Welcome to Real Talk on AI and I care with your host, Dr. Scott Morris. And I'm co host, Dr. Rehan Ahmed.
In each episode, we discuss current news, adding a little AI education, and debate innovative AI tools and topics that will change the industry. So news of the week, I don't know if any of you guys saw this, Microsoft unveiled their MAI DXO AI model that claims, and that's in quotation marks, to our perform physicians with diagnostic accuracy. So they use the team of kind of five AI physician agents that we're working together as a team. So it's almost like you had a panel of doctors, one, Dr.
hypothesis that tracked the diagnosis, and then Dr. TestChooser, which kind of like which test should you do? And those Dr. Challenger, which is kind of is like, let me tell you what, not right about this.
And he had Dr. Fine answer, Dr. Stewardship, which is kind of managing costs. And so what happens?
They said that they achieved an 80% diagnostic accuracy, versus 20% for the live physicians, while they also cut costs. Now, I have to tell you, though, as positive AI as it can go, there were some issues of this. Number one, they only looked at 304 cases, and there's 9 billion people on the planet. So they didn't look at a very big end value.
They didn't use any healthy patients. So they were all sick patients. So could they die? Can these team diagnose people who are, quote, unquote healthy?
You know, he says, we don't know that. And when they say it was more cost effective, well, they were kind of only looking at lab tests. They didn't actually count like how much to take to develop the AI and all those types of things. So I think there were some question marks about this.
We always talk about how it's infant program, right? And this is Microsoft kind of infant merger, infant access into the medical artificial intelligence world. And so there's some challenges there. You know, in these cases, we're already solved.
So their generative AI could go and look at something. There's nothing perspective. There's nothing predictive about this. But it is some interesting news about Microsoft, which is a pretty big fish, trying to get into the healthcare world.
Here's your news of the day. Today in our knowledge bite, we're going to talk a little bit about what generative AI is. And so, you know, you know that knowledge bites where we take these complex AI concepts. And we try to break them down in these little bite size, kind of understandable pieces.
And today we're tackling that hot topic, which is all over the news. In fact, those are news of the day, generative AI. So what exactly is generative AI? Well, in simple terms, it's kind of a type of artificial intelligence that creates brand new things.
It generates new stuff. Think of it as like super smart artist or writer musician that kind of learns from these vast amounts of data. And that's what we talk about all the time talking about data. And these vast amounts of existing data.
And then we use that knowledge to generate something new, something different, something original. Now, traditionally, I often focus on analyzing data. Like we have like, let's find, I think we used to example a couple of months ago, like identifying a cat in a picture. Well, generating the other hand says, let's make that cat picture from scratch.
Let's create a new cat. Or imagine a cat in a style that no one's ever seen before. Like he's wearing a top hat or he's flying a spaceship or something like that. Well, here's a few examples.
You know, if you think about that, you guys, most of you are very familiar with generative AI in terms of like chat GBT or Gemini or Quad or GROC or one of those things. And those are all different chat bots. And when you ask those tools, do you know, write an email or poem or a story or that's generative AI. That's creating new content.
And it's not just pulling from some of their answer. It finds on the internet. It's just creating something totally new based on the patterns or language or scripts that you wanted it to learn from. It takes billions of words.
It's okay. I'm gonna learn this pattern. I'm gonna create something new. And then for audio or for visual, you have things like image generation.
Like I'm a big fan of mid-journey or dolly. And those are, you know, to create these kind of interesting images in the beginning, they were kind of a little clunky and maybe a little surreal. But now they're getting pretty good. And you type in, hey, I want some fused or futuristic city made of plants that as cats in it, it's gonna create some brand new images that matches your imagination.
As long as you put in the right prompts and you kind of get really deep-threatening prompts, you can create some really crazy new artwork. And then now we're starting to get even AI-generated music and some of these tools can build pieces of music, the classical, the jazz. And you know, if you just give them a little bit of proper prompting. So the kind of the key to this whole gender-of-AI discussion is creation.
So it doesn't just recognize, it's not, it's actually imagining producing much like, well, what will sapiens do? It's trained on these massive data sets of other human-created content. So it suffers from some of the same biases because it's learning from human-created content. So those biases will show up.
It learns kind of the underlying rules and structures of language and imagery and those types of things and it uses those rules to create these new outputs, which may be an image, it may be a music, maybe a text. It's kind of really transforming everything from art to music to writing and in our world, drug discovery. So next time you see something or next time you see some AI, ask it to create you something new. And this is one of the things I would encourage all of you to start practicing using AI.
Go on to one of the free gender-of-AI's like I'm a big fan of Gemini and start working to say great things. You're going to mid-Journey and say, I want to create a picture of my kid throwing a football. I don't know, make something up. But get started on gender-of-AI.
It's here, it's now, it gets better every week. There's some new thing out there. Hope you guys learned something. This is the knowledge bite of the day.
Today in the show, we have Katherine Bourne-Bomb, who's the chief business officer at Redis Beck. And she's quickly becoming one of my favorite people. I love talking with her. She's got a lot of really creative ideas.
She sees things not necessarily from a doctor perspective, but from how we change the world perspective. And so, fascinating to have around the show today, we're concept today is we're going to talk, how is AI going to fix healthcare delivery? And so, Katherine, I wanted to welcome to the show and I can't wait to see where this goes. Thank you.
I'm delighted to be here. Well, so let's just kind of kick off and say, we were on a panel at AOA talking about, what are some of the new innovations? And it got us kind of off on this discussion, thinking about, well, in the futuristic world, let's get our little crystal ball out and go, if everything happens the way we want, and of course, we always know that's what happens, but everything was happened the way we want. How could AI change the world?
And I don't even know where you start in this, because there's so many different concepts that we've covered on these various real talk podcasts and our innovators podcasts and through articles and journals on AI and I care. But so let's start with here. Let's start with, because you're in the world of digital diagnostics and how do you feel of a three of a big huge softball? How do you feel like AI is going to change the way we diagnose diseases in the eye and maybe even outside the eye?
Yeah, so we are at a really interesting and inflection point right now, where we're starting to see these new diagnostic AI being implemented, both in eye care and other areas of medicine, especially kind of radiology and pathology. And what they're offering right now is really a different way to diagnose disease, a faster way to diagnose disease. What they're doing right now is really providing sort of a second opinion for a clinician, right? So they're looking at lots of images, detecting patterns of early disease and flagging things for a clinician who can kind of take a look, see if they agree and take next steps as relevant.
What a relief. And I think that's interesting. You bring up a great point on that, Catherine, because I think that so many people who go, oh, AI is going to replace us, right? It's going to do it.
And I love the way you phrased it. And most of the people in our AI part of our industry are all saying the same thing. No, it's here as a tool to augment us, to help us, to give us more information. None of these AI is, at least I don't look in, the next five to tenures are going to replace anything we do, because they're just not built that way.
They're built to help us. And we're basically not only using as a tool, but it's kind of in reverse, we're going to act as supervised learning for the AI as well. Like we're going to teach it as much as it's going to teach us, in my opinion. Absolutely, I agree with you there.
I think what's really exciting and what's coming down the pipeline is sort of the second tier diagnostics, are where we're leveraging information that's already available in the body that clinicians can't necessarily access right now through traditional means. And so what we're building at Reddyspect is a good example of that, right? We're looking at information available in the Rednut, that a clinician can't see with their eyes, but that we can detect information. We're using deep learning models that can help identify early signs of disease before we would normally catch them in our standard workflow.
So there's a really unique opportunity to especially from the eye, to leverage insights about systemic health, where optometrist can really be that first point of access. Yeah, that whole field of acuilomics is really starting to get a lot of press. Like are the eyes really not just the windows to the soul, but the windows to the rest of the body, we just have easier access to. And as you said, that brings earlier diagnosis, or at least, hey, look at this spot.
Maybe we start following people, instead of once a year, maybe you're following them every six months or still once or five years now, it's every year. But I need to think about how we fix healthcare delivery, because that's kind of our theme is, does that earlier diagnosis mean that we prevent people because we catch it earlier, and we're not gonna have some of the morphological changes that happen later down, because we're catching this earlier. I still think we have some treatment issues. Like where do we treat things?
Do we treat earlier, do we treat later? But I do think that earlier diagnosis, because of, as you said, machine learning models, does that mean earlier prevention? And the answer is maybe. I mean, I think that brings a whole other field is that now that we start, it's like everything else, is once we find something wrong, then we start looking for treatments.
Maybe we're gonna find that there's something that something the AI finds and says, oh, hey, look right here, and this seems to be consistent among lots of other things. And once we know there's a problem at, let's say the level of the RPE, do we start developing drugs to fix the RPE that we don't even conceptualize as being a problem now? So I mean, do you feel like where do you think AI's gonna fit in terms of earlier prevention and developing medicines and treatments, medicines and other therapies for treating disease earlier? Yeah, so I wanna pick up on one thing you said earlier, and then I'll jump to the treatment thing, it's that's okay.
We right now, our healthcare systems are really, really good at treating acute emergencies, right? If you have something critical that requires care right now, you can show up in a emergency department and receive care. We are not very good at the preventative and proactive levels of care. And so I think there is a unique opportunity here for AI to help provide additional information that can both detect these things much earlier, but also provide routine checkups and see, is this progressing to a point where we do need to take clinical action?
So a lot of conditions are diagnosed just way too late, they're very complex, they're very expensive, they're burdensome to the patients and clinicians, and we can do better on that. Do better, I agree, do better. My kids felt me a little bit, Dad, do better. You're right, I mean, I just think that I, well, we have an entire series coming up on just this talk about, are we waiting too long?
And you're right, our healthcare system, all of the world is an acute care system. We're a, we're a fix an illness system, not a well care system, and we need to change that, and maybe I was gonna do that, and then I think about, you know, where does that bring us? I get really excited about the future of, and there's some challenges, there's some obstacles we have to overcome, but, you know, I get really excited about the fact that I think personalized medicine will happen in our, a little older than you, but will happen in my lifetime, and definitely your lifetime, where, you know, we're going to look at phenotype genotype and go, hey, you might respond this way to this treatment, and somebody else might respond this way to this treatment, and now we're gonna have diagnosis, diagnostic tools, like we started this going, hey, we can look at these little changes way before we're ever gonna see them with the naked eye, and even some of the technology of today, I mean, I think in the last 20 years, how much technology is improved, and I think it's gonna only happen faster, better, quicker, from that perspective, but, so, where do you think, where do you think we're going in terms of personalized medicine? I mean, I, one of the things I love about Catherine to the audience is she's pretty diverse, she's got a, she's got a great range, so I'm gonna push her here a little bit, and see, where do you think we're going in terms of personalized medicine?
Yeah, I think, I'm really excited about the future of personalized medicine. You know, there's some areas of healthcare that do it quite well, so on ecology would be one example, where, you know, you fully characterize someone before determining their treatment plans, that you can really kind of target what's there, but I think we also have opportunities to do better, and to apply these same principles in other areas of health, so brain health is an area that we're just sort of tapping into here, and certainly I care, I think both from the sort of clinical process, because it's connected to the brain and to other systems in your body, you have this really unique, non-invasive access point that can share so much information about what's happening in your brain, in your cardiovascular system, you know, your full systemic health, and I think there's a really interesting opportunity both to learn more about, how we can detect these biomarkers in such a, you know, simple, non-invasive way, but also how we can act upon it. And so just to tie back to your earlier question about sort of what's the utility here for treatments, and how can we, you know, not only diagnose different conditions earlier, faster, you know, more accurately, but what can we do about it? You know, we can apply these same sort of biomarkers to helping understand how treatments are progressing, and who might be a good candidate for one treatment over another.
So I think we were just on the cusp of what's possible, but I think, you know, between rigorous science and new technologies that were able to unlock through AI and, and quite frankly, the AI, we have a huge potential that we are just beginning to tap into. Yeah, I love the three things. I love it kind of as you were talking as thing with the fact that, you know, you're right, we've gotten so precision medicine oriented in oncology because it kills people, right? And thus there's been a lot of time and energy and focus and money spent on doing that.
Now, you know, a diagnosis 10 years ago that was definitely going to be very short mortality is not some people can live for 20, 30 years with. And I do think like you said, there's more and more information about the second big killer, which is neurodegenerative disease, you know, and whatever the cause of, I think someday we're going to find the cause of many of these forms of neurodegenerative disease. I don't think they're all genotype. I think there's a lot of phenotype that that's my opinion that are involved in this.
And maybe we're going to be able to identify that in the retina or in the choreocapalaris or other things that, you know, look at a metabolites that are happening in the retina and saying, hey, this is the risk factor for developing certain forms of neurodegenerative disease. But then I think the third big thing, everybody says the principal sense that they don't want to lose is vision. So maybe, you know, a combination of neurodegenerative disease and retina, you know, maybe we're going to see a day where there is no such thing as advanced AMD. Maybe we're going to see a day where there's no such thing as many forms of dementia, just like, or maybe we'll see it, but there's going to be ways to treat it much as there is 35 different derivations of different kinds of cancers these days.
Things are very targeted. I hope that we see a day, we target and find out there's 20 different kinds of AMD and this is what you do for one and so we do for another and so you do for another. But that brings up my next subject is, you know, the challenge is right now, and I think you and I talked about this when we're on the panel is that, you know, so much of what we do is we're such in the early phases. And I think about right now, the access to that information is you've got to live in a major metropolitan city.
You pretty much have to be at a major medical center with somebody who has the cloud and tout to be able to get that type of technology into their office to do that. There's a lot of ifs there, right? There's a lot of geolocation, geopolitical, to geofinational issues that are going on there. But I hope we see a day where we take all this technology and we change the way telemedicine works, right?
As remote health, maybe you don't have to go physically see a person in person every three months or six months, but maybe we're going to have diadoxid testing centers that sit out in clinics all over and are doing scanning, where AI is reading and going, hey, this person's changed. Trigger mark, let's go have them see somebody versus this person's still stable. Good, we'll see you again, six months from other tests. I mean, if you look at the crystal ball, where do you think we're going in terms of remote testing?
And how does that affect telemedicine? Those are fabulous questions. So I think that the future of AI adoption and healthcare in general honestly requires strong collaboration and partnerships. It's part of why I love optometry so much as an access point, right?
Because a lot of folks do not live in major urban centers. They do not have access to the latest and greatest in technology, but optometrists are so accessible, they are everywhere. They are ubiquitous and we also need to go see them because even if you don't want to go to your annual checkup, if your vision is changing, you need a new prescription, you have a reason to go in. And then that can unlock the conversations about other areas of health.
And so I think a lot of the new technologies are leveraging cameras that are already in optometry offices or already in retail, pharmacy or things like that. So you don't necessarily need a whole new infrastructure when you can leverage what already exists, maybe by adding something onto it or just layering in software, there's tons of different options and ways to go about this so that there's not this huge change to the workflow. But you bring up a good point about what do you do once you actually identify these conditions or these early signs of a condition, right? Because you don't want to leave all of these people with new information about something there at risk or that they may have and not connect them into what they need.
What do we do about it? Yeah, I mean, that's the most frustrating thing when we're like, okay, you have this disease. Well, what can you do about it? Yeah.
Yeah, cool. Well, the first step, right, is we gotta know it exists before we can treat it, right? It's, we can say that about every disease has been diseases in my career of 30 years, that 30 years ago we got, we're kinda hoesed on this one, we got no way to fix this. And now we're like, oh, yeah, we do this, right?
I think about WEDA MD, dry AMD, many forms of retinal problems. Yeah, now we just treat it. Okay, sure, go have some injections, go do this, and it's gonna be fine. Versus before it was, yeah, this is bad news.
So, and I wonder, I didn't make that interesting. So Catherine lives in Toronto. And so she's in a major metro area. I, on the other hand, as you can see, oh, she has on a video today, but I live out in the middle of the mountains in Colorado.
And so I am the last provider for two hours to the west. Like the last I care provider that exists for two hours to the west. And so I have everybody, but I look at it and go, are we someday gonna have diagnostic centers that we put an hour and a half away? And those become your test site.
It's not, you know, maybe we have telemedicine, maybe it's just the testing center, and as the technology improves, that's the early capture for some of these diseases, that say, do I need to go see somebody every year? How do we get more focused, more personalized, not just medicine and germs of treatment, but personalized in terms of diagnosis in the first place? So that's my first curiosity questions, what you think about that? And I'll kind of say, and do we use those types of situations to encourage more patient engagement?
Because I think one of the challenges that I'll care providers, there's a lot of people that don't care, as you said earlier, acute care, they don't care until it's broke, and then they wanna get it fixed. How do we use AI and some of the other technologies that come about to get people more engaged in their own healthcare? And I wonder about, like when we think about diagnostic technologies, now that they know they have something, even they don't feel that they're broke, like I don't see a problem, but I know it's there. Do I have better care?
So I'm curious your thoughts on any of that stuff? A couple things. So I don't know that it's that folks don't care, whether it's sort of the patient or the clinician, maybe that's not what you meant, but I think that the way our systems are structured is often inadequate for the needs of the clinicians, of the patients to sort of come together and close those care gaps. So I think your idea of sort of having these hubs where diagnostics can happen, where treatment can happen is fantastic.
And we're even starting to see mobile clinics. So truly like an RV or an 18-wheeler that's been retrofitted to be- I'm not a little travel. Yeah. Absolutely.
So these exist now, like they're actually being rolled out across the US. And I'm really inspired by the creativity that we're starting to see. And I think especially as we have AI kind of on the front end for a lot of the diagnostics, we're also seeing this increase in other providers coming together with mobile clinics, with telemedicine for specialized areas so that you can reduce the friction between sort of the diagnosis and what happens next in the care pathway. So I think there's a lot of reasons to be optimistic about how the structure of our healthcare system can evolve to meet these emerging needs and really increase access and meet people where they are.
Well, so that's a great transition into that whole idea of patient engagement. I wonder about that as both a provider and the owner of a startup that we're trying to figure, how do we get people more engaged in their own healthcare? How do we get them to say, hey, this is important, whether it be vision care or a follow-up, I had a patient yesterday come in, we see a hemorrhage in the back of the eye, I'm like, how should we put pressure? Oh, it's fine.
What is the last thing you check? Oh, I don't need to check, it's totally fine. And I'm like, okay, and say, so I look and I go, okay, we're gonna check your blood pressure. I need 220 over like 110.
And I'm like, oh my gosh, like you should be going to the hospital, it goes, I feel fine. And I'm like, right now, you know, but this is your early warning sign and this is one of the things that vision care gets to do. And what happens if they were out in remote Colorado or remote Ontario and they were like, hey, I do need to go get this fixed. I do need to make the effort to go get this taken care of.
But I wonder about where do you think, I'm gonna switch gears, I need to go where do you think patient engagement is going? I personally think that just like we have Alexa and Siri, we're soon gonna have a personal health assistant that's gonna help guide us. I think we're already heading that way with rings and watches and, oh, hey, you didn't sleep enough or hey, you know, your blood pressure will lie or hey, you know, you didn't do your steps today. We're kind of already working into that.
It's just so early and that we don't really have interaction. We have them, we have a device telling us, but the question is, what do you think it looks like in the future when we start interacting with the AI to improve our care? That is a really interesting question. I think there, I'll be a little pet domestic here, which is not really my nature, but I'm afraid you've been.
I have to have to be a bit, a bit maybe not pessimistic skeptical for us. So I believe firmly in the benefits of AI, of connected care, of having these smooth workflows and of identifying things early, I believe in that. But I think we have to be very careful here to ensure that the information that people are making health decisions based on is reliable, is valid and is trustworthy. So I think especially as we have a lot of new technologies, kind of coming out to market, or especially if they're not sort of regulated by FDA or in my case, health Canada here in Toronto, we really need to be mindful of what information you are being provided with and what that actually means.
So if it's just sort of held in sleep well, just take that for what it's worth. If we're talking more deeply about sort of diagnostics and health information or making decisions about your health, we need to make sure that these are coming from trustworthy sources and that they're qualified to be providing that information. Well, okay, so I'm gonna play Devils App Kit, okay? So I look at and go, well, and we're kind of bridging into that next discussion, which is predictive analytics, is if we have enough data, and that's always the question, right?
That's all, and use like truthful, valid, non-biased data. And that's tough to come by these days. But if we had enough data, like let's say you could pan 10,000 or 100,000 people with a ring, and I mean, I think we still have that word, a ring or a watch or whatever, isn't connected into healthcare yet, yet being the keyword. But will we find out that people who averages 4,000 steps a day have a 12% less risk of cardiovascular event?
Well, you could do a study of 1,000 people, and that doesn't mean much considering there's 9 billion people on the planet. But we start looking at, hey, here's data from 200,000, a million, 200 million people. Now all of a sudden, will it start saying, will you, and I use the word personal health assistant, I don't know if that's what it's gonna be called or whatever, but will your personal health assistant go, hey, you know, Katherine, just wanted to remind you, you've been averaging doing really great, but now do we go from a personal health assistant to more of almost like a coach telling us, hey, look at you, have this risk for disease, we've looked at 100,000 people, and if you get your 4,000 steps, I'm just making something out, but you get 4,000 steps a day, you have a 12% decrease risk. Well, if you're conscious about, hey, I don't know, dementia, right?
Just throwing something out there, and you're going, man, I just don't want it in like my grandma, are you gonna make a concert effort? If you don't get your 4,000 steps in a day, you're gonna do 5,000 tomorrow, maybe, and will we figure out, will, and I think this is a five, 10 years away without a doubt, but I look at predictive analytics, and when we start having data, that now tells us individually, you know, we take that personalized medicine with predictive analytics and go, hey, you do this, you have a less risk for this, and these are the phenotype genotypes like you that have seen the same results, you know? So I agree with you that data's a problem, and I try to be very conscious about not showing shiny objects to people who listen to us and read us, because I don't want to promise something's not gonna happen, but I do think this is gonna happen. Please argue with me if you want, because, you know, I'm curious, everybody starts, I just had a discussion with another gentleman who's on our panel, the other day, easy, and easily Scott, that's never gonna happen.
You know, it was like, it was great, you know, that's all good. I appreciate discussion points. Yeah, no, I think absolutely, we are going to see proliferation of these types of technologies, and I think there's value in them. I just wanna make sure that when we're using these types of data to inform our behavior and our actions that it is sort of grounded in science and research and appropriate methods, but absolutely, you know, we know what's good for your, hard is good for your brain, and so absolutely, I'm a proponent of things that are helpful to people that give little nudges, that, you know, gamify getting out there and being active and doing things that we know are good for your health.
I think for me, it just the line gets a little fuzzier when if we're talking about precision, medicine, diagnostics, et cetera, that when you cross that threshold towards that type of information, I think it really does need to be regulated in a pretty rigorous way, just so that we don't erode credibility, trust, safety. Totally agree. Yeah, totally agree. All right, well, I'm gonna have you put your business hat on now and I want you to think about, so let's take AI out of the clinic in terms of patient care and let's move it into, because I think a big part of fixing healthcare delivery, which is our theme, is how do we fix the inefficiencies and the inadequacies of our care centers?
And I don't mean that as a drugitory because we're all doing the best we can with what we have. It will get better, like we will get more efficient, we will get faster. I mean, I remember when I came out of school back in the dark ages, it was pretty common to see eight or 12 people a day and you thought you were really busy. I mean, now if you do that, you're starving, right?
You're just not gonna survive that way. And so we've had to get more efficient. Where do we think AI is gonna go in terms of, you know, I guess we can throw predictive analytics, like, hey, how do you run a bit of business? How does it change efficiency?
How does it change workflow workforce? You know, what are some of your concepts about how AI is gonna change the care, the delivery centers, not necessarily the delivery of care? Yeah, so I think AI and technologies like that are really, really good at handling sort of repetitive data heavy tasks. So, you know, things on the admin side, like scheduling, calling folks, flagging this person is overdue.
Like these exist already. So I think there's a really useful sort of no nonsense way to be helpful there. Also for things like taking notes or following up, you can have sort of secure scribes in the room. They can take your clinical notes and you can review them at the end of a session and just be much more efficient there.
But I also think, you know, from the clinic workflow perspective that we are also able to use these types of technologies or in the future for risk stratification, for triage, for decision support types of activities. So I think it really depends on the focus of the clinic, but there's huge opportunities on sort of the workflow and the admin side for technologies like this to really help with efficiencies and just a better experience for all, by frankly. Cool. All right, so I always start to ask this at all of our podcasts that we do.
So looking at the crystal ball, the next three years, what do our listeners, in this case, what do our listeners need to pay attention to you and where do they need to dip their toes into fixing healthcare with AI in their own clinical situations? Goodness. I think that's three up a big softball there, right? She's like, oh, great, thanks, guys.
Big ball ball ball. You know, I really think the future of AI in healthcare delivery isn't really about just the technology. It's about how we use it. And if we can figure out how to get that delivery model right, then technologies become a real force multiplier for the human side of care and not really a replacement for it.
So if I'm a clinician thinking through, how do I make sure my practice is staying on the cutting edge? Then I'm gonna follow very closely and see what's happening in the AI landscape, see how the diagnostic capabilities and monitoring capabilities evolve over time so that I can offer the best options for my patients that are both within I care and beyond. Katherine, now as fascinating, I love some of your comments there. I think we're gonna use some of this for some of our promise to the rest of the pieces that we do.
I just wanna say thank you for such a fascinating discussion. This is Katherine Boranbaum from the She's the Chief Business Officer at Redispect. She comes with lots of interesting information. So please watch for her on the speaking circuit and watch for her.
I'm gonna try to recruit her to write some articles for us too. But so thank you so much for your time today as we dip our toes into how is AI really gonna fix healthcare delivery? You've been listening to ReelTalk, an AI in I care. Your weekly podcast to keep you informed about AI technologies revolutionizing I care.
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