When AI Spots What Doctors Miss w/ Steve Brown, Founder & CEO, CureWise

Modern medicine generates more data than any one clinician or patient can reasonably process, and for people with rare or complex conditions, that gap can mean delayed diagnoses, missed options, and life-altering consequences. As precision medicine advances, the challenge is no longer just having information, but making it understandable and usable when it matters most.

Steve Brown, Founder & CEO of CureWise, joins HealthBiz Podcast host David Williams to discuss how AI helped uncover his own missed cancer diagnosis and why patients need better tools to understand their own medical data. 

🎙️⚕️ABOUT STEVE BROWN
Tech innovator and AI expert with a passion for creating impactful solutions. As a developer, he's built numerous apps and led two startups to successful acquisitions by global companies. As a filmmaker, his award-winning documentaries have been featured in theaters and on major networks. Driven by the power of media and technology to transform relationships and make a lasting impact.

🎙️⚕️ABOUT HEALTH BIZ PODCAST
HealthBiz is a CareTalk podcast that delivers in-depth interviews on healthcare business, technology, and policy with entrepreneurs and CEOs. Host David E. Williams — president of the healthcare strategy consulting boutique Health Business Group — is also a board member, investor in private healthcare companies, and author of the Health Business Blog. Known for his strategic insights and sharp humor, David offers a refreshing break from the usual healthcare industry BS.

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Transcript

David: 0:00

What happens when a patient uses AI to solve a medical mystery that top physicians miss? And what does it mean for how we diagnose, treat, and manage serious illness? Hi everyone. I'm David Williams, president of Strategy consulting firm, health Business Group, and host of the Health Biz Podcast, where I interview top healthcare leaders about their lives and careers. Today I'm joined by Steve Brown. He's a technologist, entrepreneur, and the founder of CureWise. After surviving a devastating wildfire and developing baffling symptoms, Steve was told his condition was everything from stress to indigestion. When he built an AI medical agent and fed it the same data the clinicians had, it flagged a rare and lethal cancer they've missed. That agent helped guide his treatment and it worked. He's now turning that personal breakthrough into CureWise, a platform designed to give patients access to AI driven insight and a virtual tumor board of their own. Do you like this show? If so, please subscribe and leave a review. Steve, welcome to the podcast. Great to be here, David. So that's a heck of a story. Sounds like a, a good one so far. Um, you've got a long career as a technologist, and I'm wondering what was your relationship like with AI and healthcare before becoming a patient?

Steve: 1:23

Well, I have ai, healthcare patents that have already expired 'cause I was, had been in this field, uh, a long time. Um, in fact I was in it for such a, a long time that I left for a while and became a documentary filmmaker for about 10 years.'cause I was sort of tired of the glacial pace of, uh. Health, the healthcare business. But my original, um, uh, business actually right outta college and then the first startups were in, um, chronic care, um, looking for the biggest problem in the biggest industry. And, and we started what became the, I'd say the pioneering remote patient monitoring, uh, chronic disease management technology company. We won the national contract with the va. We did the work to get that data into, uh, to to, to Medicare and to Congress, and, uh, uh, got into Medicare and kind of paved the way for, uh, a lot of things that now are kind of commonplace. Uh, but it was a long haul and, and it was a lot of work on, uh. Other people's problems. But, uh, when I kind of got dragged back into healthcare, it was, uh, via diagnosis and, um, I, I, I realized that, uh, things are a little different now in healthcare because AI is really changing the game. Uh, we were too early for AI before, but now it's, uh, it's just having a tremendous impact on all aspects of care.

David: 2:44

Yeah. Now is the time. I think there's a jazz number called that and I think it's, uh, it's about right. Okay, so I heard this story about a wildfire. What, what does that have to do with anything? And it sounds like you, you started having symptoms after that.

Steve: 2:58

Well, actually I had symptoms before that and I was being, uh, kind of tested for everything. Um, kind of doing the full, um, you know, get the full scan, get all the tests, you know, get the colonoscopy, get the endoscopy, you know, test everything. Um, and uh, the response was, well, we, this is a fishing expedition. We can't find anything. I mean, it. It turns out they were fishing in the wrong pond. Um, there was a lot of stuff going on, but, uh, I, it's, you know, I hear this a lot with, uh, cancer that, uh, it's misdiagnosed for a while, uh, because, you know, maybe it's just, you know, finding the signal and the noise is, is hard if you don't know what you're looking for. Um, what I actually got, I actually got diagnosed What I, I got displaced from my. Regular doctors and my regular health system by the fire to a different health system. So a fresh set of eyes. Um, and I, I, I ended up getting diagnosed, but my, my, my question then when I was in the hospital was why didn't they catch the sooner?

David: 4:00

Yeah. So

Steve: 4:01

the, what I did is I, I, I used my old data. And with ai and AI was immediately kind of right on target, um, saying, well, this is, you need to, you need to do these tests. You need to check your plasma cells, you might need to bo bone marrow biopsy. So the AI was kind of right on target where the doctors before were calling the phishing expedition. So for me, that was. Very enlightening as to how much power is already in the AI foundation. Models we have today have been trained quite, uh, quite extensively on medicine. Um, 'cause I have something that's quite rare and, uh, it's sort of new, all the details, so,

David: 4:43

yeah. Yeah, yeah. Well the thing in medicine, of course, rare, rare conditions are quite common in the sense that many people have something that's rare. Right? So it's not just like it was, you had a specific thing that maybe somebody hadn't. Seen before, but a lot of people have things

Steve: 4:58

Yeah. Like

David: 4:58

that, that are, that are different.

Steve: 5:00

Well, this is what really led me to precision medicine, because precision medicine is kind of the recognition. That, uh, if a million people have a cancer, it's a million different genetically unique diseases. Um, so, but pretty much everybody has a rare disease. Yeah. Everybody has a unique disease. We kind of put people in buckets and treat them with kind of the same standard of care, which works, you know, 30% of the time, or it doesn't, you know, it's, it, it's, um, uh, it. Depending on what you have, sometimes it works great, sometimes it doesn't work at all. But it's, it's not that personalized, it's kind of treating the average, um, the whole new. Um, field of, of, of oncology is looking at the genotype, geno looking at the genomics of the cancer cells, looking at the microenvironment, looking at the molecular profiling, looking at this whole new field of diagnostics. Um, and from that, discovering what are the unique. Vulnerabilities of that cancer because there are, you might need a unique set of treatments based on that. And that's exactly what happened to me. I was initially put on standard of care. Um, I, I started digging into the cytogenetics and molecular profiling. Um, things that were kind of attached, you know, and these big long attachments in, in my, yeah, that, uh, no one really even talked about, but I was, I insisted on getting tested weekly rather than monthly or every other month.'cause I really wanted to know what was going on. And I could see pretty early on that the standard of care wasn't going to get me to a complete response. But when I started looking at the specific mutations of my cancer with ai, with the assistance of ai, I realized that there were other options for me. Um, they happened to be off-label and not approved for my disease because my disease is too rare, it's no one's gonna do a clinical trial. It's too rare. Um, and, uh, but I, you know, when I went to the Mayo Clinic and UCSD and UCSF, uh, I, I found that hey, these are the right treatment options and I should get on this completely different path. Um, which I did, and it was extraordinarily effective. But I never would've been on that different path if I didn't know about it, if I didn't kind of become an expert in my own disease. Right? I didn't advocate for it. So what I was developing with the AI. Was really a way to, to look at my medical record from many different points of view. And syn synthesize those results and try to give me feedback. Um, so I, when I go and talk to my doctor, you know, there, there's a human in the loop here. It's called your doctor. But yeah, what you get from your doctor actually does depend, uh, uh, a bit on, uh, you know, what, what you know and, uh, what you advocate for and, and, and what you ask for.

David: 7:50

So lemme go back to the, uh, a little bit back in the conversation where you were talking about how. If they were fishing in the wrong pond. And I just wanna ask about that and maybe challenge it a little bit. So it sounds like you were able to look at what they previously had gathered from whatever tests were being done and to be able to see the insight there. So maybe they were in the right pond, but they just couldn't see the fish, or they had the wrong sonar for it or, or something like that. Is that right? Like the information was there?

Steve: 8:15

The information is there. But you know, like with, with a lot of things in healthcare, there's a lot of say ambiguous results. It's like, Hey, yeah. You're anemic, you know, that that could be a bunch of different things. Well, there, there was, there were some very clear clues in, in the data that were, that were missed because we just, they weren't looking, you know, they weren't looking,

David: 8:38

yeah.

Steve: 8:38

Looking for something else. Um, uh, you know, they, they, this happens a lot in medicine there. They, the explosion of medical knowledge is beyond human scale. Right. It's like no can contain that in, in, in their brain. But AI has a. The ability to access and synthesize a much broader, uh, body of knowledge. So it's really gonna be an essential tool in, in diagnosis. But diagnosis is kind of one and done. Okay, now you got your diagnosis. Now what? But where it's really interesting is in precision medicine and say the personalized treatment options, uh, 'cause this happens. I also see this, I kind of. Learned all these things along the way just by going through it my myself. But there's a lot of things where it's kind of like, Hey, you can do this or you can do that. You decide, yeah, well, you know, how do you, you gotta, based on what you gotta educate yourself to, to even be, you know, competent in that, that conversation. Right. It's pretty hard when you're kind of hit with, um, all of this life changing stuff. So, you know, the first kind of order of business is. It really helps for, for you to become educated in your disease down to the details, down to the, you know, these, these, these reports that might be 12 pages long and have a bunch of Greek in them, and it looked totally foreign to you. Yeah. There's a lot of clues in there. How can you educate yourself on that? You, I mean, I didn't go to medical school. I didn't get a PhD in molecular biology, but with the assistance of ai, I can zoom in and I can learn the parts that are relevant to me. I don't need to learn all of medicine. I need to learn the parts that are relevant to me. So if I'm gonna have a collaborative, you know, shared decision making with my doctor, you need to be a good counterparty to that. You need to be able to, if you only get 10 minutes with your doctor, how do you make every minute count? So I saw a need for, um, you know, there's a lot of precision medicine out there, and if you go to Mayo Clinic or Dana-Farber or UCSF or UCSD, you're gonna get a lot closer to it. But if you're not going to someplace like that, how are you gonna get ac? How are you gonna get access to it? Well, you need to ask for it. You need to, right? You need to dig in because if you don't dig in and advocate for yourself, you're gonna get standard of care, which maybe is exactly the right thing for you to get. You know, it's the, it's what worked on average. It's what worked best on average. Yeah. But chances are, you know, you're not average chances. Yeah. Got it. Are different than average.

David: 11:03

Alright. So we use, so you've got the, you know, you, you did the first thing of kinda like the data gathering. Mm-hmm. And then the analysis and assessment of that was essentially for diagnosis as basically coming with the help of the AI. Then there's the personalized treatment. Once you've got the diagnosis, of course you wanna treat the right thing, but you don't necessarily treat it in the standard way and understand how there's the interaction between you, the machine, and still going to the right places and dealing with the, with the physician. So then how did you go and what, so then where does CureWise come out of that? Why does it a, why does it go beyond just your own personal experience?

Steve: 11:35

Well, I mean, I developed, you know, I was customer number one. I developed, yeah, I mean, I'm a technologist and I'd been developing a lot of AI related products. Um, I, I was the Chief AI officer for Peter Diamandis and I developed a whole bunch of kind of educational AI things for, for his community and it was all really interesting and really fascinating, kind of pioneering kind of new ways of. Using a mixture of agents and models and chain of debate and having agents argue with each other to kind of, yeah, that information is sorted out. And I took all of this kind of stuff I'd been working on and pointed it at my medical record.

David: 12:12

Mm-hmm.

Steve: 12:13

And, you know, I, I wasn't planning to make a product or make a company, I was just trying to figure out like, how do I, how do I survive this? Yeah. Question. But as it started to work. And then, you know, I know a lot of people in the medical field and some of 'em started inviting me to come speak at their conferences and, um, talk about what I'm doing. And, and I thought, you know, like, uh, you know, I thought, I sort of, you first thing you, you think when you get cancer is okay, well certainly my career is over. Like, right? So first thing you think, but um, you know, people started saying, well, how do I get this? You know, for myself or my family and how do I invest in this? And so I realized, you know, there's where there, there're people really interested in what I'm doing. I started sharing it with friends who, who had, uh, cancer. Um, and other people started also getting insights. I mean, this is cure y is not doing any diagnosis or treatment. What it's doing is it's educating you. To have a, a, a more enlightened conversation with your doctor so that you, you kind of know what's going on and you know what to ask for, and, uh, knowing what your options are and kind of advocating for those, you're gonna, you're gonna have more options. I did this for, for, for myself. It started off with treatment options, but then there are a lot of other factors beyond that. Like a lot of cancer is immune compromising. So it's like, okay, I treated the cancer, but then I got an infection. So then there's all the whole nother set of things for like, how do I keep my immune system, um, you know, uh, in, in good shape. So there was a whole nother. Uh, layer there kind of, you know, offense was working, but what about defense and then lifestyle factors? I mean the, the, you know, if you think all those are important, you know, for prevention, they, they turn out to be really important if you're an immunotherapy. That's right. Chemotherapy. So there's a lot to manage and a lot, yeah. Monitor. So I, I, I built an application for myself that was kind of like, let me, let me manage all aspects of, of this, um. When I got to a complete response, when my new AI kind of discovered, um, at least for me, I mean, it was obviously it's out there, people were doing this at, uh, leading centers. But when I found, figured it out and I asked for it, and I advocated for it and got on a better path, I'd had a complete response. I started feeling like, okay. You know, I'm, I'm gonna be in, I'm gonna be okay. Um, yeah, I've done a startups before. I know how much energy that takes, um, and how, and how challenging that is. And, uh, but I started to, um, uh, uh, recruit a really fantastic team. Um, I raised some money, um, and we're bringing that product I originally built for myself and, and then some friends, and we're bringing that to market. Um, it's in a private beta right now. Um, you can sign up and get on the waiting list and write a, yeah, write in there about why, why we should let you in early. Um, yeah, yeah, we're, we're kind of doing that right now where we can do some handholding, but, uh, by, uh, January we're gonna release it to the, to the know wide open to the, to the public. And hopefully it's gonna help a lot of people. And it's the kind of phase one of this is let's make precision medicine more understandable and accessible to patients. Um. You know, the, the, the downstream. But what, where do we go from there? It's, well, precision medicine is exploding because the diagnostics are exploding and the treatment options are exploding. So what's happening is that everybody's ending up with a rare disease. Everybody's ending. Right. With an s. With a,

David: 15:44

exactly. Yeah. Back to the, back, to the back, to the point about rare diseases being common and being, in fact. You know, they, you're back in the marketing terms of, uh, you know, segment of one. Uh, so if you can actually apply that, that, that makes great, great sense. So you talk about, um, I've heard Q Wise described as a, a virtual tumor board. Sounds good. But I think first question is, uh, what is a tumor board and then why does it need to go virtual?

Steve: 16:08

Well, so. I dunno if you remember like some of those medical shows. I mean, maybe it was like house, you know, there's several doctors, you know, go into a room and they look at a case and they all have different opinions and they talk about it. And that process, um, can be very helpful because again, medical knowledge is too vast to be all contained in one brain, right. So, um, and then you, you notice in, in real life you've got a lot of different doctors. If you've got cancer, you got a bunch of different doctors probably. Yes. Uh, and, and if you go to different doctors, even, uh, different oncologists and hematologists, you get a lot of different opinions. Um, that doesn't mean that you know, the fact that you go to five different doctors and you get five different opinions, it doesn't mean. Your doctors are hallucinating. What it means mm-hmm is that, um, things are complicated and, you know, there isn't, uh, an absolute right or wrong. You know, there people don't know the absolute answer on these things, and there are different opinions. So with ai it's, it's similar in that you, you don't want the AI to just try to give you an answer. Um, 'cause chances are it's, you know, it's gonna be one of a bunch of po possibilities and who knows? Yeah. It's the right one. So we've constructed this. To intentionally look for a diversity of opinions from a di diversity of points of view on your situation, on your question, and then to have those agents. Kind of talk to each other and, and see where they agree, see where they disagree, um, and kind of synthesize the results or, or, um, kind of cross validate if one, if one of 'em has a different idea and you know, the, the, other than the other ones, like, have the other ones weigh in on, on the new idea. Yeah. And through that process, you, you have, that you, what you're doing is you're digging deeper into the knowledge because mm-hmm. You've, you know, everybody watching this podcast probably is used chat GPT by now. And you will know that. Depending on how you ask the question, you might get a different answer. Right. Well, that's not very reassuring In medicine. You kind of wanna no deterministic answer, but you know, you're not gonna get a deterministic answer. You get different opinions, um, because there's a lot of unknowns. So let's actually dive into that. Let's look for a diversity of, of ideas, and then let's look for, for sit for convergence, because yeah, that, that's, that's, that's important to know. So in my case, when I did the. Looked at my old data and said, what's going on here? I had five different agents from five different, um, points of view in medicine. Um, look at that. And I had five different opinions about what they thought the diagnosis would be, right. But what they said is, Hey, to settle our argument. You need to ask your doctor for this test. Right? And that test will decide. So, so, so, and it was exactly the test I needed. And if I own that a year earlier and I would've gotten that test, they would've diagnosed me a year earlier. But I did not know to ask for that test.'cause I didn't, you know, I didn't have any clue, you know, like, I'm, how am I supposed to know about any of this stuff? But it, but if I had known enough about it to say, Hey, what about that test? Why don't you do that test? Um, you know, it looks like you can't quite figure out what's going on. Why don't we do this test? It would've been, uh, you know, it, it would've,

David: 19:14

yeah, that's perfect. I mean, it sounds like a very, you know, kind of modern AI approach based approach to this question about differential diagnosis.'cause the best systems that I've seen up to this point, they help you narrow it. They say, mm-hmm. You know, they never give you the one definitive answer, but they say, here's what we know. And with a 70% chance of this, and if we did this test. Yeah, then we're gonna be able to get it to 95, you know, percent chance, something like that. So on this approach, it sounds, uh, broadly useful. Uh, certainly in, in cancer and then beyond. And even you, you could imagine, uh, even ex expending extending this to. The whole social and political context. Say, if you know people of different views could, uh, learn to, uh, come together and speak and, and converge, that would be great. We'll save that for a different podcast or a different startup. But for right now, for what you're focused on, the Qis Qis, who is best suited for this approach?

Steve: 20:03

So we did decide to focus on cancer first because that is the frontier of precision medicine, and it is more complex and it has a level of complexity where you, you can't just type it in a chat, GPT, you know, it's, you've got a, you got a much bigger medical record. Um, there's a lot more going on. It needs some special care because it's not just. You or your genes or it, it's the, it's an evolving cancer that has its own genes and its own variations and, and, and it's constantly changing, so it's complex. And it's the frontier of precision medicine. And you're not gonna cure cancer without ai. It's too complex. We finally have a tool that allows us to deal with this vast new kind of sets of data that are coming out of precision, uh, genomics and molecular profiling. So you, it needs ai, it needs an AI application. So, um, we're focusing there. Who, who is the. Kind of target audience at first? Well, it's, it's people who are, who want to become more of an expert in their disease.'cause they want to advocate for, they want to figure out what the best options are and they want to advocate for that. Now I've been sitting in the infusion, uh, center in, in, uh, at, at the hospital for my own treatment. And you know, some people are just clearly just gonna do whatever their doctor says. Yeah. There are other people that are there, they're on their iPhone and looking at MyChart and they're talking to a family member. You know, they might be like my parents' age, but they're talking to a family looking at MyChart and they're trying to figure stuff out, you know, like that's our audience initially. Right, right. Um, it's, it's for who's ever gonna advocate for that patient, which it might be you, you advocating for yourself, or it might be a family member. Got it.

David: 21:55

So. It seems, you know, almost axiomatic that a physician using AI is gonna be better than a physician on their own or random person using AI on their own. And yet, at least some of the kind of early, maybe small, provocative studies have shown that doesn't always exactly add up. And so I'm wondering what you're seeing in terms of the, the state of, uh, let's say, let's start with diagnosis, speed in accuracy from some of these top medical centers. Is it being accelerated by ai? Is it improving? Is it worsening?

Steve: 22:30

I think there's a lot of biases in some of these studies because I think there are, I think there are people that, you know, wanna show this and wanna show that, and you probably, yeah. Things up to show whatever you wanna show uhhuh. But the, uh, the reality is the medical knowledge. The rate of medical knowledge generation is beyond the scale of any person to absorb, right? And that's just a fact. I mean, if you, if you, if you even attempted to read the top papers in your field, you'd spend all day long doing that and not practicing medicine. So. What, what do we do? We just go to like, okay, what's the standard of care? It's like, okay, you have this. Let's, we, we don't have time to think about it. And there's a, there's a shortage of oncologists, um, and there's a rise in cancer. Um, so, so there, it, it's just there's no, we, we need assistance to do this. We need knowledge tools to be able to, to do this. I've seen some of the studies that you're referring to, some of them were done on, you know, if it's published now, it probably was done on Yeah, yeah, yeah. From a year ago or a year and a half ago. Um, it's, it's very dated. This, this field is growing exponentially, right? The college that's compressed into those foundation models is quite extraordinary. You do need to know how to manage it, um, uh, because you can, you know, you, you can. You can find a way to set things up where, you know, it doesn't work if you wanna show it doesn't work. But you also can find a ways to set things up that are super, super helpful. And there are companies that have already become significant companies doing this for doctors. I mean, open evidence is, is they're doing this for doctors. Um, and you know, like your doctors are gonna get AI. Yeah, your insurance company is gonna get ai, the drug companies are getting ai, I mean, everybody's getting ai who's looking out for the patient, right? So we're focused on that. Um, and, uh, and I, I, I, I think that people are gonna see that we're on their side because we're doing this for ourselves. Yeah. Saying, look, I'm doing this for myself. Um, if you wanna come along, you know, like. If you wanna come along, uh, further ride and, and do this too, welcome. Um, but, uh, you know, we we're doing this for the patient first.

David: 24:42

Yeah. Got it. So what guardrails are needed for, uh, use of AI in, in medicine? I get the sense that it, it, it is going to be used. It is being used. And notwithstanding some of the papers that I'm, that I'm describing, it is gonna get there if it isn't already. But what guardrails do we need?

Steve: 24:59

Well, we're not trying to, to. Dr. Robot, and we're not trying to get an answer. What we're trying to do is we're trying to explore what's possible and we're trying to get educated. So if you put this in the context of collaborative care or shared decision making, you know it's between you and your doctor. Um, it's really helpful if you're a little more of an equal partner in that, rather than the doctor in the white coat is telling you, uh, and you're a deer in the headlights and had, you know, don't know what to do. Um, the more educated you are. And understanding what's going on, the more valuable those conversations are gonna be, and the more likely it is, you're going to get to a better result. So, so I, I think it's What are your expectations? The biggest guardrail is having the right expectation of the ai. The AI is not there to diagnose and treat a disease. It's there to help educate you about your disease. And give you some ideas about what might be possible so that you can have a better conversation with your doctor. If you go in with that expectation, it's going to be awesomely helpful.

David: 26:08

Good. So things are moving very quickly as you, uh, described, and not only, as you know, medical knowledge has also has grown a lot, but AI is moving quickly and the, the brain doesn't understand exponential growth too well and can't relate to it. So, but where do you see things going over the next few years? I mean, when we have this conversation in three to five years, what is AI powered? Diagnosis and treatment planning gonna look like at that point.

Steve: 26:35

It's really getting to truly personalized medicine, um, and precision medicine. So I mentioned earlier on that, you know, if a million people have cancer, it's a million different genetically unique diseases. Yeah. Point. Every one of those people is gonna get their own precis, precision optimized cocktail that's, that's optimized for whatever their unique condition is. How do we get there? Um, and right now we're looking for fragments of DNA and fragments of, uh, genes in the. In the, you know, the soup related to your, to your cancer. Yeah. And that's, you know, fairly incomplete. But we're gonna be sequencing the entire genome of those cancer cells, and then we're gonna be using AI to predict the evolution. Because, you know, if you, if you, let's say we discover this weakness in your cancer cell, Hey, it's keeping itself alive because of this particular weakness. Let's treat that well, chances are. It's gonna evolve a workaround. Yeah. But you, if you understand what that weakness is and how it might try to evolve a workaround, maybe you'll hit the workarounds at the same time in a better cocktail and you'll knock it out, um, you know, uh, with a higher probability and, and, and faster. So there's a lot that's gonna happen because we actually know more about the cancer. Cancer is really unique, uh, disease because it's your own cells. That have decided, hey, I'm not part of the whole anymore. Yeah. I'm gonna evolve on my own and do my own thing. And it becomes a parasite. Um, and it, but it's evolving. It's its own kind of, uh, you know, like, uh, Darwinian, uh, yeah, that's going on. So it's, you kind of have to crack the code on life itself to really figure out cancer. Which is why it's kind of interesting to, to, to dive in because as we figure that out, we're gonna figure out a lot of other things. Um, it's gonna have a massive impact on, on, on healthcare in general, but this is like becoming the most expensive disease we have. Yeah. Uh, and it's the frontier of all of this new, um, kind of new field of, uh, diagnostic diagnostics and all the new molecules and things we're discovering, you know, also with ai. So, you know, it's like three exponentials converging. Yeah, the exponentials on the, the genomics and molecular profiling, the, the exponentials and all the new ideas for, um, uh, molecules that might work, and the exponential of ai. So these things are converging in a way that I think it's, there's really a shot at getting closer and closer to what, what we call cures.

David: 29:07

Yeah. Outstanding. Well, let's wrap it up there. That's it for another episode of the Health Biz Podcast on the Cure Talk Channel. I'm your host, David Williams, president of Health Business Group, and I've been here today with Steve Brown, he's founder of CureWise. If you like what you heard, I hope you will subscribe on your favorite service. And Steve, thanks for what you're doing and thanks for joining me today.

Steve: 29:28

Great to be here, David. Thanks.