge the AI innovation engine
within medical imaging.
Medical imaging is one of the most essential tools
in all of healthcare.
Two-dimensional imaging for screening and early detection.
Three-dimensional imaging actually provides
spatial understanding,
quantitative measurement and segmentation.
The fourth dimension introduces temporal information
that's essential for diagnosing and planning treatments.
Now using medical images,
in combination with realtime deep learning
and computer vision,
brings us into a fifth dimension.
This fifth dimension allows us to perceive our surroundings,
navigate within the human body,
plan actions, track objects,
and perform surgical tasks.
Let's take a look at how NVIDIA
is driving innovation within medical imaging today.
NVIDIA's working with leaders
in diagnostic imaging equipment
to provide the computational infrastructure
needed to improve image quality, lower radiation dose,
and run AI applications.
Medical imaging breakthroughs have historically come
from innovations in sensor technology at the device level.
And for every leap in sensor technology,
medical imaging needs a leap in computational performance.
Photon Counting CT is the future of medical imaging.
This breakthrough would not be possible
without a step function increase
in computational performance
provided by NVIDIA's full stack computing architecture.
Let's go through a couple examples.
Siemens' Naeotom Alpha uses Photon Counting CT
to improve image acquisition
and reconstruction by reducing electronic noise
as well as lowering radiation dose
and contrast material on patients.
NeuroLogica's OmniTom Elite
with photon counting detectors generates spectral CT images
at multiple energy levels,
leading to potentially more accurate visualization
and segmentation of bone,
as well as improve visualization of blood clots,
plaque, hemorrhage, and intercranial tumors.
And Advanced Breast-CT's nu:view product
uses spiral CT combined with photon counting technology
to deliver compressionless breast exam
with absolutely amazing image quality.
NVIDIA's providing the computational foundation
for innovators developing next generation
enterprise imaging and AI platforms.
These platforms leverage NVIDIA's
accelerated computing stack
to provide advanced visualization and AI
to medical images from any device for any modality.
NVIDIA customer, Aidoc, won a 2022 mini-award
for their AI operating system.
This vendor-agnostic operating system
makes it easier for healthcare institutions
to deploy AI in radiology
by orchestrating a range of AI algorithms,
both from Aidoc
as well as a variety of third-party developers.
It truly is amazing to see what accelerated computing
has enabled our customers to build.
The future of medical imaging
is software-defined and AI-enabled.
NVIDIA's democratizing AI for the global healthcare industry
by enabling developers
to easily adopt artificial intelligence
from the cloud all the way to the edge.
NVIDIA created Clara to harness the incredible advancements
in accelerated computing,
computer graphics, and artificial intelligence,
and then apply them
to key healthcare and life sciences workflows.
The Clara Medical Imaging Platform
is a set of developer tools, SDKs,
and application frameworks
that enable the medical imaging ecosystem
to accelerate their development
and deployment of AI-powered products and services,
from the imaging device, to the data center, to the cloud.
At the core of the Clara Medical Imaging Platform,
we have MONAI,
an open-source medical-specific AI framework
that's used to develop and deploy AI models
into AI applications at scale.
And secondly, we have Clara Holoscan,
a medical-grade realtime edge AI computing platform
developed specifically for generating insights
at that point of care.
Let's take a little bit of a deeper look
into each of these different technologies.
Clara Holoscan is a realtime edge AI computing platform,
specifically for medical devices,
consisting of an application framework
that runs a robotics pipeline.
Clara Holoscan ingests and processes
streaming data and images
and performs AI inferencing and visualization
at ultra low latencies,
as low as 10 milliseconds from sensor to screen.
This application framework
is optimized to run in the recently announced
NVIDIA IGX platform.
This is a production-ready medical-grade
edge AI appliance that comes with an operating system,
built-in security and management,
safety extension package, and long-term support.
Clara Holoscan gives the entire medical device industry
a standard computing platform
for realtime edge AI processing.
This saves a huge amount
of hardware and software engineering time,
freeing up those resources
to develop what the industry needs most,
life-saving healthcare applications.
MONAI is a community-led open-source framework
developed in collaboration with King's College London,
designed specifically to accelerate
the clinical translation of AI
within the field of medical imaging.
MONAI is purpose built for radiology,
pathology, and surgical data,
and tackles the entire AI life cycle
with pre-trained models, AI-assisted labeling tools,
state-of-the-art training techniques,
like federated learning and self-supervised learning.
MONAI standardizes the application development, packaging,
and deployment to the healthcare IT infrastructure.
With over 650,000 downloads,
more than 450 GitHub projects,
160 papers published,
and 11 Kaggle competition wins,
the MONAI community continues to grow and thrive.
Let's hear from a few of our community members
who are developing clinical applications today using MONAI.
- In surgery in general,
and neurosurgery specifically,
you can fix what you can see.
Therefore, visualization is critical in our work.
- If you're able to interact
and visualize that patient's images,
you're better able to plan the correct intervention
and make sure that you apply the right intervention
the first time.
- It's a way to see MONAI as a toolkit
which is dedicated to AI in imaging.
- So MONAI is a community-driven effort
to bring data science into the clinic.
And that's everything from the development
of data-driven products
and the curation of data
to then how we get those insights
in front of clinicians looking after patients.
- Since adopting the MONAI infrastructure,
it's freed us to focus
on application to specific clinical problems
and catalyzed that application in multiple directions.
- What we can do through MONAI
is that be able to segment the tumor,
segment all the critical structures around it,
and in surgery, provide real feedback data
to navigate the surgeon around the tumor
to make sure most of the tumor is removed.
Therefore, we're providing the patient
with the most benefit.
- What MONAI then brings is the final layer on top,
which is a way to scalably
apply artificial intelligence
across that entire medical record,
across every patient pathway.
The potential scale of that is massive.
I can see MONAI in every clinic
in every hospital across the world.
- How do we ensure that AI makes its way
into every hospital, every clinic all across the world?
The challenge is bridging the gap
between research and commercial deployment.
Adoption of AI in radiology
has traditionally been constrained
by the complexity of clinical workflow
and the lack of a de facto standard
for AI development and deployment.
It's been said that 99% of medical imaging AI applications
never actually make their way
to clinical deployments at scale.
This puts AI in medical imaging at a critical tipping point
that Geoffrey Moore famously refers to as the chasm.
To cross the chasm and bring AI
from the innovators in research and development
to every clinic, every hospital,
and every patient around the globe,
there are really three key requirements
that must be addressed
to bring AI from the bench to the bedside.
First, you need a wide and diverse ecosystem of developers,
leveraging a common development deployment environment.
Second, you need a de facto standard
for your AI application packaging.
And, third, enterprise support for clinical deployment
of these AI applications.
To expand the reach of MONAI,
today, we're announcing the adoption
and integration of MONAI
by all of the major hyperscalers.
Leading cloud service providers like AWS,
Google Cloud Platform, Microsoft Azure,
and Oracle Cloud infrastructure
are all using MONAI
to develop AI-enabled products and services.
MONAI is available on AWS SageMaker.
And recently at Health,
they announced their new medical imaging storage service,
Amazon HealthLake Imaging,
that uses MONAI for fast data processing and AI inference.
Google Cloud Platform recently announced
their medical imaging suite
and featured MONAI as a core component
to both their imaging lab,
for ground truth data annotation,
and imaging AI pipeline for inference.
Microsoft Azure is focused on delivering
enterprise platforms at scale.
Microsoft's Nuance Communication
partnered with NVIDIA to bring medical imaging AI models
directly into the clinical setting
by implementing MONAI
within their precision imaging network.
Oracle Cloud has big things planned
with their recent acquisition of Cerner,
one of the largest electronic medical record companies
in the world.
Oracle has adopted MONAI
to develop their healthcare-specific
medical imaging products and services.
It's so amazing to see the reception
that MONAI is getting throughout the entire ecosystem,
specifically with the CSPs and hyperscalers.
To ease the development effort to bring AI applications
from the bench to the bedside,
today, NVIDIA's announcing the availability
of the MONAI Application Package,
also known as MAP.
The MAP specification standardizes
how developers build and package their AI models
into deployable applications.
This ensures a consistent
and repeatable deployment pipeline
that will run exactly the same
on any clinical platform supporting MAPS.
By adopting MAPS as a native application,
healthcare organizations and clinical platforms
can begin leveraging all of the amazing AI research
that's happening now in MONAI
that was unavailable to them until now.
MAP streamlines the model adaptation time
from years to weeks,
and application updates go from months to days.
This together accelerates the clinical impact of AI.
The MAP specification is truly the bridge the industry needs
to connect researchers and developers
building the groundbreaking AI models
with the clinical teams
that ultimately realize their benefits.
This is a giant step forward
for bringing AI into every hospital,
every clinic all around the world.
Map once, run anywhere.
Commercial AI application developers,
cloud-based AI inference platforms,
and large-scale clinical organizations
are already taking advantage of the ability to map once
and run anywhere.
qure.ai, a breakthrough radiology
artificial intelligence solution provider,
taps deep learning technology
to provide automated interpretation of radiology exams.
This enables faster diagnosis and speed to treatment.
They've adopted the MAP specification
to widen their network of deployment options
across their entire partner ecosystem
with the least amount of development effort.
Nuance Communications, a leader in ambient intelligence,
has added support for MAP application deployments
within their precision imaging network.
This brings the medical imaging
machine learning development framework
directly into the clinical translation workflow,
accelerating innovation and clinical impact
all the way from bench to the bedside.
The London AI Centre for Value Based Healthcare,
a team of AI data science research and clinical experts
working collaboratively
across a wide number of academic institutions,
like the NHS and others,
have adopted MONAI and the MAP specification
to standardize their end-to-end workflow.
This platform is called AIDE
and has been deployed across 10 NHS trusts
and is serving more than 18 million patients.
Earlier this week,
I had the opportunity to catch up with Jorge Cardoso,
the CTO of the AI Centre,
to talk about the work he is doing with MONAI
and how it's impacting patients all across the UK.
Jorge, it's so great to see you again.
Thank you so much for taking the time to join me
and tell me a little bit about the work you're doing
at the AI Centre.
- Thank you for inviting me, it's really a pleasure.
- So tell us a little bit about the work you're doing
at the AI Centre,
how you're using MONAI,
and specifically how are you using
the MONAI application packages?
- We are on the journey
to make all of these hospitals AI-enabled.
And first, to do this,
we need to solve the translational issue
of how do we go from research and development to deployment.
This is where MAP comes in.
It allows us to transition from research to deployment
faster than what we could before.
- Fantastic.
So can you translate this into a real-world use case
with actual clinical impact?
- Of course.
Stroke is currently the largest source
of adult disability in the world.
When a patient has a stroke,
a clinician needs to act incredibly quickly.
So we have been creating algorithms
to be able to support the decisions of how to intervene
on the patient that just had a stroke.
We have run tests on these algorithms
and gather evidence on how well they will work
in real patients.
And we're using now that evidence
to further improve the algorithms
so that we can deploy
an improved version of those algorithms.
So this cycle would only exist
in a scenario where we can actually reach clinics quickly,
which is almost unheard in a academic environment.
And MAP really is an enabler of all of that.
- It's so great to see the actual clinical impact.
Can you share what you see is next for MONAI?
- At the moment, MONAI is a collection of tools
that take care of many of the pain points that you have,
again, from research to deployment.
But what's interesting is
when you start building on that technology
and MONAI goes from a toolkit
to a more platformic infrastructure,
you will slowly have MONAI
going in the direction of what I like to call
the healthcare OS, the healthcare operative system.
If we are able to go that far
and provide that core infrastructure
that anyone throughout the world can use,
we will make AI a reality in healthcare.
- It is so great to see the vision
that Jorge has for the future of MONAI
and how he views it as the operating system
for AI in medical imaging.
One of the biggest drivers for widespread adoption of MONAI
is its open source nature,
allowing for collaboration and contributions
from the entire global medical imaging community.
To optimize AI deployment for speed of impact
into the mainstream clinic,
the third and final component we need
is an enterprise-grade software suite.
This allows us to truly harness the power of MONAI.
Today, we're announcing that MONAI is now part
of NVIDIA AI Enterprise software suite.
This gives you the best of both worlds.
You can begin your journey using the open-source
community-supported AI development and deployment platform.
Then as you make the transition
to production-ready enterprise deployment,
NVIDIA AI Enterprise gives you the suite of AI
and data analytics software
that's optimized, certified, and supported
for every organization to deploy AI anywhere,
on-prem or in any public cloud.
NVIDIA AI Enterprise speeds development
and accelerates clinical impact
by providing you the support you need when you need it.
NVIDIA Clara is the medical imaging platform
that combines MONAI and Holoscan
in an innovative way to accelerate AI
across all of healthcare.
MONAI is building AI workflows that address the unique needs
of the AI developer.
From AI-assisted labeling,
to privacy-preserving federated learning,
MONAI will tackle every imaging modality
that drives healthcare innovation,
starting in radiology
and expanding into pathology and surgical video.
Clara Holoscan is our realtime AI compute platform,
built to tackle the most critical imaging use cases,
like robotic surgery.
We will continue to evolve this platform
with new features needed to meet the requirements
of these mission-critical use cases.
The next frontier of AI development
incorporates new capabilities
enabled by things like digital twin technology,
allowing us to generate synthetic data, build AI algorithms,
and validate them in virtual worlds.
Our roadmap illustrates our extreme focus and commitment
to bringing the most advanced computing approaches,
AI, accelerated computing, computer graphics
to the entire medical imaging community.
If you haven't started working with MONAI or Holoscan,
it just got a whole lot easier to get started.
MONAI and Holoscan are now available on NVIDIA LaunchPad.
LaunchPad is an NVIDIA-hosted infrastructure
with an entire catalog of hands-on labs,
making it easy to test
and prototype with the latest NVIDIA technology.
Begin your journey with the MONAI Label Lab,
designed to let you see how quick and easy it is
to create annotated data sets,
build AI annotation models for labeling.
Then try out Clara Holoscan lab
where you can run sample applications
using the Holoscan SDK.
NVIDIA's leading AI-powered innovation in medical imaging,
our healthcare-specific accelerated computing platform
enables the entire medical imaging ecosystem
to easily explore new AI-powered capabilities,
speed time to market,
and drive immediate clinical impact.
Device developers can use our Clara Holoscan on IGX platform
to deploy realtime AI skills directly at the point of care.
The research community can build upon MONAI's
robust ecosystem of partners
and begin standardizing on MONAI application packages
to expand their deployment options.
And for hospitals, clinics,
and medical imaging platforms around the world,
you can now reduce your time to clinical impact
by building or buying products
that incorporate and leverage MONAI.
Just as the theme of RSNA 2022
is empowering patients and partners in care,
you can see that NVIDIA
and NVIDIA technology is playing a huge role
in empowering the entire radiological ecosystem
by accelerating the adoption and clinical usage of AI.
I wish you all a great RSNA 2022.
Come to our sessions,
join our demos given by NVIDIA leaders,
customers, and partners
that'll be available throughout the entire week in Chicago.
And please keep on driving to bring AI in medical imaging
from the bench to the bedside.