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

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When we think about agented AI, sometimes, uh, I think people think of sitting behind the computer screen and, you know, maybe, uh, 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 and very important sectors like healthcare and medicine and medtech. I think that generative AI 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 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's 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. If that's you, you're in the right place. So make sure you do go to our website at youreverydayai.com, because if you miss 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 said, 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 GTC 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 Dohra, the Director of Product Management for Healthcare AI at NVIDIA. Prerna, 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 at NVIDIA, I head our product management and developer 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 Healthcare is very domain specific. So as an example, in computer vision, things like cats and dogs videos translate to these MRI and CT one-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 AI. How do these AI models sort out the medical jargon, the medical stuff, right? So we sit at that translation layer of NVIDIA's amazing technology stack for the industry of healthcare and life sciences, and my focus is 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 go-to-markets with our partners and our customers, but essentially bringing GPT 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, you know, an NVIDIA logo on there, right? But I'm guessing, you know, that many of the major, you know, names in health are using something along the line, whether it's, you know, GPUs from NVIDIA or your software in the health space. But tell us a little bit, who are those partners and customers that are using the medtech offerings from or through NVIDIA?

Absolutely. So our journey in medtech really started 12, 13 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 cure," 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? That is one area where it's almost like NVIDIA inside all of these devices. Robotic surgery, right? It became so real, it really came home for me. I always thought of this, right? Like, we are all fast-growing industries, but we're all consumers of healthcare. And my father, 2 years ago, had to go through a prostate surgery. And that was the, the Da Vinci system powered by NVIDIA and it's a minimally invasive surgery. So really again, right, 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, you know, being able to see it help firsthand, you know, in your father's surgery, that's really, really cool story. 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 like where are we at today? Because as an example, I didn't know that image reconstruction was essentially just powered by GPUs, right? So like where are we at if you had to give a state of the state address for AI in med tech? Like where are we? Are most large healthcare organizations using GenAI 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 GenAI 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 GenAI, it just becomes ubiquitous. But I would say that GenAI and the promise is really coming to life. The journey really started with a lot of the perception AI, which have been there for like 7, 8 years now, and you can see the growing number of FDA algorithms that are approved with deep learning. But this power of GenAI and agentic is now really coming through. So as an example of ethics, I don't know if everybody knows about it, but if you've used your MyChart, And it is the number one EMR system. It is getting powered by all sorts of these copilots 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, right, once someone like Epic integrates GenAI, it's going to touch everyone's life. And it's as simple as when I open my MyChart app, 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 that I can write? In the future, can I make a call and not be on a waiting line? And even to talk with a digital agent. So these capabilities of sort of really bringing that access to care, we're totally seeing that come through. I think it's a great example. Abridge is again a wonderful example, right? Like when they have really, they have state-of-the-art technology for their automatic speech recognition and text-to-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, it's been you talking to the patient, And the doctor is constantly at the screen typing 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, you know, could you let us know like where are we at now and maybe talk us through, you know, some of the, you know, the benefits or use cases that you're already seeing, you know, because it seems like, you know, last year, you know, here at GDC, 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 med tech space?

You hit it on the head, Jordyn. Last year it was so like GenAI and med tech was like, oh, we don't have our foundation models 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 will 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 can have this agentic AI system that is then calling and knows like, I need to call this machine learning model from the insurance place. I need to call the specialty model from a nutrition space. I need to pull this out from the EMRs and so on, right? So what AGENT.AI has done is taken all this amazing technology of AI that's being researched, papers and a model here and a 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, that is down a bridge, not a great one, but here we are, 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. We just see that this GenAI, agentic AI, and now this ability of reasoning and chain of thought, which allows to bring more transparency into these systems, the perfect time to really bring value into healthcare.

So the benefits, you know, I think are fairly obvious, right? You know, better healthcare, you know, faster, you know, communication, hopefully improved outcomes, right? But I think, you know, a lot of the early talk on agentic 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 with the potential benefit so high with agentic AI in medtech? You know, what about the potential risks, right? Because I know people are always worried about, you know, agentic AI, and then when you get in the medtech 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 agency doesn't go off the rails, so to speak?

Yeah, no, that is an excellent point. And also to the car, like I said, this is a regulated industry as it should be. So a lot What we see with our partners, even with these systems and these agents, you are essentially with an expert in the loop, right? So the value actually is in the operational efficiency of these systems that they can bring. As an example, it helps 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 well? 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 where it's not available at all. But then also because they are working, in case of Hippocratic AI, they have a whole body of nurses across different systems really trying out these digital agents and things like this that work and not work, and really giving that time back to the clinicians and the nurses. So in all of these cases, with Bridge, with Hippocratic AI, the whole suite of these startups, Artifis, they're all very much with that human expert in the loop that's really trying to say, are these agencies, well-guardrailed and so on. From a technology standpoint, NVIDIA does that, right? We have in our own stack, we make sure we have the right software that can allow developers to build very safe systems with things like guardrails and so on.

So there's been a lot of talk of the meetups so far here at GTC about kind of this 3-computer system, right? Can you explain that maybe to our audience that didn't get a chance to listen to Jen Sense and 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. And me, I've been an engineer and technologist at heart, so you can really see this convergence of three very key components, 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 cracked this notion of 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 in the field of robotics, there is no chance that you can have humanoids and all these robots. Imagine in our industry, like surgical robots, 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 scenarios, right? That is the second computer simulation. You take something that you trained, you put that into simulation where 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 contextualized in the new task, which is similar, but in a different domain, right? And the best part of the simulation is it's not running on wall clock time, right? So your AI can practice and practice till it really masters the skill. And the third computer, as we like to call it, is the brain of the robot. That is really 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 3 computers, the AI computer to train models, the simulation computer as the playground for this AI tool, and the assistants actually, because it's not just one AI model, it's a combination of AI models and agent 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 acquired, okay, oh, I think this robot knows how to do a smooth grip just perfectly. We could take that and bring it into a real world, what we call field-to-real 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 in more depth.

Yeah, I think that was a crucial explanation because I'm sure there's people out there that when they hear agentic AI and then they hear humanoids performing surgeries, it's probably helpful to understand that this is a very well thought out, I'm sure there's trillions of data points that go into this and so many countless simulations. So thank you for taking us behind the curtains on that one a little bit. But you know, so one question as we wrap up here, because we've talked about a lot in this conversation so far, but out of everything that was announced here at GTC, what would you say that at least under the umbrella of today's episode of Agentic 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 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 a new type of service for different type of healthcare organizations? What's that one thing that you're most excited about?

Yeah.

I know it's hard. You could learn about so many things.

You can learn about anything.

Yeah, it's like a 2-hour keynote. How can you pick one thing?

I'm going to pick something that wasn't in the keynote.

Ooh, lie.

Right? But it was in Kimberly Powell's special address. She's the VP and GM for our healthcare business unit. And we launched what we're calling Isaac for Healthcare. right? So it is this domain specialization of the 3 computers I just walked through. And personally, I am most excited and passionate about this space because the reality is we are millions of people short of healthcare staff. The reality is we have aging parents. We will all one day be consumers. Of healthcare. And so this, it's not just about innovation in healthcare, it is necessity to really bring physical AI to help in ways it was not possible. And I was walking the show floor and there's this company called Wondercraft who builds like 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 design these brain-computer interface implants and they have this whole way of potentially patients with ALS. Like, how do you take that signal, turn it into an intent, and allow someone who is not able to interact with the world because of the impairments of dystonia, 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 with our entire ecosystem to take this forward. So like I said, it is a necessity. And so that, that where my mind, my heart, and my entirety's focus is gonna be to really see how we bring these 3 computers to life as we take on the bigger challenge of robotics and AI.

Love to hear it. It's 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, Prerna, thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.

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

All right. And there was a lot in there. So if you missed something or if you want to know more about something that Prerna said, It's gonna be in our newsletter, so make sure you go check that out. So if you haven't already, go to youreverydayai.com, sign up for that free daily newsletter. We're gonna be recapping this as well as everything else you need to stay up to date, get ahead with Gen AI. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all.

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