Ep 491 Agentic and Physical AI in Medtech How NVIDIA is Changing the Space EDAI.

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When we think about agent AI, sometimes I think people think of sitting behind the computer screen and, you know, maybe helping you get that quarterly KPI report out quicker. When we talk about physical AI, maybe we just think about humanoids in a warehouse, but both of those things are actually so much more than that. And especially when we talk about, I think, highly specialized, very important sectors like healthcare and medicine and med tech, I think that generative AI is, you know, is maybe always not talked about as much as it should in those fields because it impacts all of us. Right. Not everyone here is in marketing. Not everyone here deals with legal. But, you know, we all benefit from what generative AI can do in the medtech space. So that's why I'm extremely excited for today's conversation and back at Nvidia GTC to talk about agentic AI and physical AI in medtech and how Nvidia is changing that space. All right, I'm excited for today's conversation. I hope you are, too. What's going on, y'?

All?

My name is Jordan Wilson. I'm the host of Everyday AI, and this is your daily livestream podcast and free daily newsletter helping us all not just keep up with AI, but how we can use all of the latest news and advancements to get ahead to grow our company and career. That's you. You're in the right place. So make sure you do go to our website at your everyday AI.com, because if you missed some of the great insights from today's episode, we're going to be recapping it all in the newsletter, so make sure you check that out. All right, so like, I. If you're listening on the podcast, you don't see this. If you're on the live stream, you see that we're here at GDC and there's been a ton going on. So I'm excited for today's conversation. So please help me welcome to the show Prerna Dogra, the Director of Product Management for Healthcare AI at Nvidia pr. Thank you so much for joining the Everyday AI Show.

Thank you, Jordan, for having me.

All right, I'm excited for today's conversation, but before we get started, tell everyone a little bit of what you do in your role at Nvidia.

Sure. So I head our product management and the Larva Ecosystems team, and essentially our mission is to translate the amazing technology Stack that we've built with our computing appliance and our accelerated computing artificial intelligence into the most impactful industries, the healthcare industry. So we are really solving for like what are the domain specific problems, right? Healthcare is very domain specific. So as an example, in computer vision, things like cats and dogs videos translate to these MRI and CT1 metric images. So how do we turn that domain problem into a computer science problem and really bring accelerated computing to that? Similarly, when we are thinking about agents, then the big bang of LLMs conversational layer. How do these AI models, the medical jargon, the medical stuff we sit at that translation layer of in Bilia's amazing technology staff or the industry of healthcare and life sciences. And my focus specifically in medtech. And for that we may create new software capabilities because we found something very unique to solve. Or sometimes we adapt our existing stack and really build very intentional good markets with our partners and our customers. But essentially bringing GPU community to.

Yeah, and it's extremely exciting, but maybe help explain to our audience a little bit how does this ultimately play out in their lives. Right, because no one goes to the doctor and sees an Nvidia logo on there. Right. But I'm guessing that many of the major know names in health are using, you know, something along the line, whether it's, you know, GEUs from Nvidia or your software, you know, in the health space. But you know, tell us a little bit, who are those, you know, partners and customers that are, you know, using the MedTech kind of offerings from or through Nvidia?

Absolutely. So our journey in MedTech really started 1200 years ago. And the first application that we identified for accelerated computing was this thing called image reconstruction. But what that essentially is that when you go to get your MRI or your CT scan, it's actually a GPU that can take all of that sensor information and convert it into an image. And so in diagnostics generally, if you talk to these clinicians, they'll say what you can see, you can hear. Right? And all these breakthroughs, like with PET scanning and so on, like how with low dosage can you get these really high quality, high fidelity imaging? So that is one area where it's almost like Nvidia inside all of these devices. Robotic surgery, right. It became surreal. It really came home for me. I always thought of this, right, like not all fast industries, but we're all consumers of healthcare. And my father, two years ago I had to go to a prostate surgery and that was the Da Vinci system powered by Nvidia. And it's a minimally invasive surgery. So really again bringing this ability of keeping care at center and solving with technology what couldn't be possible in the past.

Super, super touching story. Yeah. Like the work that you and your colleagues are doing, being able to see it help firsthand in your father's surgery, that's really, really cool. So one thing I do want to talk about is before we get into the announcements and what's new and what's changing, can you maybe just bring our audience to where are we at today? Because as an example, I didn't know that image reconstruction was essentially just powered by gdu. So where are we at? If you had to give the state of the State address for AI in MedTech, where are we our most large healthcare organizations using Gen AI and AI and GPUs around their entire structure? Or is it still like some companies and some larger organizations are still kind of quote unquote old school and still figuring out this new gen AI wave?

Yeah. So what I would say is because healthcare is so fragmented unlike any other industry, it's not like we have one big layer and if they take gen AI it just becomes ubiquitous. But I would say that gen AI and the promise is really the journey really started with a lot of the perception AI pieces which which have been there for like eight, seven, eight years now. And you can see the growing number of FDA algorithms that are approved with deep learning. But this power of Genai and Agentex is now really coming through. So as an example of epic, I don't know if everybody knows about it, but if you've used your my chart and it is the number one EMR system, it is getting powered by all sorts of these co pilots and really streamline the patient experience. We also announced the integration of Nvidia's technology stack and our models into EPIC on their AI foundry. But that is one direction where once someone like EPIC integrates Genai, it's going to touch everyone's life. And it's as simple as when I open my my chart today, I look at the summary and it's a bunch of jargon, I'm not following. Can I have a patient summary that's in English in the future can I make a call and not be innovating live and even if talk with a digital agent. So these capabilities are really bringing that access to care. We're totally seeing that come through. I think it's a great example. A bridge is again a wonderful example. Right. Like when they have really, they have state of the art technology for Their automatic speech recognition and Dexter speech and this really alien intelligence system where they are bringing back that conversation between the doctor and the patient. Right. Like the last so many years, you're talking to the patient and the doctor is constantly at the screen helping administrative work. The nurses are putting in the codes. So that's a great example. And a bridge is installed and is having a great success rate.

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So when it comes to agentic AI in medtech, could you let us know where are we at now and maybe talk us through some of the benefits or use cases that you're already seeing? Because it seems like last year here at ggc it's like agentic was more of a whisper, but now it's everywhere. So where are we at and what are some of those use cases already in the medtech space?

You hit it on Jordan. Like last year it was so like gen AI and nettech was like, oh, we don't have a foundation model yet and so on and so forth. And that is actually the proof point of what a foundation model means. Right? Because all these companies and to take these models and adapt them to our context and really give AI now agency. So that means not just that the AI model has the knowledge, but it now has the context awareness and we now like this ability to reason through a problem. Right? So if I'm a patient, I'd have to come to the hospital and figure out, hey, what's my care plan? Or how do I get where's the party? Or what's my insurance code? And so on. You have, you can have this agentic AI system that is then calling and knows like I need to call a specialty model from the insurance place, I need to call the specialty model from a nutrition space, I need to pull this out from the emr, so on, right? So what agentic AI has done is taken all this amazing technology of AI, that's the research papers and a model here and model there, but really brought it together to deliver something of value. And so where it is right now, I would say is where we are certainly seeing like 10 examples of bridge now, the great ones for AI, right? Where we are seeing these companies that have set on this journey really taking off exponentially because those systems are performing incredibly well in the real world. But even more importantly, it opens up the avenue for so many more to now come because traditionally medtech has been like a very regulated industry, as it should be. But the bar to bring real world impact from all this technology has been very high. And we just see that this gen AI, agentic AI and now this ability of reasoning and chain of thought which allows to bring a transparency into these systems. The perfect time to really bring value into healthcare.

The benefits I think are fairly obvious. Better healthcare, faster communication, hopefully improved outcomes. But I think a lot of the early talk on hygienic AI was just based around more of like, okay, here's things that I myself would be doing on my computer, right? Researching this, updating this document, you know, sending it over to an internal or external stakeholder, you know, can we talk, you know, with the benefit or when the potential benefit so high with agency AI in medtech, you know, what about the potential risks, right, Because I know people are always worried about, you know, agentic AI then when you get in the med tech space. So can you talk a little bit about kind of the work that you and your team or maybe your partners are doing, you know, to ensure kind of that that agency doesn't go off the rail, so to speak.

Yeah, no, that, that, that is an excellent point and also to the climax. And this is a regulated industry, as it should be. So what we see with our part, like even with these systems and agents, you are essentially with an expert in the right. So the value actually is in the operational efficiency of these systems that they can bring. As an example, it'll help contextualize like Hippocratic AI has built these digital agents that can make a call post operative to a patient and find out how you're doing after your surgery and have you been taking your medications where the patient can ask what kind of dietary plans I should be in one. These are areas that don't happen today, right? So these digital agents and systems can bring care which, where it's not available at all. But then also because they are working these like in case of Hippocratic AI, they have like a whole body of nurses across different systems really trying out these agent digital agents and saying like, does that work? Does that not work? And really giving that time back to the clinicians and the nurses. So in all of these cases with the bridge with Hippocratic AI, the whole suite of these startups artifacts come in. They're all very much with that human expertise in the loop that's really trying to say like, are these agencies well guarded and so on. From a technology standpoint, Nvidia does that. Right. We have in our own. Make sure we have the right software, allow developers to build very safe systems with things like guardrails and so on.

So there's, there's been a lot of talk of the meetup so far here at GTC about kind of this, this three, three computer system. Right. Can you explain that, you know, maybe to our audience that didn't get a chance to listen to Jensen's keynote, which you should all go do by the way. But can you explain what that is and how it's going to be impacting the work that you all do?

Yes, this is truly an inflection point in me. I've been an engineer and technologist at the heart. So you can really see this convergence of three very key computers. Right. The first one is your AI computer. This is where you are training and creating these world class AI models. The second computer that's really come to life is now the simulation computer. Right. And if you think about the simulation computer was actually one huge missing piece. So the AI computer had its breakthrough when we grabbed this notion of like unsupervised learning. I don't need data that's constantly labeled the simulation computer now almost is the AI playground. All this intelligence that I created, right. If you think about it, in the field of robotics, there is no chance that you can hack humanoids and all these robots. Imagine in our industry, like the cost of doing testing and all of that in the real world is so high. So now you almost have this playground. I can be generating synthetic cases, I can be generating synthetic words that are mimicking the real world synopsis, right? That is the second computer of simulation. You take something that you train, you put that into Stimulation when it goes into post training and it's getting really refined. So a model that was trained for task S or foundation model is brought in and now put into simulation to get really contextualizing the new task, which is similar but in a different domain. Right. And the best part of the simulation features, it's not running on wall clock time. Right. So your AI can practice and practice till it really masters this gear. And the third computer, as we like to call, is the brain of the robot that is really the, the real time computer. So we have invested heavily into this platform called the Nvidia Holoscan, which is all about this real time process. And so these three computers, the AI computer to train models, the simulation computer as the playground for this AI tool. And there are systems actually, because it's not just one, it's a combination of AI models and agentic AI frameworks to really bring this sort of intelligence that's just not knowledge, can perceive and act in meaningful ways in the physical world into that simulation computer. And when you think it's a square escape, oh, I think this robot knows how to do a Sutri just perfectly. We could take that and bring it into a real world, what we call centrimeal transfer of a prototype. And the fact that you can go through all these three loops in constant digital prototyping more is what that really means for every industry. And then for healthcare specifically, yeah, I.

Think that was a crucial explanation because I'm sure there's people out there that, you know, when they hear agency AI and then they hear, you know, humanoids, you know, performing surgeries, it's probably helpful to understand that, you know, this is a very well thought out. You know, I'm sure there's trillions of data points that go into this and so many countless simulations. So thank you for, you know, taking us behind the curtains on that one a little bit. But, you know, so, so one question as we wrap up here, because we've talked about a lot in this conversation so far, but, you know, out of everything, you know, that was announced here at gtc, what would you say that, you know, at least, you know, under the umbrella of today's episode of agency AI and physical AI in MedTech, what's maybe the one advancement that you and your team maybe are most excited about? And then what will, you know, that recent announcement here mean for your space in the future? Kind of like, hey, are you able to tackle the new problem? Are you able to offer, you know, a new type of, you know, service for different type of health care organizations. What's that one thing that you're most excited about? Yeah, I know it's hard. Yeah, it's like a two hour keynote. How can you pick one thing?

I want to pick something that wasn't in the keynote but it was in Kuberly Powell's special address. She's the VPNGM for a healthcare business unit and we launched what we call Isaac for healthcare. Right. So it is this domain specialization of the three computers I just walked through and personally I in most excited and passionate about this space because the reality is we have millions of people shot of healthcare side the reality is we have aging parents. We all wanted to be consumers of healthcare. And so it's not just about innovation in healthcare. It is a necessity to really bring physical AI to help in ways was not possible. I was walking the floor floor. There's this company called Wondercraft who builds these exoskeletons for people who are paralyzed. And to see that young woman stand up from her wheelchair and be able to walk that is life changing as it can get. There's this company called Synchron. They designed these brain computer interface implants and they have this whole way of potentially patients with als like how do you take that signal and allow someone who was not able to interact with the world because of the impairments of the so many, give them the agency to be able to do so. To be able to text your wife I love you, to be able to turn on the fan, to be able to tell your caregiver I can't breathe. So this is going to be a long journey. We are aware of that. But we are here because this is an incredibly hard problem to solve and we are committed in our entire ecosystem to take this forward. So like I said, it is a necessity. And so that where my mind, my heart and my entire team focus is going to be to really see how to bring these three computers to life be as they take on the bigger challenge of robotics and AI.

Love to hear it. Just extremely exciting and inspiring to see what's next because I think yeah, sometimes people think oh GPUs and this, that, that perhaps the headlines but what that opens up in terms of capabilities and possibilities just for us as humans, for our health, I think is extremely inspiring. So prayer, thank you so much for taking time out of your day to join the everyday a action. We really appreciate it.

Absolutely. It's a pleasure. Thank you Jordan.

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All.

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