AI Integration for Human Augmentation in Healthcare

Transcript

Intro – 00:00:03: Regenerative AI takes the center stage. But is your enterprise still watching from the sidelines? Come on in. Let’s fix that. This is Not Another Bot: The Generative AI Show, where we unpack and help you understand the rapidly evolving space of conversational experiences and the technology behind it all. Here is your host, TJ.

TJ – 00:00:26: Hello and welcome to Not Another Bot: The Generative AI Show. I’m your host, TJ. Joining us today is Ganesh, the Chief Executive Officer and Co-founder of Autonomize AI, and the Founder and Host of Stories in AI. You might have seen him, very known face, in the podcast world. From his roots in a small town in southern India to becoming a pivotal figure in the AI industry, Ganesh’s journey is nothing short of inspiring. Adeptness infusing businesses with technology and his deep expertise in data-driven AI solutions have positioned him at the vanguard of revolutionizing healthcare. Ganesh’s emphasis on creating a symbiotic relationship between humans and AI, especially in the realm of healthcare, underscores his vision for a future where AI amplifies human potential. His insights, backed by his wide-ranging experiences, mark him as a distinguished voice in the world of AI. Massive, warm welcome, Ganesh. Good to have you here.

Ganesh – 00:01:21: Thank you. And thank you for all of those words. I hope I can live up to that, those kind words that you actually do. But TJ, great to be here. Thanks for inviting me to this great show. 

TJ – 00:01:31: Absolutely. We’re going to have a lot of good time today, for sure. With the topic in hand, with human augmentation in the age of AI and with your experiences, I’m sure the audience is going to have fun. So my first question, Ganesh, and generally we want to learn more about you before even we go deeper into the topic. The journey from growing up back in Southern India to pursuing your professional cricket and then transitioning into the tech realm is both fascinating. Given I love playing cricket and I play cricket at a good level too, I can imagine the amount of work that goes into it. How have these diverse experiences influenced your approach to AI and its potential to change human lives connection? 

With AI, you’re not building super-intelligent machines, in fact, you’re building super-intelligent human beings whose intelligence is augmented with artificial intelligence.

Ganesh Padmanabhan

Founder and CEO of Autonomize.ai

Ganesh – 00:02:09: Wow, that’s a great question. Now, so look, I mean, if you’re growing up in India in the late 80s and 90s, you know, all you wanted to be was the Sachin Tendulkar or somewhere. You just want to go play cricket. So, I mean, it wasn’t super orchestrated or anything, but I was love playing cricket. I was good at it. And I think I pushed really hard and I did a lot of stuff, played the state league, the Ranchi Trophies and that kind of stuff too, right, in India. In those days, there was no professional cricket at the time, right? So, there was no clubs or anything. You know, you had these local city life clubs. So, it was good. And then always was in technology and was fascinated by technology. We grew up pretty poor, but we had enough. We actually had the side gigs during college that allowed me to buy a computer. And so, we really got sucked into that world of computers, if you will, right, early on in life. But, you know, long story short, I think to your question on how has this shaped my view of life or use of AI. I think, look, there’s a few things that stand out, right? I mean, nobody takes a preset. There’s no one path for living your life. There’s like multiple paths. Everybody takes their own things. And it’s very highly, you know, it’s influenced by so many factors. Your individual desires, the environment you grew up in, 90% of all we are, like there are some statistics, I don’t know the numbers, but 80% or 90% of what we end up doing is the genetic lottery, right? The fact that I was born in India, the post, um, independence era were only that was educated and community, that value education. And understanding that people and pets are going to always be different. That diversity is what makes it exciting to live your life. And when you start thinking about technologies, like AI, I think the one of the profound things about AI, in my view, is the ability to actually say humans are really not great at doing a lot of things ourselves. So we try to do statistics. We actually make opinions. We actually say, okay, you know what? Everybody who looks like you and I are going to be behaving a certain way. The reality is a lot more different in the ground, right? I mean, people like you and I might have very different backgrounds. I’m sure we have a lot of common interests, but very different backgrounds. But AI, as an augmenter of human cognition engine, to now you can actually have, you’re not bottlenecked by an individual who cannot comprehend more than 50 people of the same kind and really look at what’s unique about each of them to shape an experience. You now have AI who can actually drive that level of personalization because, you know, as long as you can throw enough compute onto this thing and you have enough data to actually train the models, you can get to a level of specificity and personalization as previously would not have been possible, right? So I just try to link that thing to what you were asking for. I think the diverse experiences in my life and with people around, when you actually start seeing how this thing is, I think it’s just the, AI is, you know, like there’s that famous meme or a picture, right? Where you evolve from. The amoeba to monkeys to Neanderthals to paleo neanderthals to homo sapiens and stuff and the evolution into robot cyborgs, if you will. It’s actually true, right? Because, I mean, think about lives we live here, kids of today versus kids of when we were growing up, totally different experiences. So part of this, I think this whole thing is a human evolution as humanity is evolving. AI has a very significant role to play. And I’m so, a lot of things happened in the last, six, eight months that really put this in the open, but you and I have been involved with AI for a while. So we’ve seen the potential and the promise. I think now it’s getting a lot more democratized and achievable and attainable for everybody. Right? So. I don’t know whether I answered your question, but that’s my thoughts on this.

TJ – 00:06:12: Absolutely. And democratizing AI, I mean, such a true statement. We used to make that statement in augmenting human intelligence. Well, that’s all becoming true, right? With personalization, with generative AI becoming more human-oriented, whether in your language processing and then beyond. So just perfect in that way. Now, having transitioned from roles at tech giants like Dell and Intel to becoming a two-times founder, including your recent venture at Hanuman’s AI, what pivotal moments or experiences, Ganesh, during this evolution have solidified your beliefs in AI’s potential to Autonomize healthcare?

Ganesh – 00:06:49: Thank you for that question. I think, look, I got smitten by AI a long time ago, the potential to actually, I mean, just the notion of a Turing test. I know it’s a little outdated for today’s world when you think about it, but the notion of actually being able to mimic, specifically use the word mimic human cognition through software and systems. To me, I was always fascinated by the idea, right? And it’s important to understand it’s not a form of intelligence. It’s a mimicking of the form of human intelligence in a software. I think that’s an important element in here. But healthcare. So all this time during when I was at Dell, I launched the convergent infrastructure business. Data analytics was a huge part of the business there. High performance computing was a huge part of the business. We set up several of the sequencing clusters in the underworld. You could always see the potential of AI being an accelerant for human creativity, augmenting human potential, right? But healthcare as an industry, if you just take the stats, right? One third of all the world’s data is produced by the healthcare industry. One third. If you talk to, if you survey a hundred healthcare organizations in the United States, which is supposed to be the leading beacon of technology progress, every single one of them, like majority of those organizations will say they are not even ready for this onslaught. I mean, healthcare as an industry has probably been the furthest behind in adopting, AI to go do it. So all of the promises we’ve made in the last 10 to 15 years around personalized medicine, faster drug launches, lowering the cost of care, improving access to care, I mean, just cost of care, we spend close to a trillion dollars a year in this US administering healthcare, a trillion dollars, right? And then think about a drug launch, any new drug. I mean, there’s so many life-saving therapies. It takes $2 billion in 15 years for these pharmaceutical companies to launch it. There has to be a better way, right? So I got interested, like when we were, I was building my last company, Molecula that is now called FeatureBase, a highly technical product, one of the first open source, open core feature stores in the market. And when COVID hit in 2020 happened, right? There was a lot of things that everything changed. For humanity, everything. We had this moment in time where you saw how fragile human life is, how a civilization is, right? And what are all the risks in it, right? So personally, for me, it was a moment like saying, what am I going to tell my grandkids that I helped everybody shop more and search more with AI? Or should I actually use all of that experience, all of that expertise and relationships and networks to go solve a problem that means a lot for humanity, right? And that’s how I got interested in healthcare. But as an industry, I think, you know, the opportunity is, and it’s not as hard as people make it sound, right? In terms of like, yes, it’s a regulated industry. Yes, it’s actually slow in this thing. But I play this game with a lot of customers, but I ask this question. You have a spreadsheet full of your healthcare data, all your experience, all your medical history, your drugs that you take, and the good ones, and all of that stuff. And then you have a financial transaction history with all your bank accounts and everything in there. Which one would you think twice before uploading to a random website on the internet? And most people will say it’s the financial service thing. Yet, we show up in a foreign country somewhere down the dump or doesn’t have any English characters, you swipe your debit card, get money, walk out of there without worrying about, is my data going to get stolen? My money going to get stolen, right? My point is, technology is there. It’s the adaptation of the technology to the industry that hasn’t happened in healthcare. So, I mean, I can go on and on, but healthcare is an industry. What better calling for a technologist like me and for you, to not apply all the powers of this powerful technology humankind has ever produced, artificial intelligence and AI, to improve the human health condition, right? And I can talk, go more and more about the specifics around healthcare, but there is so much opportunity and so much potential to improve human lives by applying AI in healthcare.

TJ – 00:11:00: So rightly said, and spot on. You know, I think Ganesh, in terms of the regulations are so high, right? I mean, there’s certainly those challenges to deal with. But I think probably, and the more we have been doing research, I think healthcare can benefit the most from all these innovations today in so many ways. I mean, that’s one of the major things, because certainly the regulations and formalities around it. Could you delve deeper into, or share your thoughts on how your journey led you to identify this gap and the vision behind Autonomize AI in bridging it?

Ganesh – 00:11:29: This was post-COVID. We were in the middle of reeling with COVID, the whole humanity. This is 2020. And I was spending a lot of time. I have young kids. So kids were at home. It really shook as a civilization, right? And I was going through my own demons on that process. Like, what am I doing with my life? And stuff like that. And then one thing we realized when we started. So I went back in 2021. I spent a lot of time consulting for several healthcare organizations that I know and I had relationships with, right? And I was hoping to actually go figure out some big problems to go start a company. I wanted to do something in healthcare. I wanted to go abroad. What I learned was not that it was one giant problem that’s waiting to be solved. But in healthcare, had a chipping away from the edges issue, right? You had a lot of problems across the entire value chain, right? And we started thinking through, like, you know, okay, why does it take so long to launch a drug? And you start looking through.

When you remove the barrier to experience reality, and you take the computing environment from outside a screen to life around us, that’s when you’ll start seeing the pervasiveness.

Ganesh Padmanabhan

Founder and CEO of Autonomize.ai

One is the risk mitigation aspect. So FDA has to review everything and do that. So there’s a slowness of that. But then there are pockets within that process wherein saying, look, I have to review 3,000, 4,000 pages of medical history of this patient times 10,000 patients to select 400 people for a trial. And then do everything in my powers to keep them in the clinical trial, make sure they don’t have an adverse reaction, and then launch the drug. Obviously, that process is going to take about 15 years, right? So you start noticing these patterns over and over again. You look at care management. And why does a prior authorization request that you send from a hospital, when the patient is with the specialist, and the specialist actually has looked through the patient’s medical history, they attach it into a document, they send it to this payer and say, approve this prior auth for this MRI scan, and it takes two weeks, and it actually goes in, you get into a medical review, and then people get on the phone, talk about it. The information is already there, right? And then you go to provider spaces, and you have, like, last time you went to a doctor, and the amount of time that the doctor spends looking at your face and having a conversation with you, versus looking at the EMR system and clicking and clicking and clicking and things. I have so many horror stories, my friends just going through episodes of people thought they were entirely healthy, and then they go and suddenly have a heart attack, they have a 99% blockage of an artery. You go and find out that if you really stitch together the time series data that they have for their cholesterol readings, you would have caught this, you would see this coming. But healthcare is very episodic. You go into a doctor, they’re treating you for the symptoms that you have at the moment, on that day, right then. And you know, the most valuable piece of information that healthcare caregivers use today to make decisions, it’s what the patient reports to them. They don’t look at medical history, they don’t look, they don’t have the time to, right? It’s just impossible to do that. So when we were looking and exploring this, there was a few things that stood out. And we wanted to build a company that will really change. I mean, we don’t want to just build another, no offense to all the great people who are doing this, but revenue cycle management app that’ll improve the collections for a provider. And it’s great. What’s in it for the patient? What’s in it for the caregiver, right? What’s in it for the clinician? So we wanted to solve a real meaningful meaty problem. The one thing that we saw across the entire spectrum that was a consistent theme was that healthcare data was three things. One, healthcare data was highly unstructured. Most of that data, 80, 90% of the data is human generated. It’s lab reports. It’s doctors. It’s notes. It’s summaries. It’s visual data. It’s research reports. Highly, highly, highly unstructured. Number one. Number two, a lot of the energy and time was being spent by rationalizing multiple silos of data to make any decision, right? Because healthcare data was incredibly siloed. And silos are like, it’s also industry. You think about all the last 10 years of health tech evolution. There are so many health tech companies. All they do is build one app that is vertically integrated because that’s the only way you can win in healthcare. As a result, you’re exacerbating the problem by creating another data silo, and that data is not accessible for any other decisions that you want to make, right? So it’s highly siloed, highly unstructured, and the most insightful thing that we found was it was highly contextual. So you take a piece of a doctor’s note or a discharge summary. When clinicians reviewing it make a decision on the care pathway for the patient, they look for very different things on the same note versus if you are a researcher who is actually reviewing it to select patients for a trial, right? So, Autonomize was born with two very simple ideas, right? One was, if we can turn all of that unstructured, siloed, messy data into highly contextual nuggets of information to assist knowledge workers to make decisions, we are solving a huge problem across the spectrum, not just in the sub-market. Second is, I think you’ve got to do it in a way that can scale the impact pretty rapidly. And I have so many stories on that, right? And what that means is we don’t want to just build a narrow app that’s just one thing and then declare a victory and build a company and sell it. No, we want to really change the way decisions are being made in healthcare, right? So, and then, you know, the second. The second big thing was actually to do that, we wanted to do it, the only way you can scale is you put the human in the center of everything that you do, be it the patient, be it the clinician, the decision maker, and then augment them than just trying to automate a workflow, right? It’s about augmenting the person within the workflow to do a job better. That was the origin story for Autonomize. We started in clinical trial, evolved into, we’re now serving prayers, providers, and pharma across a wide variety of use cases. Everything that the common thread I would say is anywhere you’re spending way too much time and effort and administrative time and cost to do anything with clinical data or clinical workflows, that’s our sweet spot. So we apply that clinical trials, recruiting. We apply that for safety signal detection for pharmacovigilance. We apply that to look at drug-drug interactions for actually looking at side effects and things like that for trials or other use cases. We use it for prior authorization approval. We use it for care management, case management decisions, or care gap identification for providers and population health companies. So there are so many things that we do, all with the same understanding. The core of what we do is turning the messy, unstructured, siloed data into a data-based data, into linked, structured, contextual data that you can use to make decisions.

TJ – 00:18:02: Got it. Sweet. And probably that’s why you were also hinting towards graph databases, given it does keeps the context of the context and the relationship between the data points, right? So I think it makes total sense to collectively understand what the relationship looked like so that you can reason over the data even more meaningfully. So no wonder you calling about graphs in general. So it’s totally makes sense and it’s a benefit to have that sort of data storage.

Ganesh – 00:18:28: And I know we were talking before we started recording on graphs with your background at Neo4j and stuff too. I think I fundamentally believe in graph being the way you can represent the complexity of the world as it actually is, right? If you look at anything that we do, most decisions are made with some data and some data. And especially in healthcare, for example, you got to have and capture as much context as you can about the data, not just the data. So what do we do across the industry for any decision making? Yeah. So we flatten the data into an X, Y axis. We make it super flat so you can make decisions or run a regression model on it, right? You can’t make impactful decisions by having data in that form. You have to have it in the most innate natural form that captures the complexity, the multi-dimensionality of the data. And that’s where graph databases come in.

TJ – 00:19:18: Yeah, exactly, Ganesh. You know, I mean, the entirety of the context and the relationship within the graph databases is phenomenal, right? That’s where you can extract so much value. One thing, Ganesh, you have definitely once insightfully stated, and allow me to sort of paraphrase that, why is AI a big deal? Because humans are not very good at a lot of things. We make mistakes, we get tired, we misinterpret data, we’re emotional, we have a bandwidth problem with the amount of data we can analyze. Now, with that perspective in mind, Ganesh, what ways do you believe? AI has fundamentally altered the trajectory of human augmentation.

Ganesh – 00:19:56: That’s a good question. You’re right. I mean, like humans are humans at the end of the day. It’s the drama, right? We have drama. We have to get tired. We make decisions without all the data. And some cases, I mean, like a lot of things are, and there’s so much philosophical areas that you can go into here. A lot of that is great, right? But at a fundamental and a core level, we have a bandwidth problem. We cannot process all the data that’s actually being coming in, being pushed into us. We have the level of our brain power, even though it’s very highly efficient, but the volume of data you can analyze at any given point in time is much more limited than an amazing deep learning algorithm that is just specialized to go do that thing. Right. So I think the, in general, I think the argument is like AI is actually a net positive for humanity because of that. The way you think about how can it really help augment this thing is just like, how do you create? And I used to always use this analogy of the Jarvis suit for Iron Man. Wherein you want to have the Jarvis, just another virtually integrated system, whatever it stands for, that will be my eyes. When I can only see through blurry text a little bit, I want the algorithm to go and extend my eyesight to actually go deeper than that, right? If I can only hear certain things and not understand all the nuances of what I’m hearing on a recording or on a video or on a phone call, I want AI to really have these audio models that will extract some more meaning out of that and enrich my understanding of what I’m hearing. Similarly, if I’m actually reading volumes of documentation instead of me combing through like thousands of pages of medical information to understand about a patient’s history, I want AI to just summarize that for me, right? So if you think about almost everything that we do, and earlier my point about how do you mimic human cognition, it’s about think of like I want to perceive, I want to understand, I want to decide, and I want to act, right? The four spectrums. If you can actually make each of that. 10%, 20%, 30%, 40% faster. And more contextual and more richer, if you will. It’ll open up this whole notion about, hey, you’re building super intelligent machines. No, actually, in fact, you’re building super intelligent human beings whose intelligence is actually augmented with artificial intelligence. So they become the super beings, not a sum machine sitting in a data center. I think people have to reframe the way we think about like all this bullshit about the whole risk of AGI. I mean, I’m not saying AGI is never possible. It’ll eventually be possible. We’re far away from it, right? But when you go into a debate of AGI versus whether AI is bad and whether we can build super intelligent machines and we will lose control of humanity, you’re losing focus on the real problem or the real opportunity or the problem is with human agency. Humans want to do certain things. Oh, AI will destroy the world. And the question you should ask is, is why? And AI will only destroy the world because AI doesn’t have agency or consciousness on its own. So it’ll want to have to do that. Which often will come from a set of programmers, one who controls the AI, or the human is in the picture. So full circle, I would say. Human beings have agency. Human beings are smart and the ability, but they also have the flip side. They have the drama. They get tired. They, you know, we get old, we get aged, right? Aging is a big problem. You know, like even recently, the population decline is a huge problem, right? There’s all of these things happening. What AI has the ability to do is put that Jarvi suit around you so we can now extend our perception, extend our understanding, extend the ability to action and to make decisions faster. So you are giving rise to super intelligent human beings with artificial intelligence augmenting their existing intelligence. I mean, that’s the way you should think about it.

TJ – 00:24:15: Love that analogy. So well explained. The Jarvis suit. I mean, totally. How we augment human intelligences. I don’t think anybody has said it in the way you just explained. Love it. And I’ve been in this field for so long, but this is really simple, but so effective. In your opinion, how close are we to experiencing a fully augmented reality where every aspect of our lives, from professional tasks to personal interaction, is going to be enhanced by technology? And two sub-questions to it, if I may, and we can always get back to it, is what milestones or technological breakthroughs do you believe we need to achieve before this becomes a pervasive reality? And are there specific sectors, areas of human experience where you see full augmentation, which will be realized sooner than others, precisely?

There is so much opportunity and so much potential to improve human lives by applying AI in healthcare.

Ganesh Padmanabhan

Founder and CEO of Autonomize.ai

Ganesh – 00:25:05: That’s a great question. Very loaded question. I would start by reframing it a little bit. I have a very different view of… What we call augmented reality. And to me, it’s a definition issue. If you were a kid that grew up in the 1980s, your understanding of your reality is very different than my son who’s nine years old right now. He doesn’t know. I’ll take a simple example, maps. When you drive, I mean, remember when we first, both of us probably moved to this country, we would get these maps and then you map it out and you understand where you’re going. You remember that, you write it down on a piece of paper, you stick it on a ticket, not on a car, you drive, right? Today, nobody thinks about maps. Nobody thinks about it. It’s just there on your phone. You assume it, you take it for granted, right? So our reality is already augmented in pieces, right? So I don’t think it’s a net new thing or a form factor that’s going to take off everything. So to me, the pervasive nature will come when you remove the barriers for people to see that augmentation that is already happening. The apps in the Apple’s virtual reality helmet, augmented reality headpiece, will still be the same maps, will still be the same augmentation for doing understanding object detections from a picture. But you’ve removed the barrier for how they actually have to experience that, right? So the pervasive nature will come when you get these form factors. I mean, I really can’t wait for a newer, modern way of Google Glass to really come in. And the big problem with technologists always think of is like, we try to solve everything. Because, oh, you have to have it as a general purpose device that will do everything. Well, even if somebody actually tells me, gives me a glasses that just does one thing really well, right? I’ll wear it. And I’ll swap that glasses for another glasses if I want to actually think about healthcare. And swap it out for actually looking at, you know, watching movies. I don’t mind that as long as you’re augmenting it. So when you remove the barrier to experience reality that is already augmented without having a screen, without having a mobile phone, and just see it in our natural. When you take the computing environment from outside a screen to life around us, that’s when you’ll start seeing the pervasiveness, right? Which industry is going to see it? I don’t know. I don’t have a prediction on it. I think there is always going to be, I mean, we thought like back in the day, real estate is going to start seeing when COVID happened and people want to experience how do you go around and do a virtual tour. It didn’t really happen. So as much, it didn’t really take off or anything. I think people underestimate how the famous saying, right? People overestimate what happens in the short run and really underestimate what happens in the long run, right? Like my example of you don’t take a look at your maps. For the last 10 years, I haven’t looked at my maps, right? Things have changed and I haven’t realized it. But a sudden new form factor in my hand, I’ll actually say, oh, this is great. It’s going to do all these things, right? So in my view, I don’t even know it’s an answer. My answer is a non-answer, to be honest, right? Which is, I think augmented reality is around us. It is how you experience it that is going through a transformation. And I don’t think people will notice the big ticket moment in that thing or anything. So maybe it will happen. But I see it as more a gradual thing where you start seeing parts of you getting this aggregation of content and things that happen. Now there’s going to be that aggregation that will happen to have your better experience, I guess. And I was kind of rambling towards the end.

TJ – 00:28:43: Ganesh, thanks so much for the insights. I would love to know some of your closing thoughts. As we envision a future of augmented humanity, and given all the discussion we just had, what do you think is the ultimate potential and what could be the defining moment that signifies we have truly integrated AI, generative AI, form of AI? To enhance our evolutionary journey. And while there’s immense excitement around AI and human augmentation, are there any red lines you would suggest we don’t cross? No matter what the potential benefits are. And that will be our final question for today.

Ganesh – 00:29:19: We have great questions. I think what’s the ultimate opportunity here? Look, I had a friend, I’ll share a story. I have a friend of mine who talks about it in a different context, but I’m paraphrasing what he says, but he was telling me, I would love it if all I had to do is sit in front of my TV or some augmented thing and do bong bongs as long as everything that I need to do on a daily basis is already happening in some way or the other, right? So the future, I believe, for humanity is going to be almost everything that we do, if I can automate, and there’s a future to me, is these autonomous agents who would do these things. It’s not going to be one giant robot that’s going to do it, but you’ll have intelligent pockets of intelligent systems that can be used to automate this thing. Hence the word, I mean, like even with our vision or Autonomize, Autonomize is all about autonomizing the world, right? It’s about making sure you’re building this intelligent, autonomous. Agents that can actually enhance our human lives and human protection, the red lines, the challenges and the risks, right? And I go back to this thing saying it is no different than any other piece of technology that we have invented. It’s mostly not different. Two specific things that we have to really watch out for. One is… The scale of impact of what you do with AI, it just brings it out to a whole new level, right? So scale is something we’d always be aware of with AI because AI has the potential. Cathy O’Neill’s Weapons of Mass Destruction, Math Destruction, talks about the same. What’s different with these algorithms is not like, you know, earlier bias was very much. Limited to the person making the decision. Now, if you take that bias and encode that into an algorithm, Every decision being made is going to have that bias, right? So the scale is one thing we have to really think about. The second part of this is to not forget to hold the human agency holders accountable, right? It is not about the AI. It’s about the humans. It’s about the organizations. It’s about the companies. When a really mature AI early leader in this market is going and making a recommendation of how they should govern AI to the regulatory bodies because they have the power to do it, it’s the classic textbook case of regulatory capture that they’re trying to do, where they’re trying to price out everybody else, the amount of resources they have to go make this happen. Don’t let that blind you from what you as a regulator or an organization, put the human back in the picture. Who’s building the solution? Who’s going to be impacting? What is the intended or unintended consequences? Hold people accountable, not systems and AIs. So those are the two things I would just call out on this one.

TJ – 00:32:08: Awesome. Well, Ganesh, I think this has been a phenomenal discussion in terms of how you were able to channelize the ethical concerns, the entire healthcare industry, and what the potential benefits could be when we will have an integrated AI system to kind of the red lines. I think it’s super critical to understand in today’s day and time, especially when enterprises are trying to fit every use case into general AI or beyond, like just trying to run towards one goal. I think it’s so important to have that clarity, Ganesh. So I wholeheartedly thank you for bringing that. I truly enjoyed this conversation.

Ganesh – 00:32:42: Of course. Thank you so much, TJ, for inviting me to the show.

TJ – 00:32:46: It’s been a true pleasure, Ganesh. Thank you so much.

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