This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.
Today on This Week Health.bers, and which now I oversee:
welcome to a Solution Showcase. AI is powering change in every industry across the globe as companies are increasingly data driven the demand for AI technology grows. Today we're talking with Renee Yao global healthcare, AI startups business development lead at Nvidia about making your enterprise AI ready and unleashing its power and potential with Nvidia AI Enterprise. This is a really cool platform. Great discussion. I learn a lot. And if you're wondering, my name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of this week Health, A set of channels dedicated to keeping Health IT staff current and engaged. You can subscribe wherever you list the podcast. Apple, Google, Spotify, Stitcher, overcast, you name it, we are there. You can also go on this weekhealth.com and subscribe there as well. And now onto this discussion 📍 with Renee. All right. Here we are for a solution showcase, and I'm excited to have Renee Yao, the Global Healthcare AI Startups lead for Nvidia on the on the show. Renee, welcome to the show.
Thank you, bill. Great to be here and look forward to learning lots and sharing lots with the.
Yeah, I'm looking forward to this conversation cuz I have so many conversations these days around ai organizations doing things and almost invariably all of them, like NVIDIA is behind the scenes. It's like you're not front and center. but You are enabling some really cool new workflows, some new applications and whatnot that are really exciting. I talked to Nuance, which is doing some stuff. I talked to Art Site, which is doing some stuff. I think Rhino Health, there was a bunch of 'em that are doing some really neat things.
But you're right in the center of it. So where are we seeing AI in healthcare and what are some of the really cool use cases that you're seeing out there?
Yeah, we are seeing quite a lot in radiology pathology using AI to automatically identify contouring tumors. As an example, lots happened at RSNA and hopefully many of you guys checked out Luna CureAI Quantive and in fact 60 some startups and organizations.
There are our core partners that are using various different solutions There. Patient monitoring, like you mentioned, Artisight detecting patient falls, improving operation efficiency improving surgeries and trained surgeons on how to do surgery better. For example, theater in the space or in the genomic space using AI to speed geno sequencing.
You guys may have heard about how Stanford and Oxford Nanopore using. our Clara Parabricks to be able to sequence whole genome in just less than five hours, which usually could take weeks or months out. And the drug discovery predicting drug candidates to reduce, like the need for clinical trial or human participation in research it's pretty fascinating space too. So those are several areas that we definitely see where AI add tremendous values.
it's interesting because AI. Invidia's doing stuff in AI across the board. I mean, I read about you in a lot of other industries and whatnot. The specific focus in healthcare, what kind of challenge specific challenges in healthcare is Invidia addressing?
Yeah. I think we have passed the stage of using AI to train applications because lots have happened the last 10 years, and now we are really at the space of deploying. At scale. So some of the challenges in particularly in AI deployment we're seeing is application integration. There's a lot of existing core hospital clinical and non-clinical applications adding on top of new AI applications.
It's quite a lot for any IT professionals to handle. And on top of that, the modern infrastructure on-prem, in the cloud are hybrid approaches. Timely and consistent support that these organizations need or deploying at scale with interoperability in mind. So those five challenges in application integration, modern infrastructure support, deployment at scale and interoperability are top challenges that we see.
How does NVIDIA address those challenge?
Yeah, so we announced Nvidia AI Enterprise, which you can think of it as the OS for Nvidia AI platform. There's a huge play in the healthcare space in particularly, and it's an end to end secure cloud native suite of AI app software for operation efficiency improvement. It can be deployed anywhere from data center to the cloud. And we actually have now expanded enterprise support, new AI workflows and optimized pre-train models and. applications From our ecosystem partners and all of those that I mention and more addresses the five challenges that mention above.
Fantastic. So act, can you run through those five challenges one more time, like bullet point format? The five challenges are
applications, integration, modern infrastructure, enterprise support, deployment at scale, interoperability
Interoperability Fantastic. So enterprise AI operating system for healthcare, that's what I, that's what I heard you sort of sum up that addresses those five challenges. What are some of the different AI workflows that are available for healthcare today?
Yeah, so many of you guys here may have heard of MONAI M O N A I, which is an open source AI power medical imaging framework. And since it's launched many years ago, it has already been downloaded 600,000 times more than 30 medical centers have published papers using them and more than a 450 GitHub projects.
What was announced at RSNA that you may have picked up is. that NVIDIA AI enterprise support from MONAI was also launched. What that means is MONAI now has a enterprise grade version of it. When you need to deploy it, you can ensure that the MONAI that's being deployed is battle tested and have enterprise support behind that.
So you can essentially use AI to to better segment and classify in radiology, pathology, and upcoming surgery. Video datas as well
So what Red Hat did for Linux in the enterprise essentially you're now doing for MONAI for the healthcare enterprise.
That's a very good way to think about it. Exactly. And MONAI is a collaborative effort with a dozen of leading contributors we're one key players. We heard from the customers in the space that, can we just please get some enterprise support? And there we go. We heard it, we heard the customer's demand and we're making it.
Plus you're essentially saying, Hey, this build works. Like we've, tested it, it works, and you can go with it. One of the biggest challenges with, open source as a former cio, is we could go in that direction, but then we had to get that build just right for us. Test it constantly, keep it updated, but that's, that's the thing that you're taking care.
Exactly. And build your spot on. That's exactly the challenge we addressed. Many of the CIOs, CTOs come to us and said, can we actually pay someone for support? We were supporting the open source ecosystem for a while, and it's time that we offer enterprise support for many open source pre-trained models out there.
Right. So imaging you also mentioned some genomics. And by the way, that mapping the genome in, would you say five hours or four? Five hours. Do you? I, I remember when that project was going on and there was all these companies racing towards it, and it was taking them years and years and years to do what we now do in five hours. That's, that's just absolutely amazing. Talk about the genomics solution workflows. What's going on there and, and what does that look like?
Yeah, so the lproduct is called para bricks. So under our Clara umbrella, Clara para bricks is the software suite that specifically target the secondary analysis of next. Generation sequencing DNA and RNA data. So that's the paras that was used to break the world record with Stanford Ox Nanopore cetera. So it's quite a great solution. And now with also enterprise support, it's very much battle tested, secure and have a level of enterprise support that's needed.
It's, it's interesting cuz back when we were mapping the genome, they were talking about all the potential that this would unlock and, but one of the things we were trying to get to was, Really reducing the cost of mapping the genome and the timeframe it took to Math Genome because we've been talking about this in healthcare for a while.
If we can get to the genome along with the healthcare data and whatnot, we can get to a really personalized level of medicine that we know is possible. But we've been waiting for the technology to catch.
Absolutely. And in fact if many of you guys were at Vibe and Hymns this year, you probably, and maybe health, you probably have heard this wave of revolution of making gene sequencing potentially a standard of care in the future. Map your genome, sequencer, genome, and along with many other data points that we can offer our clinicians to make better data for our patients around the world.
Yeah. In fact, Geisinger is already heading down that path. They're, they're taking. Some of that information in, I don't know how they're applying it yet. I, I need to do another interview with them to find out how that, how that project's going. Talk about some of the partners who are taking advantage of the solution workflows out there.bers, and which now I oversee:
They are a biomedical research platform. They use MONAI and Nvidia Flare, which is a federated learning solution with Nvidia Air Enterprise has already been deployed over 120 some hospital. Qantip one of their solution is used AI for prostate cancer. They use MONAI Core, which is one of the three components of MONAI And they have deployed over a hundred plus some hospitals, let's say Symbios is another one, a precision medicine AI company. They uses MONAI as well to, for their medical AI solution. They have this tumor scope platform that can actually create a 3D. Digital twin that visualizes patient cancer. It's a very, very cool solution. They use MONAI label which is a second component of MONAI to assist with the annotation and that they were able to reduce the complexity of up to 10 different types of tissues. CureAI almost a household name Now in the startup world they uses deep learning to automatically interpret the radiology exams.
So they have actually take on also MONAI the maps aspect of the the solution. So those are just naming a few. And we, within our inception program, we have more than 300 some medical imaging specific ai. solutions Wow.
Invidia inception. By the way, one of the coolest names out there and maybe I'm just referring to the movie, but the whole concept of really creating a whole new class of applications for healthcare and helping them to go from the whiteboard to actual implementation is interesting.
What does it look like to with Nvidia inception for a partner who's listening to this and they're saying, Hey we need to call them right away. This, we need to tie into this. What does that look?
So if you are a startup listening to this, apply for free. Because we have lots of free resources for the startups. We treat every single startup like a developer, and there's either AI technology support or discounts for. Any of the technology platforms and go to market support. We put you guys in front of VCs and also many of the leading hospitals in the world or pharmaceutical organization, if that's your direct target market.
If you are a venture capitalist in the world you can also join our VC alliance within our inception. We have 200, 300 VCs in this space. If you are a partner that is delivering the solution, we absolutely need you because a lot of startups are hoping to deliver their solution into the hospital or many other spaces, and you can be one of our Nvidia partner network.
Partners and we have many different tiers of support in that area. And if you are a hospital, a CIO like the Hill and many others, we also would like to meet with you because we will love to be your trusted advisors to put many leading startups in front of you, so can save you hours and millions of dollars evaluating startups. At a very minimum, we can let you know that which ones are enterprise grade and ready to.
Fantastic. I'd be remiss if I didn't mention VMware here. So, VMware and Invidia have partnered to make this content available and we've done the shows in the past of just the, the partnership and how VMware enables this platform.
They essentially, What they've done for virtualizing the data center, they're now helping to virtualize the AI stack for those CIOs who are listening to this saying what's the connection? It is that platform where we used to provision compute, storage and network, and now we're actually computing AI cycles and, and Vidia GPUs and other things With a stack that your team is already familiar with. So it just, it again, takes that complexity, the support takes that all down. So I just wanted to mention that I probably should have mentioned it in the beginning of the show, I'm mentioning at the end of the show. But I really appreciate VMware partnering with Nvidia on this content.
Oh, yes. I have to say VMware is almost a given. That's part of our Invidia AI enterprise stack. Invidia AI Enterprise actually has been launched more than a year and a half ago. And the new versions is coming out every six months or so. The very first version, we were great partners with. VMware and the fact that actually close to 90% of the hospitals in the US use VMware, it's a no brainer to partner with VMware.
And earlier we have mentioned about Flywheel and others. Many, many healthcare startup solutions are optimized on top of VMware. Ensure. Enterprise grade deployment. So VMware is a fantastic partner and plays a very key role in the healthcare ecosystem. And wanna sincerely thank VMware for opening doors for this, for us and for many of the partners in the ecosystem. And we look forward to continue to do great work with partners like VMware and many others in the future.
Renee, I wanna thank you for your time and sharing this great content and your experience with the community. Thank you very. Thank 📍 you, bill.
What a great conversation with Renee. Preventing disease, revolutionizing analytics, untapping the potential of medical imaging and voice. These are just a few of things made possible with AI, deep learning and data science powered by NVIDIA. So insightful. We wanna thank our sponsors, VMware and NVIDIA for this episode, they are 📍 investing in our mission to develop the next generation of health leaders. Thanks for listening. That's all for now.