Everyday AI Podcast – An AI and ChatGPT Podcast - EP 594: Data Dreams & Digital Delusions: The role of AI in health tech

Episode Date: August 21, 2025

Will more data solve AI hallucinations? Maybe. But what about the industries that have the most to gain (and lose) from AI transformation like healthcare? Join us as we dive deep into the role of data and transformation and what obstacles the healthcare industry still has to clear to turn their digital delusions into data dreams. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Trillion-Dollar Data Center Investments for AIGenerative AI Transforming Health Tech Use CasesAI and Physician Burnout SolutionsOrganizational Challenges: AI Literacy and EducationData Quality, Cleanliness, and Health Tech OutcomesTransparency and Accountability in Health Data PipelinesImportance of RAG (Retrieval Augmented Generation)AI Hallucinations and Patient Safety RisksOpen Source AI Models and Health Data PrivacyFuture Impacts of Large Language Model InvestmentsTimestamps:00:00 "Tech and Healthcare Evolution Insights"06:50 AI Investment and Data Challenges in Ghana09:48 Ensuring Accurate, Real-Time Data Retrieval13:18 "Building Transparent GenAI Models"15:54 Prior Authorization Ensures Claims Accuracy18:34 Responsible Data Management Challenges22:45 AI Data Accuracy and Consequences25:04 Data Transparency and Accuracy ImperativeKeywords:Generative AI in health tech, artificial intelligence in healthcare, large language models, data center investment, AI hallucinations, data quality, retrieval augmented generation (RAG), physician burnout, AI-powered clinical notes, medical imaging AI, HR workforce optimization, health tech digital transformation, healthcare data privacy, healthcare data security, AI model training, AI education, data literacy, operational efficiency in healthcare, sustainable data centers, environmental impact of data centers, transparency in AI, data trustworthiness, real-time data extraction, cross-checking AI outputs, patient outcomes, healthcare regulations, high-stakes AI industries, open source AI models, AI supply chain management, healthcare claims denial, prior authorization automation, grounding AI models, human-in-the-loop in AI, data accuracy, precision in healthcare AI, medical decision making AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. The largest companies in the world are spending trillions of dollars,

building out data centers for artificial intelligence. You have single companies investing hundreds of billions of dollars, hoping that more data and maybe even better data will make AI exponentially better. But is that a reality? Or is that a delusion? And especially in high stakes industries like health. So today, it's going to be a fun conversation. I can't wait to have this.

We're going to be talking about data dreams and digital delusions, the role of AI and health tech. This is going to be a good one. Trust me. Strap in. It's going to be fun. All right. This is your first time. Welcome.

This is Everyday AI. My name is Jordan Wilson. And welcome. This thing. It's your daily live stream podcast. and free daily newsletter helping everyday business leaders like you and me, not just keep up with everything happening in the world of AI, but how we can leverage all this information to actually grow our companies and our careers.

If that's you, you're like, hey, that's what I'm trying to do. Cool. Starts here with the unedited, unscripted livestream podcast. But if you want to take it to the next level, make sure to go to your everyday AI. Sign up for the free daily newsletter. We're going to be recapping the highlights from today's conversation. So if you're out jogging on the treadmill and you, run out of breath and you're like, wait, what did they just say?

It's all going to be in the newsletter. So make sure you go check that out as well as today's news is going to be in there as well. All right. Enough chit-chat. Let's talk data, not just data because we know we all need it for large language models, for generative AI for our companies to leverage that technology. But what about just these wild investments in these data centers? And is it ultimately going to get rid of hallucinations?

And what does that mean for these high stakes? industries. All right. You don't have to listen to me, chit chat by myself. I'm excited for today's guests. So please help me welcome to the show. There we have our Smerti Kuraban Nandan. Smurdy, thank you for joining the Everyday AI show. Thank you for having me. All right. Tell us a little bit about your background. So you are a health tech executive, but tell us a little bit kind of on your expertise. So Smirty, Simi, two names, based in Los Angeles, been for a couple of decades, but background by education is primarily in robotics and public health, also recently completing my master's in data science because I figured that's where the world is going.

We all are geeking out on data. Might as well jump on the boat. But really spent a couple of my decades working in startups, product companies, designing UI, going to market, working on the growth side of these products, and then eventually got into the consulting world with obviously the top fortune companies, working on digital transformations, especially for health care payers and providers, and also, you know, the blend of health where it meets every industry.

So retail health, health tech, digital health. But it's quite fascinating to me, especially now the world we're living in with AI and Gen AI, the implications of that, the usage of that is quite large and quite expansive. But obviously today we'll talk about, you know, the two sides of the coin, which I'm excited to talk about is the investments in Data Center,

but also as an individual, even divorcing me from what I do, I mean, truly, you know, getting the right information at the right time. Yeah. So obviously health tech is nothing new, right? And artificial intelligence in health tech is also not new. But, you know, bring everyone maybe up to speed before we dive into the details. How has generative AI changed the health tech scene?

See, I think in many ways, right? So one is using Gen. AI in certain use cases, maybe, for example, physicians using Gen. AI could be prescribing for clinical nodes, obviously a very positive change. Given our physician burnout, right, a couple of years back, we had 800,000 physicians. Today, I'm going to say we have 600,000 physicians. So we're dropping quite a bit, right? But systems, whether using Gen. AI to help them write clinical notes, verify medical imaging, is obviously helping them accelerate the job so they can see more patients with greater quality than go through a major burnout, right?

So that's just like one use case. But we are seeing implications of AI and Gen AI across the enterprise. It could be for HR workforce optimization. It could be as simple as, you know, note taking which has shared before. It could be, you know, in marketing, where you're creating images and documentation. It could be legal. So the implications are enterprise-wide, and it's quite helpful if done the right way. So that's kind of by seeing a lot of momentum and, you know, kind of changes being lamented.

All right. Let's maybe skip to the end a little bit here. When we talk about these just insanely large investments in, you know, infrastructure around AI, you know, you have individual companies, multiple of them, investing hundreds, hundreds of billions of dollars into these projects. why? And is this ultimately in the health tech space going to mean safer, better outcomes for patients if it all works out how big tech companies want it to work out? You know, great, great question. I feel like we'll have, we'll need hours or years to actually get to the bottom of this. But see, I think some of this is a business imperative, right? This is a business decision which each company is making. in order to be ahead in terms of the Gen AIs, just as a competition in the market, you, by just general, need access to data, right?

So data centers is a natural investment. All these companies are making. But given the current administration, the regulations, obviously trying to move the workforce to the U.S., have the job market to be really strong here. Obviously, the investments in the U.S. have been large, but that's also globally. Like I found this news that there's a big investment just made at Ghana

for, you know, Africans and that population to, really come up to speed on AI and data centers, right? So the investments are being made overall for the right intent and the right reasons. But that comes to the other side of the equation is that how much of this data is truly clean, how much of that is truly modeled. And then on the other side, users like you and me and just everyone else, even data scientists, how many of them are actually prompting right, how many of these answers are right? So there's a little bit of, you know, disconnect, I would say, between what's being trained, what's being prompted, what's being coming out. And obviously in healthcare, the weightage of those outcomes are really,

really high stakes. Yeah, you bring up some great points. And, you know, I won't name drop or shame drop, I get anyone by name. But I've been taken aback by even larger companies making just giant investments, seven, eight, nine figure. investments that still don't push literacy. They don't push AI education, right, but they're investing sometimes hundreds of millions or billions of dollars. How have you seen that play out in the health tech side? Because I would assume it's no different, right? It's one of the, I think, one of the more, you know, appealing maybe sectors that has yet to fully get cracked by AI. but how do you see it shaking out on that side?

So let me answer this two ways, right? One is the personal side. When someone is investing in data center, I wrote a piece just maybe last year on just the sustainable impact of data centers, right? There is a high cost on environmental pollution, on just mental health, physical health, and all those things that happens,

the creation of the data centers. So one call to action is obviously to make a responsible ecosystem investment, not just in terms of data and what the business outcomes are, but also what does that do for the particular region society in terms of health, right? So that's key. That's a public health call out. The second one is how is it truly playing out in healthcare where people are using a shared

earlier that physicians are using it, physicians are using it, healthcare management is using it. We're using it across, say, supply chain, but we want to see what is happening in the inventory for transparency, resiliency. We're using it in FNA, which is finance and accounting to truly create. you know, payment transparency across different models, give people access to, you know, the payers, insurances. So there's quite a bit of use cases on where Gen.EI is being used. But obviously, the three stacks where I see where leadership is using it is one cost takeout, operational efficiency, obviously reducing burnout for physicians and clinicians.

But overall, I think some of this is being done with the right intent of augmenting and doing the right things, right? But then teeing back to our conversation about delusions, a little bit of hallucinations, how much of these models and answers that we are retrieving is accurate, right? So some of the things that I do advise clients and, you know, work is how do they implement a rag, which is retrieval augmented generation to make sure that the data that they're extracting is from the right data sets and is being cross-checked live and is not just, you know, from the abundance of stored data that is not real. So it's very important that the extraction that's being done is real-time, real-time answers,

real-time analytics, real-time web sources. And even that, before we jumped at the call, you and I have a discussion, an example about organic fruits and vegetables. When we go to the store, it's tagged organic, right? But do we know, is the soil organic? Is the fertilizer organic? Was it grown organic? We have no clue, right?

It's a similar example, but the weight age and the, the weight agent, the high stakes of health care is really high because, you know, you're dealing with lives. So it's important for us to really go back to what is the source, what is the process, you know, what is the end outcome? So, you know, my ask is obviously investments are important, right? But when they're making these investments, make the investment to your point on data education, what does the process look like, how much are you investing in RAC, in the guardrails of the fame work and security. And then the, obviously, the end outcomes, which is, you know, know, you and I or even a patient viewing that data, what are not just the information outcomes,

but the mental and physical outcomes of reading that data, you know, I think it's a heavy pull. Yeah, so using your organic fruit analogy there, how should maybe a healthcare executive when they're looking at, you know, maybe they've had a successful pilot of Gen A.I in a smaller scenario and they're looking at a wider rollout. How should they be examining that fruit, that large language model to make sure it's organic? How should they be looking at that large language model to make sure the data is maybe more real than delusion? So let me share an example. I was with a friend having a glass of wine many months back.

And, you know, he shared that there, you know, honesty is great, Simi, right? Honesty is saying, Simi, I ask you, where did you go for dinner? And you say I went to X for dinner and it was with friends, right? That's honesty. But he said, what's really true and authentic is transparency. If you said I went to Javier's and I met Jordan, Eve and Alice for dinner, that's transparency, right? So at that point of transparency, you build trust, you build credibility and you own accountability, right? So that's where the moving of the needle, right? I think organizations by large are honest. But if they're transparent, then then that's when if they're able to show consumers and clients and everybody else,

what are the investments at data centers? What does the investment look like? Break it down to us, right? Have some kind of a transparency on what that's happening. What data sets are they truly using? Create like a transparent, like a pipeline on the training. What's being trained? How often it's being trained?

And then the entire process pipeline. So if I go in and I'm like, okay, these are the data sets that's being pulled from. that's how efficient those data sets are, that's how accurate those data sets, that now you're building trust. And then the entire pipeline of how is it reasoning, right? Because as you know, there is a chain of thought when a Gen. AI is reasoning.

How actively and accurately and how fast is the reasoning happening? I think seeing that, like creating this very glass model of Gen AI, I think would be incredible, right? And then the prism light, if I look at it, is like the different outcomes that produces. Then the accountability goes to a person on how they want, want to use the data, how do they want to trust it? And how quickly do they want to keep prompting to train it further? Right? So I think that would be my ask, creating this glass model of

Gen. AI. So you had just mentioned a little bit about RAG, retrieval augmented generation, right? And I don't know, at least for me, and maybe it's just because we're getting overagentified, but it seems like people are maybe talking about or focusing less on RAG, right, which is probably more bad than good, but that's beside the point. It seems like people are just hoping that, you know, the scaling laws of large language models and larger context windows, right? They're going to get, like, get rid of the need for RAG, right? Can you talk a little bit about, like, is that a good or bad idea? And then specifically talk about maybe the importance of RAG on the health tech side. I mean, just for people listening, right?

I think RAG is when you prompt something and then the system is actually actively checking from live resources as you type in, and it's not pulling data from something stored, you know, 10 years back or 20 years back. So it's more real time, it's accurate data, it's live, which is more trusted. So, you know, I personally Jordan believe that everything in life needs a checkpoint, right? Every process needs a checkpoint. And it's healthy for especially a data. data-driven system to have a checkpoint, which is what RAG is in a very simple way, right? It's a checkpoint to make sure it's accurate, it's live and everything else.

So regardless of how models are being trained, unless that's a modern version of, you know, rag, I think that'd be great. But I think it's very important for us to get grounded. That's what Rack does, right? The grounding prevents hallucinations. Without the grounding, we're all just taking information and becoming these bodies of misinformation because perception is a reality. What we read is true.

Like, how much time do you and I have to be cross-checking models and figuring out if the answer is right, right? So I think the responsibility and the imperative goes on the leader is to create that rag as a grounding as an important pillar to be able to do what they're doing, especially in health care. Because implications for, say, even a physician, health care management, to make a decision, let me say an example of prior authorization, right? Because claims denial is one of the key issues, but patients don't get the right care at the right time. time. But if the rag is there, it grounds them a reality on the claim, approves it, disapproves it, whatever. It's a very trusted source and forms as a sense of truth. Without that, you know, the claims can be denied. It could be on a base of wrong data sets, wrong prompting. So it's almost like how do you, it's kind of a mediator that holds both parties accountable and holds like a

source of truth. So to me, I think, I think it is important. So, you know, as we talk about data investment and I think that everyone wants better data, right? They might not ultimately know what that means or how to get it. But, you know, what, and I'm not asking you to look in your crystal ball here, but presumably, right, you have as an example, the Stargate project, a $500 billion investment in data centers for AI, right? And we already look at the level empower and complexity of these models, like prior to these, you know, $100 billion investments. What might this mean for the future of AI, its applications, and the data quality? I know, you know, I'm not going to, you know, put this, put this on a wall somewhere and come back

in five years and see if you were wrong or right. But, you know, I think we always have to be thinking ahead. So how do we think ahead about that scenario? So I love the question. And one, I think we should connect in five years and see where the world is gone. But I think, see, I believe that data is very important just overall in life to make the right decisions, right? It empowers people to make the right decision. So the availability of this amount of data, I think the other problem we have is like it's too much data now.

We don't know, right? It's kind of like it's like kid in a candy store. Now, the kid has unlimited access to data, but too much sugar leads to diabetes and other issues, right? So that's where I struggle with, quite honestly, as to how much data is good data, and then how do you truly make sure this is responsible in execution of data? So I think to that point, I think we have to make responsible investments and also figure out, is that a way where we can control the release of data and data sets, and then what does that look like?

As long as we're still in the process of refining the data sets, making sure the information is accurate, because we still don't know. I mean, you know, you and I discussed, are there bad actors creating fake data sets to confuse the system? Possibly, right? So until we get at the point of, you know, a trusted way of really managing these data sets and extracting them, I think it's a very long and general process. But the good part of this is I'm hoping that especially in healthcare, all this data helps, you know, research. It helps make the right decisions, improves physician burnout.

obviously improves the job market, really helps people out. I think that is obviously tons and tons of hope and the goodness it also brings, right? But with great power comes great responsibility. I just don't want people to forget that. Yeah. Yeah. And, you know, at least, you know, I'm always looking when I'm thinking about the future of AI, right? What's after the next model, so to speak?

And what might that mean for certain sectors? And, you know, my very little understanding of the, you know, health tech. scene, right? I was, you know, I've got to talk to super smart people before, you know, the head of the AMA and some other great people. But it seemed like earlier on, you know, in the first, you know, year or three of the Gen AI phase, right? So many bigger health organizations just didn't get on board with AI, you know, just because of data security, data privacy, you know, PHI, all of these things. But now as we look forward, okay, we are at a point today where you have open source models from OpenAI, right, with their new GPTOSS. You have new variants of, you know,

Gemma's three. So you have models that are open source. You can download them. You can run them on-prem, no internet, anything else, right, that are more powerful than what we had 18 months ago. How might this change? Might we see a big surgeons of, you know, kind of open source usage, you know, in the health tech side? I mean, another great question. I think, see, the one thing you mentioned is data privacy is very, very key. And the responsible use of data is also important. So, I mean, that could be, you know, extreme search where everyone's using it and implementing it and, you know, using it different use cases.

So I don't know if I know the right answer to where, you know, the trajectory of this goes in health care. But because health care, unlike other industries, touches people's lives, right? this is not retail. I mean, obviously, they touch people's life, but not, you know, the actual lives. I think going slow to go fast is, it is important. And hence, I do see why health care is behind in terms of the implementations and using it, because it does impact life, right? One wrong outcome or a decision can change all, you know, save lives.

So I think that's where I'm always a little caution about promoting or sharing, be like, this is great, let's all use it in health care, because we don't know what we don't know, right? Yeah, that's a great point. And even kind of related to that, right, there's obviously with today's models, if, you know, I'll rewind and stop asking you questions five years down the road. But when we look at today's models, hallucinations are still very much a part of everyone's day-to-day. How do hallucinations, especially hallucinations that maybe get unchecked or kind of go to production on the health,

how does that impact, you know, research, you know, patient quality of life and just the overall direction of health companies and how can they better deal with hallucinations when they can be increasingly difficult to spot? So, I mean, this goes back to having the transparency in the pipeline, right? Where is the data being extracted from? That is the first step, right? The second one is understanding is the data that's being extracted from the prompt? Is it life? Is it accurate? Is it from a web source? Like, are those sources credible? That's why Iraq comes to place. The grounding of that is key. But the implications and the high stakes are high, right? So say someone puts in an MRI and x-ray saying, does this person have, say,

cancer? And say the AI system is hallucinating and says, yes, the potential of this person having cancer is 80%. And it's wrong. Think about the mental, the physical, the financial burden on the patient, on the provider. And let's talk about the other side of just the payers and providers fighting about utilization and people losing Medicaid and Medicare. So, you know, bringing this kind of a hallucinated decision and outcome in a very complex, turbulent time can have severe implications on just the patient, right? That's why I keep saying, like, this is very serious when it comes to patient outcomes

because we do have to take it seriously. So in order to avoid these hallucinations, that's why I believe we need to go slow to go fast, right? We need to have that transparency to check the data set, to have the rag, to double check given models, right? Like, I don't want, you know, it's okay to go an extra mile to make sure it's right. Check with three different models to see if you're getting the same answer, right? And then, and then make an informed decision. But you should also keep the human in the loop, right? We are all there because we are educated, we're there for reason, especially physicians.

So I think having that ecosystem of cross-checking, having the rag, having human in the loop, I think it still will always be important. All right. So we've covered a lot in today's episode. We've talked a little bit about the current and future role of AI in health tech. We've talked about these large data investments and how they ultimately may impact data quality in future large language models. But as we wrap up, is your one most important takeaway or piece of advice for business leaders in the health tech

space still grappling maybe between the data dreams and the digital delusions? I think one key, maybe couple will be obviously transparency over honesty. Second one, you know, is truly creating being responsible about the data that's being released, but also constantly training and making sure it's accurate. I think it's just, you know, accuracy and precision is going to take over, you know, most of these concerns, especially in health care. So I think those two will be my key, key important pressing points for data. All right.

We covered a ton. And I think that this was a conversation that was very much worth having because I think the innovations in the health tech scene is going to be exploding in 2025 and beyond. So this was a great, I think, conversation. to have. So, Samirty, thank you so much for your time and for coming on the Everyday AI show. We really appreciate it. Thank you for having me, Jada. Appreciate it.

All right. There was a lot. We covered. If you missed anything, it's going to be in our newsletter. So if you haven't already, please make sure you go to your everyday AI.com. If this was helpful, tell someone about it, right? We bring on some of the world's leading experts so you can get your questions answered. Thank you for tuning in.

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