MEDIASCAPE: Insights From Digital Changemakers
Join hosts Joseph Itaya and Anika Jackson as they dive into conversations with leaders and changemakers shaping the future of digital media. Each episode explores the frontier of multimedia, artificial intelligence, marketing, branding, and communication, spotlighting how emerging digital trends and technologies are transforming industries across the globe.
MEDIASCAPE is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. This online master’s program is designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
MEDIASCAPE: Insights From Digital Changemakers
Digital Twins and the Next Frontier in Health Management with Jim St. Clair
What if you could take control of your healthcare future and navigate the complexities of AI and digital breakthroughs with confidence? Join us as we sit down with Jim St Clair, a leading figure in AI and health technology, to unravel his remarkable journey from the military to the cutting-edge world of AI in healthcare. Jim shares how his experiences during the Clinton administration and his work with health tech startups have shaped his approach to integrating AI in healthcare, emphasizing the significance of business process improvement and thoughtful implementation of new technologies.
We dive into the evolving landscape of IT systems and the unique challenges posed by AI, including data security and AI bias. Jim offers insights into the critical decisions organizations face when choosing AI tools and how smaller models can enhance data security. As we explore innovative uses of AI in healthcare, such as ChatGPT, Jim highlights the importance of digital identity solutions and mentions organizations like the Digital Twin Consortium and Health AI Partnership, which are advancing healthcare technology.
The conversation also addresses pressing healthcare challenges, such as the aging population and the shortage of doctors, especially in rural areas. Jim underscores the urgent need for AI-driven solutions and predictive analytics to bridge these gaps. We emphasize the power of individuals taking ownership of their healthcare data, advocating for a cultural shift toward proactive health management. As we conclude, we explore the transformative potential of digital twins in healthcare, setting the stage for a future where technology and personal health management go hand in hand. Join us on this enlightening journey with Jim St Clair!
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
Welcome to Mediascape insights from digital changemakers, a speaker series and podcast brought to you by USC Annenberg's Digital Media Management Program. Join us as we unlock the secrets to success in an increasingly digital world.
Speaker 2:I am so excited to dive in to one of our favorite topics AI and healthcare and coordination and a lot of really innovative things that my guest, jim St Clair, is involved in. Jim, I can't even name all the things that you're involved in.
Speaker 3:I can't name all the things I'm involved in.
Speaker 2:But you started out as a naval officer. Is that correct? You were in the Navy.
Speaker 3:Many moons ago. That's correct, yeah.
Speaker 2:Yeah, and then everything you've done since then has really been about transformation, whether it's financial systems, health systems. So I'd love to hear a little bit about how you got into that space, where your interest is, and of course, now you are a top tech leader. You is, and of course, now you are a top tech leader.
Speaker 3:You know, you're really an innovator. I feel like a top tech leader Just take the praise. That's right, exactly, thank you.
Speaker 2:Yeah, just thank you for being here, and I'm really excited because I do have a number of students who are in the healthcare industry, and so I think it'll be really interesting for them to hear from you what you're doing now. But before we do that, let's backtrack and get to that. You know, how did this become your passion point, where you saw that day but have been both?
Speaker 3:blessed and cursed, I guess, over the course of the last 24 years to become affiliated with transformational efforts of one type or another. The first thing that kind of boosted my career was around the topic of systems resiliency and infrastructure protection, which became a top topic within the Clinton White House, and we're still seeing the ramifications every day now, what we were trying to do back in 1998, 99 and into the turn of the century. I then got associated with financial transformation from a government financial management context because I went to work with a large accounting and advisory firm and their public sector practice in the DC area Interestingly enough, still lots of parallels of financial data transformation and things being done with financial data models and standards that parallel very closely to health care. So the opportunity came up to join PIMS the Health Information and Management Systems Society, which is the largest health IT membership association in the world, as the Director of Interoperability and Standards.
Speaker 3:So I was once again very much heavily in these areas of HL7 and what was the early days of FHIR and IG profiles and WDX12 and all this stuff that are standards for how healthcare transactions and healthcare data move around and got the sickness and the virus for staying in health IT, which, of course, as many of your listeners know and you of course know very well, is a very target-rich environment when it comes to anything to be considered transformational, while at the same time, you know, very structured, bureaucratic and disincentivized to do the wrong thing in many cases, or right thing in many cases. And from there worked with a number of big companies, small companies, spent quite a bit of time in federal healthcare, especially with the VA and supporting VA IT activities, and then got more into health tech and working with a couple of health tech startups, and now working across a couple of different startups and trying to get a startup on my own, dabbling in areas as we've talked about AI and ML, patient-centric record management, patient digital identity, digital twins, etc.
Speaker 2:Yeah, well, it's not just, etc. I'd love for you to really dive into that. What took you from because I do talk to a lot of people who have backgrounds in computer science and data science and have, you know, translated that into AI startups, and is that the same kind of paths and trajectory that you went through? You just saw, this is where we need to be. This is something that's going to be very helpful.
Speaker 3:Yeah, so I'm thinking about that for just a minute. I have no academic background that supports being able to talk about algorithms and Python coding and large language models and the like. The closest I can offer is from a transformational perspective. A couple of companies ago, we established one of the first public sector partnerships with UiPath, which was a leader in robotics process automation. The cool thing about working with them and I offer them in particular is they take the approach, before working with a customer, that you develop a center of excellence around robotic process automation, which really starts with business process transformation and business process improvement, the idea being you know, bring in a bunch of robots if you had bad processes, because now you've automated a bunch of bad processes. And on the cusp of that and some of the work I was doing was this early thing called AI and ML. What I saw as a personal aside was so many cases that it was no longer oh, it's not about robotic process automation and improving your processes. Ai will take care of all of that for you and you can just throw a bunch of junk and somehow, magically, the GPU will calculate the answer you're looking for and you don't have to put in the sweat equity to make your organization run the way it's supposed to. So I've gotten more academically interested and involved with AI and ML where that's going, but still very much feel strongly about these principles and not to ramble on, but they're coming up now in conversations with the groups and coalitions I participate in.
Speaker 3:Healthcare AI, which is it's not just matter of using ChatGPT or OpenAI or CloudSonnet or something like that to try and solve a problem. It's understanding what data do you have and what is the best architectural approach to do that. Do you want to use a third-party system? Do you buy something? Do you build your own? Do you want it to run on your laptop? Is it going to run in your data center, et cetera. And those are all questions that I think we still are wrestling with.
Speaker 3:As we wrestle every day with what AI looks like and designed to run at the local level. There's been more and more work done on these smaller language models that can run locally and, of course, apple Intelligence we're all waiting for is derived around a small model with, you know, a back-end, back-haul capacity to do larger compute in a secure environment. I really think that's where it's going to be. I think the ability to not only optimize your compute capabilities locally at the level of your phone, but, on top of that, have the right data science strategy to know. Within this AI compute framework, I only need to X, y and Z to get what I'm looking for, and then, beyond that, I go to larger frameworks and parameters for other answers.
Speaker 2:Yeah, quick side note my AI ML professor, who created the program at Villanova for my MBA program, was at UiPath.
Speaker 3:Oh, fantastic, yeah, yeah, all great people at UiPath.
Speaker 2:This whole sector for UiPath, so small world. I'm sure there are some other connections I'm thinking about. We'll talk offline, but you mentioned several really key things. What kind of models we're using. A lot of people might not be aware that it's not just about LLMs, but there are the smaller language models and those are really important. Thinking about whether the data is secure right there have been. When we look at California privacy laws, we look at the GDPR, we look at other areas, we see that a lot of data, even in healthcare, has been leaked, has been utilized and shared in ways that it's not supposed to. So with the advent of, you know, overlaying these other technologies, I know that data and privacy are still huge ethical concerns. Data and privacy are still huge ethical concerns. So can these be partially solved by these new technologies? To create more secure systems, to make sure that data isn't going out, that we all have our digital number or digital twin so that it doesn't track back to our specific personhood?
Speaker 3:Yeah, and I guess I'd first pontificate and say, of course there's a great deal of hubbub that was made about AB 1047 being killed in California and Governor Newsom vetoing it, and you know whether it was good or bad. What concerns me from a regulatory framework, control framework standpoint is AB 1047 may have been a really lousy piece of legislation I don't necessarily think it was but let's just say for argument's sake it was. The problem is that we kill these things and the same, quite frankly, california-based actors step up to try and argue about, you know, hindering innovation and stifling developments when they have ulterior motives. And so now there's nothing. And so you know that you argue philosophically about, you know better to have a bad bill than no bill at all. Perhaps, maybe, just maybe, by passing it, we would get to see through and work through what those secondary and tertiary ramifications were. But right now there's nothing. There's nothing in the federal level, there's nothing at the US level, and between working groups I'm involved in in the federal government, federal IT and health care they're each trying to sort out what should we do and what's the right answer.
Speaker 3:This just happened to pop into my mind today as I was participating in a discussion around. You know, I have to concede, and first of all, as background, going back almost 20 years ago, I was an IT auditor and I audited a lot of IT systems using federal NIST standards, iso standards, et cetera, and the premise there was, especially back in those days you had a box, you secured a box, you hooked that box to another box, that made a network, you secure the network, and we're well past a lot of that now, fortunately, or unfortunately, just by the advent of technology. I'll find the links and send them to you if you like about steps now currently taken to limit bias in. Ai actually inject bias in and of itself, so it inadvertently introduces bias in an effort to stop bias. Because of how the bias evaluation process works on top of what a neural network is doing to compute an answer that you're looking for. So it's almost kind of quantum-like, which is well, by virtue of observing the state, you're destroying the state that it's in at the time and it's going to be something different the next time.
Speaker 3:So yeah, that's kind of a muddy answer, but I have been trying to stick to basics and, in several of the groups that I'm in, point out the number of control frameworks that are out there, nationally and internationally, that we have always used for IT and, most importantly, risk management, and say what's the risk associated with your data, with security, with privacy breaches, first and foremost, before using this tool At a very high level.
Speaker 3:There is a tendency for a lot of industries to make what we call build versus buy decisions, which is, hey, can I just go buy a solution that helps me do this? Ai, I think and going back to what we talked about, the smaller models provides you a lot more opportunity to roll your own. You can evaluate potential AI applications in the context of well, what if I had something small that's enclosed entirely within my secure enclave or my secure perimeters of my network and use that and not have to come up with, you know, some sort of agreements for information sharing with open AI to go out and use that model instead, where I don't really know where everything is going or what happens with it.
Speaker 2:Yeah, oh my gosh. I'm over here living my best nerdy life right now.
Speaker 3:Fantastic. Like I said, this is the last minute of the day. We're both kind of chugging along, so it's good to get engaged.
Speaker 2:Yeah, so I'd like to hear about some of the organizations you're currently involved in these startups and what you're trying to solve for each of them.
Speaker 3:Certainly, as I made the mention of Digital Twins, so I do spend quite a bit of time volunteering with some membership organizations Coalition for Healthcare AI, the Digital Twin Consortium, where I'm a co-chair for the Health and Life Sciences Working Group, the Health AI Partnership, which is run through Duke University, another federal-oriented membership association called the Advanced Technology and Academic Research Colloquium, which is ATARC, and they have a bunch of working groups around topics like zero trust and generative AI. And then I'm working on a couple of different companies, including one potentially to do digital identity and what we call zero trust architecture for your patient data and your records. And then, of course, how do we bring AI and ML into that?
Speaker 2:Yeah, so let's talk about what each of these concepts mean for people who might not be in healthcare, haven't really, you know, are just dipping their toes into AI and don't know.
Speaker 3:What's he?
Speaker 2:talking about.
Speaker 3:Yeah, so I think we've made some reference to large language models. That is kind of the bread and butter of most AI ML conversations nowadays. These are kind of the latest iteration of what has been around for a long time. It's something called neural networks, which is the ability of computer processing to sort out relationships between parts of information or to solve a problem. I'm sure many people are familiar with things like being able to guess is that a picture of a cat or a dog and how a neural network you know guesses to that.
Speaker 3:And the latest refinement is really what I refer to as a brute force method of hey, I'm just going to go out and read everything that's available on the internet and use that as inference to guide my neural network in training with things like human feedback and other techniques, to be able to create a language model that gives me answers when I ask it questions. And for anybody who isn't necessarily using something, I'm a big fan of Claude Sonnet and you can get it for free. It's very, very powerful. I use it like four times today, so I haven't paid a dime for it, and the answers are coherent and they're succinct and if you use them a couple of times a day. You'll end up saving yourself two hours in the course of what you would have been writing yourself, so that's a good example.
Speaker 3:Now think forward and say, okay, it's not just going to be me saying, hey, here's a paper summarize the top points of what I should be interested in and what could be done next. But here's all my medical records summarize where you see trends around particular things that I should be concerned about and what kind of medication you might be recommended. I was just reading and shared it with a couple of friends. Just read an article about as a survey being done around doctors using CHAT-GPT and similar large language models that are available, and it quoted one of the patients saying that they were aware that their doctor was doing this. They were doing drug allergies. They just used chat GPT for a drug allergy question and it wasn't a matter that she was concerned about it so much as. Oh well, that empowers me because I could just sit in the comfort of my living room and run my drug allergy query through an LLM just as well as you could, doc. So thanks yeah.
Speaker 2:Yeah, so, and what do you mean by zero data?
Speaker 3:So, going back a couple of years ago some people may have heard of SolarWinds because I think it made it into the media. This was a really big security breach involving a network management company known as SolarWinds. Solarwinds has been super popular for having a really cool product that allows you to manage all kinds of network things in kind of a semi-automated fashion and, you know, it helps provide an orchestration framework for doing things with servers and load balancing and all that sort of thing. What happened was that there were I believe was suspected to be Chinese hackers that were able to compromise the security certificate mechanism that allowed different instances of SolarWinds to talk to their customers' networks and as an output of that, there was more policy in the federal government to do what's known as zero trust. I happen to summarize it that zero trust means I don't trust you and you don't trust me, which means for any given transaction or information sharing, I have to provide you with a trusted means of identity verification that you're willing to accept and, conversely, the same thing, and we also have to agree, excuse me, not only in the parameters of that identity, but that our identity is adequate for whatever it is we're asking to do back and forth with one another and there has been a tremendous amount of effort to try and develop tools and vendor solutions and architectures and strategies to address that.
Speaker 3:I happen to argue that identity is identity. In the DOD, zero trust architecture, for instance, is one of the five pillars that's considered there. I happen to take it a little farther in terms of identity being a case where you know you walk into a library, you find someone you know. Perhaps you have a close friend, an acquaintance, someone you've seen at Starbucks and somebody you've never seen at all. Well, obviously, the close friend. You have established a trust framework that will allow you to start talking immediately about the fact that your cat is sick or your mom didn't make it back from the bar last night at a good hour. Then you have your acquaintance, where it's maybe hey, did you go to class yesterday, did you get notes I can take? And then the person you've seen at Starbucks you've never seen before it may be back to just exchanging names and do you come here often? You've established a trust framework already on the basis of I don't trust you any more than X, and this is what we're going to share in the context of what we've established together.
Speaker 2:Yeah, that's such a great explanation. Thank you, groovy. And the idea of digital twins. So one thing I was thinking of is last time I went to the airport, I never showed my ID. I went through clear, had them scan my eyes. I had you know I'd already had the digital approval or whatever. I know in California you could have your driver's license on your phone cards and Apple Pay or other cards on our phones. So there are a lot of things that we're really moving to towards in this digital landscape where we don't have to carry paper money, we don't have to have our license in our wallet or our purse. So can you explain how the digital twin takes this idea even further and how it relates to health care and how you see it as helping make sure people have more security over their data and their health records?
Speaker 3:Yeah, certainly. And first, to start with, the definition of a digital twin was actually invented by NASA for the Apollo 13 mission where, for anyone who's seen the movie, they come in, they dump a bunch of parts out on a box and say, well, this is what the folks up there have to work with. What can we figure out how to build? And so that's where the term came from, where anytime you create a digital emulation of a physical system, you can refer to it as a digital twin and, as you can figure, with computing power and all that growing as much as it has there is, I've been exposed to a tremendous number of working groups that we have in the Digital Twin Consortium, in engineering and climate change and manufacturing and pharmaceuticals. Where are there ways to create a digital simulation of a physical process? That I go to first, and I think I could be wrong, but I think I heard, for instance, that NASA now requires digital twins to be created for all of their new acquisition programs. So before you begin physically building a rocket, you have to have a digital twin that shows how that rocket works and the systems work, because it's a way to understand. Are there potential failure points? Is it particularly expensive to do one thing versus another thing and work through those problems in a simulated environment, you know, before you build 22 feet of solid steel rock and realize, oops, this doesn't fit together.
Speaker 3:So in a healthcare context, what has been going on to date and I highly recommend the report on digital twins in the National Academy of Sciences, which is free on their website Really thick, but it's free Appendix D is all about biomedical research and digital twins and numerous universities in the US and in Europe have done research to create digital twins, more specifically of human systems, so like the heart or the lungs or other aspects of that.
Speaker 3:And then, of course, specifically for conditions, the National Cancer Institute sponsored the Cancer Patient Digital Twin Program in conjunction with, I think, about five different universities where, using high-performance computing and other resources focused on you know what things for melanoma, which is skin cancer, dermatologically, can we consider in a digital twin model? It was kind of like what you and I were talking about right beforehand, which is, hey, I might have this condition or I've had a family history of this condition. What can a digital twin model for me to know? When I might get it or ways that I can prevent it, or how is it that there are 50,000 other people who fit the similar genetic, physiological profile, demographic profile as myself, and how is it manifested with all of them and things that I could recommend that I do or don't do in order to improve my health?
Speaker 2:Yeah, and to that. We hear about designer babies, we hear about children. You know, and decision making of you want your child to have this hair color, this eye color. Do you think that in the future that this might also become part of? It is like really not just some of the testing that they do for pregnant women, but it'll be really advanced and they'll be able to kind of almost future cast what they're like.
Speaker 3:Yeah, I hope so, and just as late as today had this discussion. I am a very strong believer that and to set the context, we talked quite a bit about the silver tsunami that's obviously our aging demographic, aging population, many of which, wonderfully, also have chronic conditions on average can have up to six chronic conditions per person, and against that you have a changing demographic of doctors and physicians themselves. So you have an older population, rapidly growing chronic conditions that needs care. You have an American Academy of Family Practitioners where the medium age of the AAFP is 55, and that was a couple of years ago. So in about the next seven years the median age will be 65. That means that you know a majority of them will be eligible for retirement and you just won't have those docs anymore for anybody.
Speaker 3:So you have a growing need for healthcare and resources, with a rapidly decreasing and currently unsolvable problem around the number of clinicians. So I said you know the people, the thing we don't. We talked about both of those problems. What we haven't talked about is there's an infrastructure piece there which is right now you and I are used to thinking well, our health record resides at that hospital over there with that doc, and they've got it. Well, when that goes away, that won't be the case and, as I'm seeing right now where I live, in rural health care, there's county after county after county that may not have health care, significant health care facilities. That drives maybe one or two hours away to get someplace that you don't have an OBGYN within 67 miles.
Speaker 3:Those are going to necessitate not only the ability to manage your own health care data and again using that zero trust model for who you share it with and how, but we have to leverage AI to be able to improve things. So when I hear people say AI will never replace docs, I say, well, they damn well better, because pretty soon we're not going to have a doc sitting there. You're going to have to have something or else, you know, we'll be facing the same challenges that developing nations will be in our ability to deliver care. So what can we do? That's a moonshot in terms of leveraging all of this AI and ML and quantum computing and digital twins to develop, you know, predictive analytics, to develop the capabilities to communicate with people at whatever language or whatever level, to convey ways to guide them, to be able to do things, and I just think that's a necessity and I say the clock is ticking for the next five or 10 years to solve it.
Speaker 2:Yeah, if that long. I think about radiologists and how there aren't enough radiologists, and that's where computer vision is really immensely helpful. Of course, you still need the human to look and make sure that AI got it right. It doesn't always, but it's pretty good most of the time. But that it's pretty good most of the time, but that is a great case where AI has already come in to solve an issue that we have and it's just going to continue. And yeah, I think it's probably even less. Of course, I'm not the person who's in healthcare every day in the space, but five to 10 years, I mean, just think about that. If we're not planning for it now, we're not going to be in a good state.
Speaker 3:That's okay. There's a prominent investor influencer on Twitter and he was saying the other day in a really interesting thread about just live three more years. He's committed to saying just hang in there three more years, because with everything he sees, he says three years from now we'll have the genetic breakthroughs, we'll have the technology, we'll have these things and life expectancy will shoot through the roof because we'll have all of these new ways to address things. It's just going to take three years. I think three years is perhaps optimistic, but I think we darn well need to solve the problems in five.
Speaker 2:Yeah, yeah.
Speaker 3:Yeah, that's fair.
Speaker 2:So, gosh, you're working on so many different things. They're separate, but they're interconnected. You talked about living in rural areas. I mean, you live in Mississippi, so you're seeing a lot of need for health care in your area. What are some things that we can consider? What can we, as just citizens, do to help propagate health care systems, to help, you know, advocate for the research that you're doing?
Speaker 3:You know, I think the biggest thing and this affects me and you and everyone else is we just have to culturally, philosophically, take ownership of our own healthcare data. And, speaking from a personal perspective, you know, I know what our family has been through and the way we approach proactively having our data, asking questions about it, working collaboratively with doctors the number of people I have that it's like it working collaboratively with doctors. The number of people I have that it's like, hey, I got diagnosed with cancer. Well, what kind of cancer? Well, they say it might be this one. Okay, what type and stage? I don't know. What do you mean? You don't know. If your tire just went flat, did you look up whether I have a tire? Yet I bet you went to the right store to say I've got a Nissan Altima and I need this kind of tire and stuff, but on Altima and I need this kind of tire and stuff, but you can't tell me what kind of life-threatening disease you have.
Speaker 3:And, without being critical of the individual, it is to me systemic and reflective of the fact that people just haven't, for 10,000 years, thought about ownership of their healthcare data. Around that We've obviously had, and continue to have, a very large population of people who you know. They work out and they eat right, I track my calories or I see what my Fitbit tells me and I forget, and that's fine. But that's what always been one half of one percent of the population. Maybe you know we're talking about doing this day to day in the same way that you do your taxes or you manage your checking account or you know how much gas you have in your car.
Speaker 3:It's the same sort of mental principle, except now it's about knowing you know what's my blood pressure been like and what changes have I had in my food intake lately and is there signs of inflammation and do I have to look at my vitamin K again or something like that. You know, just throwing points out, people don't take ownership of that. They go to a doctor. A doctor, not much different than a shaman from 10,000 years ago, says, oh, here's the vapors you have or here's the ill spirit and this is what you're prescribed to do, and say okay, and then you go back and you take a medication that you may or may not look up the ingredients for, et cetera.
Speaker 2:Yeah, yeah, wow. And even if somebody is living a very healthy lifestyle, eating all the right things, if they don't understand their history, their family history, the data, things still happen to people who are taking care of themselves every day.
Speaker 3:Absolutely, absolutely. And it's funny, I go back almost 30 years with a colleague of mine that I worked with who was a triathlete. He was like 4% body fat and the next thing you know he had to cut all this stuff out of his diet, was eating like whole grain bread and stuff like that because he had dangerously high cholesterol. Even myself I myself all confess is on cholesterol medications, which hit me about two years ago as a guy that's watched his cholesterol numbers gotten tested every year, you know relatively low BMI, physically active, et cetera, and then all of a sudden my cholesterol was like sky high. I'm like, wait a minute, I fundamentally haven't changed my existence and my behavior last year to when you took this, this labs this year.
Speaker 3:And so I said, okay, I'm going to follow the recommendations, I'm going to lose 15% of my body fat, I will reduce my body weight 15%, which I did, you know, do this and this, increase water intake and all that sort of thing, and the numbers were still off the charts. I'm like that's not fair. But now it's all back under control very easily. But again it was. Oh, you know, my wife, I think, mentioned well, your dad had cholesterol problems. I'm like, oh, there we go.
Speaker 2:Yeah, the things that are just unavoidable no matter what we do, but we need to know them and we need to know that we can at least try to mitigate them. And if we have to get on medication, how do you think this is going to change the landscape of personhood, of health care, of all the things that we deal with in life, of personhood of healthcare, of all the things that we deal with in life, to be able to have these digital twins, to be able to take control of our own data and own it ourselves and not just rely on whatever records we have from this healthcare system or this healthcare system if you switch a job or you move to a different state?
Speaker 3:Yeah, you know, I don't know, because I'm pretty cynical at heart with a lot of things and I took the approach of saying, well, we'll have to do this.
Speaker 3:There are so many rosy voices out there saying, oh, someday AI will do this and this and this, and my answer is AI, damn well, better do it, because nobody else will be doing it.
Speaker 3:I'm sad to confess that I have always found that Elysium with Matt Damon wasn't just an entertaining movie. It was kind of a foreshadowing of events, which is you're going to have a larger, larger population in the US and elsewhere, that's, you know, technocratically disconnected from what's going on, and you'll have global elites that have built their own digital twins and longevity medicines and they're going to live in something that orbits the earth with lots of grass and looks really pretty. That's preventable. I don't know if it's preventable, because I think in a lot, lot of cases, we don't want to prevent it by virtual culture, but that is a risk that we take, or perhaps even a variation of it that I see, which is kind of a leapfrogging, which is, you know, the US always known for them and make them healthier and improve various factors that we can't even necessarily envision right now because some tool hasn't been delivered yet. Nothing against Kenya, I just use it as an example.
Speaker 2:Yeah, yeah of course, but it is interesting to think about how other countries are going to play a big role. I mean, we do know that a lot of the tagging for Gen AI, the labeling, is done in African countries.
Speaker 3:Yeah, good point.
Speaker 2:Where people are maybe not making a fair you know they're making a better wage than they could, but not a fair wage for the PTSD that they're getting, because they're the ones who are helping support our use of these technologies. But there might be somebody in that community who does have a new idea and is able to have, you know, a spark. So what is the one thing that you would impart to people who are going on a journey, whether it's getting deeper into AI and how to leverage it, and whether it's, you know, healthcare, education, financial services, whatever their industry might be? And also, as a founder, how much are you seeing competition for funding and for dollars when there are, you know, a lot of people who are trying to create AI driven and technology driven tools right now?
Speaker 3:Yeah, I'm sure everyone's seen about the AI bubble and then all you had to do is stick AI in your name someplace and somebody throw money at you, and I think there's still some of that. I think it's also very Sand Hill Road Valley oriented as a result, but I do see perhaps a bit more equity. So as people come up with creative AI solutions that are well, well substantiated models, with you know, adequate teams and staffing, that they're perhaps getting a shot at things. We're not quite there yet, but are optimistic for next year and there's just a tremendous amount of resources available for free to learn about AI and ML and build things on your own, and I'm sure there's a lot in your audience that has fundamentals experience in computer science and coding and this is just taking the next step.
Speaker 2:Fantastic. And what is the best place? If people want to learn more, they want to dive into your research, they want to connect.
Speaker 3:Yeah, there isn't many great places to learn my research, but I always happy to connect on LinkedIn, communicate there very frequently, share a lot of the stuff going on from other smarter people on healthcare and AI and technology. Also want to give a shout out for health tech nerds. It's worth the 10 bucks a month to be a member of the Slack and you'll work with, I think, one of the finest community of health tech folks out there that you can hang out with, so that's a good one. Yeah, that's a great place to start.
Speaker 2:Fantastic, and on your LinkedIn you do have Jim. You have a quote artificial intelligence is whatever hasn't been done yet from the 70s.
Speaker 3:I found that one day and stole it. I said that is perfect.
Speaker 2:I was looking for something to change for the banner back there and, yeah, and that does seem to me to be kind of the career trajectory that you've had and the interest and engagement you've had in AI and what the future could look like.
Speaker 3:Absolutely, and I do consider much of my intelligence to be artificial, so that works perfectly.
Speaker 2:Well, thank you so much for a lively conversation, for really sharing a robust deepness about where we can go in the world of digital twins healthcare, how we need to consider our data practices, our own healthcare practices, and so much more.
Speaker 3:Well, thank you very much. It has been a pleasure to be here, and I think you've been far too kind in listening to my pontification, so wonderful.
Speaker 2:So Jim St Clair, everybody, digital health, gen AI expert par none, and then somewhere in the back of the bus. And I will be back again with another amazing guest to share on Mediascape Insights from Digital Changemakers very soon.
Speaker 1:To learn more about the Master of Science in Digital Media Management program, visit us on the web at dmmuscedu.
.jpg)
