Transforming Diagnostics: AI, Retinal Imaging, and the Future of Healthcare with Vicky Demas, CEO of identifeye HEALTH

[00:00:01] Welcome to the Chalk Talk Jim podcast, where we explore insights into healthcare that help uncover new opportunities for growth and success. I'm your host, Jim Jordan.

[00:00:21] Welcome back to the podcast where we explore the intersection of innovation, technology, and healthcare. Today's guest is Vicky Demas, and she is a chemical engineer turned healthcare visionary. She has over two decades of experience at the forefront of AI and life sciences. As the CEO of Identify Health,

[00:00:39] Vicky is leading efforts to revolutionize diagnostic imagery with AI-driven automation and patient-centered solutions. In this episode, Vicky shares how her company is harnessing the power of retinal imaging to transform healthcare. From tackling workflow and efficiencies to enabling early detection of chronic diseases like diabetes and cardiovascular conditions, Vicky's work is breaking down barriers to accessibility and empowering patients.

[00:01:07] We also discuss the importance of partnerships in healthcare innovation and how her company is paving the way for personalized medicine. If you're curious about how AI and automation are reshaping the diagnostics and the care of eyes, this conversation is the one for you. So tell me, Vicky, tell me in the audience a little bit more about yourself.

[00:01:27] Vicky Demas

[00:01:28] Yeah. So hi, James. Thank you so much for having me. Let's see. So I am an engineer by training, and I have been at the interface

[00:01:37] of technology, healthcare, life sciences for the majority of my career, 20, 25 years. And maybe about, gosh, a little bit over 10, 11 years ago, part of the technology, I guess, portfolio and tools that I have been working on started including some of the AI tools and infrastructure that Google had developed to become Google and its core business.

[00:02:03] And they were starting to apply it to life sciences.

[00:02:07] And they were starting to apply it to life sciences initiatives within Google X. So I got recruited to help build what became Google Life Sciences, which spun out as Verily, well, initially spun out as Google Life Sciences and got rebranded to Verily during the alphabet restructure.

[00:02:22] So we, at the time, started looking at problems worth solving that could potentially be impacted with, I guess, such technologies and collaborated with industry leaders.

[00:02:36] And some of the areas included medical imaging and specifically retinal imaging.

[00:02:42] And that was a collaboration between Google, Verily, Nikon, Optos that introduced me to the space.

[00:02:50] And the space was super fascinating because you could see there was a very clear workflow problem as there is in so many places.

[00:02:58] Fast forward and maybe I'll say we ended up setting up a lot of collaborations, joint ventures, et cetera.

[00:03:06] I ended up working for a cancer diagnostic, also similar, kind of generate massive set of data, leverage AI to build disease classifiers, et cetera.

[00:03:18] And now that can bring us to maybe about three and a half years ago to what at the time was called Tesseract Health.

[00:03:26] It was a startup that had gotten out of four Catalyzer, Jonathan Rothberg, and had just received the first, I'll just say, large amount of funding from venture capital, private equity, et cetera, to really focus on that problem.

[00:03:45] The problem statement of democratizing access to data from the retina.

[00:03:50] The company at the time was a bit different.

[00:03:52] So I came in and it was fascinating to me that a space that has been as promising as this, not very much had happened in the time that I had taken a break, which I think we can extrapolate quite a bit as to why that happens.

[00:04:07] I think some of the insights we were discussing in terms of maybe technology development not always happening in line and partnership with the key stakeholders who actually understand the problem really well that needs to be solved.

[00:04:24] So it was really an exciting opportunity for me to come in and sort of reset the company's mission at the time and pick up from where I had left off and saying there is a ton we can do if we leverage AI and automation, even like to address, let's call it the workflow problem, make it super easy to do retinal screenings, address some of the big problems in the healthcare space, like staff shortages, access to care associated with that.

[00:04:54] To really enable the power of the retinal, call it.

[00:04:57] So start with retinal screening to avoid preventable vision loss for big problems.

[00:05:04] And I'll speak more about first areas of focus for us and what I'm super excited about, but then also eventually to really unlock the potential of the retina.

[00:05:13] I like to say as the sort of the diagnostic platform of our times, it's a non-invasive test to take a photo of the back of the eye and then get a ton of insights from it.

[00:05:25] So I just had my annual exam or my by every other year exam recently.

[00:05:30] And so just maybe for the audience, so we can calibrate to make sure that I'm understanding.

[00:05:34] So you walk in and your doctor has a row of different tests they'd like you to take.

[00:05:39] And historically, the retina one is, hey, pay me $50.

[00:05:42] It's not covered by your insurance.

[00:05:44] Is that the test we're talking about?

[00:05:46] It's actually a little bit different for the first use case.

[00:05:51] And I'll come to that in a second because so the first use case is diabetic retinopathy screening, which is something that if a patient is leaving with their...

[00:06:02] Exactly. So it is totally covered.

[00:06:04] But there is an element of what you said, which is even how diabetic retinopathy screening happens is you do go to an ophthalmologist and they do have a series of tremendous equipment that a specialized sort of technician, at least, right?

[00:06:20] Knows how to operate.

[00:06:22] It's like you have a really well-qualified photographer that takes a good photo of your retina.

[00:06:26] So that's what we are trying to...

[00:06:28] I won't say replace because I go back to the staff shortages that we don't have enough doctors to see all the patients that need to be seen.

[00:06:37] And then also a lot of the patients that need to be seen don't actually make it an ophthalmologist anyhow.

[00:06:43] So I just want to stay above it for a second because I...

[00:06:47] So I've always paid for this and I've been doing it since those machines came out.

[00:06:50] So one of my most interesting experiences, so I have a cardiology background and an imaging background.

[00:06:56] And so this was before HIPAA.

[00:06:59] So I'm aging myself here.

[00:07:00] But I remember having that first scan and looking at the eye and sitting down with the doctor and saying, I believe from this you could predict heart disease, diabetes, all these other things.

[00:07:09] And he, again, this was before HIPAA, he said, let me show you.

[00:07:13] And what he did is he started bringing up scans and you could see the tortuosity and the twisting and the discoloration, which means it's hardening or maybe, you know, so there's different things in the eyes.

[00:07:23] And what struck me is to your point that this test, which now might only be justified financially from a diabetic perspective, has so much predictive quality that you can't get anywhere else.

[00:07:37] And so, you know, the old saying about the eyes tell you everything, I'm really starting to believe that's true.

[00:07:42] So I just wanted to set that up and see if my context was correct, because I think that the challenge in the imaging part that you're talking about is they're looking for certain things and they're looking for big things.

[00:07:53] And they're not really capturing and documenting these subtle changes that would be like, you know, in a blood test for CPR or when I'm watching for end-stage renal disease or diabetes, right?

[00:08:04] My doctor is plotting my progress.

[00:08:06] There's a huge opportunity to document and plot progress there.

[00:08:10] But to your point, we don't record it and we don't have maybe the people to read it and all these other things.

[00:08:16] So I just, am I onto something?

[00:08:17] Is that kind of the bigger picture here?

[00:08:19] You're spot on, actually.

[00:08:21] There are a couple of things that you said that I want to highlight.

[00:08:23] So for sure, that saying of the eyes are a window to the soul or to the body and health is how we think about it.

[00:08:31] And the reason is exactly some of the elements you were pointing out.

[00:08:35] The retina is the one place in the body that we can directly and non-invasively visualize vessels.

[00:08:42] So the veins, arteries, capillaries, and also it is part of the central nervous system.

[00:08:48] So there are a ton of biomarkers for systemic disease, cardiovascular, hypertension, neurological disorders.

[00:08:56] And this has been something that even before the fancy equipment you were discussing, like back in the 1850s with the ophthalmoscope, someone that is well-trained can actually peer into someone's eyes and actually see a lot of these features.

[00:09:11] And you were spot on with sort of what the space is called, I believe, vasculometrics, like tortuosity, density of vessels, etc.

[00:09:19] Or if you're having exudates and other features, but eventually also features that you can imagine they're not necessarily explainable by the human eye can be potentially picked up by AI once you have enough data.

[00:09:32] The way that I think about this and why I'm so excited is exactly that there is the first part of the problem statement is capturing good quality data.

[00:09:43] The second one is having the tools that scale sort of analyzing the good quality data.

[00:09:49] So we are focusing on building the platform that enables capturing that good quality data to become ubiquitous.

[00:09:56] So you don't really need highly trained personnel to actually do this.

[00:10:01] And you can do it close to the patient or the way we're starting to speak about it, the healthcare customer.

[00:10:07] And then, yes, your retina is individual.

[00:10:10] It's as individual and personalized as our DNA.

[00:10:13] And it is, as we spoke about, very informationally dense.

[00:10:18] And my vision, I guess, for this will be you do establish your own baseline and we monitor changes over time.

[00:10:27] The exciting thing about the way I think about it, and especially in the context from a business model perspective,

[00:10:33] is having at the beginning a use case that has a clear sort of reimbursement and regulatory pad is really why we have focused the company the way we did,

[00:10:46] to really create that framework and infrastructure, launch our first product, and then start building on it,

[00:10:53] what I think is going to be for sure revolutionary.

[00:10:56] I just wanted to point out to the audience something that you said, which I can tell is in the fabric of who you are.

[00:11:02] You said the customer versus the patient.

[00:11:05] And to me, patient is the passive participant and the customer is someone who you're trying to satisfy.

[00:11:12] So I just think that I sent you to that purposely.

[00:11:16] And I just wanted to point that out for the audience because I think that we're not there yet, but I think that's where we're going.

[00:11:22] So, yes, for sure.

[00:11:23] And it's something I feel very strongly about.

[00:11:26] And it is something I'm sure, you know, Eric Topol, like he speaks quite a bit about, you know, patient, patient, right?

[00:11:32] But it is.

[00:11:32] So I definitely see this as a general trend.

[00:11:36] And I also believe this is the right way to think about it, that the patient is at the center of all of this and enabling the patient to have the data and the information and the tools they need to be more proactive about their health is really going to make an impact.

[00:11:53] And in some cases, it also means educating.

[00:11:56] So thinking of the patient as the health care customer and treating them with the respect and the support they need to be a part of the solution, I think it makes a ton of sense from where I sit.

[00:12:08] Now, where do you see your organization fitting in the health care continuum in the long run?

[00:12:12] In the short term, you're trying to improve the vertical in the space.

[00:12:16] But where do you see it fitting in the broader picture in the next 10 years or so?

[00:12:20] So in general, I go there is speaking a little bit to that whole shortages, et cetera.

[00:12:27] It's like it's figuring out how to triage, right?

[00:12:29] The way that I see technology and AI in particular is about understanding inefficiencies and closing gaps.

[00:12:38] And maybe it's my approach of being more sort of like respectful to sort of like, you know, I have the greatest respect for health care practitioners.

[00:12:45] I think it would be really great to make sure we give them tools so that they can practice at the top of their license and they don't waste their time on things that we understand well.

[00:12:56] And we know that AI does better than humans, like repetitive tasks, like looking at a ton of images or right.

[00:13:03] So I definitely see it as really helping us create efficiencies.

[00:13:08] And as we learn more, it means that we can create more and more efficiencies and really scale and make sure that everyone that needs treatment and everyone that needs support has access.

[00:13:19] The beauty of the platform we're building is also that because of the way that is set up, a lot of the future tests and the support that we are providing is going to be very inexpensive.

[00:13:31] We don't have consumables.

[00:13:33] We're not like, as we're saying, like a blood exam where there's a lot of physical processing.

[00:13:37] It's we have a camera that takes a photo and then we have AI that can do all kinds of different levels of analysis.

[00:13:44] So I definitely see it as enhancing and broadening the reach and accessibility to everyone.

[00:13:50] So you dropped these subtle words that are really impactful.

[00:13:53] You said working at the top of your license.

[00:13:55] And so I did a three month section on just interviewing nurses and nurse practitioners and PAs.

[00:14:02] And they all said that they're not allowed to really go to the extent of what their license has allowed them to do.

[00:14:10] And with physician shortages coming and with rural health being a challenge, I commend you for including that in your mission.

[00:14:17] I think that's important.

[00:14:18] Thank you.

[00:14:20] So the other thing that strikes me is there's an efficiency component, which is the first part, right?

[00:14:25] You've got to find your customers and say, adopt this new thing.

[00:14:28] And here's the benefit of it for you.

[00:14:30] But then you have the other aspect of technology, which can it tell the system something that it's never been told before with this early warning sign.

[00:14:39] So I can see in prevention and population health.

[00:14:42] And even like when I was at Boston Scientific and J&J, we would fund scanning, carotid scanning or any sort of scanning as a way to give back and get people to be detected earlier.

[00:14:54] And there's so many companies out there like Lifeline and all those other companies that do these scans.

[00:14:59] I imagine that your product can eventually contribute to that greatly.

[00:15:03] For sure.

[00:15:04] That's the dream.

[00:15:05] And I think there are enough studies out there to show, as we said, the potential of the retina to show risk scores for aging or cardiovascular risk, etc.

[00:15:17] That I think is really going to be a key part, I think, in enabling personalized medicine.

[00:15:23] And then back to the being able to give back to the community and support and truly in the like when we speak about access and democratization, which is always at the back of mind when you're thinking of, right?

[00:15:38] Like, OK, so now rather than having hospital systems, we're going closer to patients at home, etc.

[00:15:43] And then you always have to stop and think and say, there are some people that don't even have a home.

[00:15:48] Like, right, like how are we as we're thinking about the technologies we build to enable health care?

[00:15:54] How do we make sure that we don't lose sight of the fact that a lot of the people that we can really help and make a huge impact up front are people that won't necessarily be the first to know about this and think about this?

[00:16:07] So I'm really excited about, as I said, the potential of the platform we're building to really help with personalized medicine in a democratized fashion.

[00:16:18] So your industry right now, how many people are in it?

[00:16:22] Is it small and emerging or is it a lot of people going after this at this point in time?

[00:16:27] So this is an interesting kind of like maybe like I'll speak a little bit.

[00:16:32] So retinal imaging and using AI for interpreting, I'll call it retinal images, either for diabetic retinopathy or some of the more systemic disease products.

[00:16:45] It is an area that has been or has quite a few players, especially in the last 10 years since we started the space at Google.

[00:16:52] So having said that, and in diabetic retinopathy in particular, there are three devices, we'll call them, right, from a regulatory perspective that have been cleared.

[00:17:01] The problem with this is that if you haven't addressed the workflow problem, you still need a human that is qualified to, right,

[00:17:12] and retains that kind of level of information that is required to capture a good quality image.

[00:17:17] You haven't really solved the workflow problem and an access problem.

[00:17:21] You still need to do that and then you take the images and you upload them in a piece of software that gives you an output.

[00:17:28] There have been quite a few of the companies that have built some of the instruments you were describing from your ophthalmologist office that have said,

[00:17:36] we're going to try to bring this to primary care, closer to patients, closer, more accessible in some cases in retail.

[00:17:42] And those have not been as easy as one would have thought because they have been designed for a different purpose.

[00:17:50] We've had to use such instruments for some of our clinical trials when we're doing comparisons to show when we're capturing an image and we're analyzing that image with our algorithm

[00:18:01] versus if the patient got dilated and got imaged in the standard of care and humans read it.

[00:18:07] What did the data look like?

[00:18:09] Those are not easy.

[00:18:10] Like it takes weeks to actually qualify a photographer to make sure that they can actually do this, like going through menus.

[00:18:17] So it's not easy.

[00:18:18] So there are a lot of players.

[00:18:20] The space is super segmented.

[00:18:21] And as I said, to me, it was really fascinating that for a space that has been around now like about 10 years or so, a little bit more,

[00:18:29] there hasn't really been a fully integrated solution, which is why we decided to actually pursue this.

[00:18:35] And I'm super excited because, again, like the way that we're addressing it hopefully fits in with the unmet need of, again,

[00:18:43] like making it so that the staff that you have, the medical assistant that setups the patient to be seen by a clinician can also run this.

[00:18:54] And when the clinician comes in, I think there's already images eventually with the AI, also with a report.

[00:19:00] But at least at the beginning, even with human readers, at least we capture your images, we analyze them,

[00:19:06] and we'll come back to you when the rest of your scores are coming, your A1Cs and et cetera, et cetera, to actually assess the plan and what you need to do next.

[00:19:15] So what was your specialty in engineering?

[00:19:17] I'm curious now that most of you talk.

[00:19:19] I am a chemical engineer and physical chemist.

[00:19:22] Ah, the smartest breed there is.

[00:19:24] Okay, now I get it.

[00:19:25] That's got a lot of process to it, too.

[00:19:27] I was just curious.

[00:19:28] I'm sure she has to be a chemical engineer in IE.

[00:19:30] I wasn't sure.

[00:19:31] But why is it that you saw flow working into the workflow?

[00:19:36] Because I do a lot of strategy consulting, and I also teach courses on startups.

[00:19:41] And so you get all these PhD brilliant professors that come out with the technology,

[00:19:45] and they're fighting the value chain of the system or how it goes together or how the money flows or whatever.

[00:19:50] How did you see that in others who have been around for 10 years that have missed this completely?

[00:19:55] So I think for me, it goes back to you enter a space, the first thing you do is like try to speak with people.

[00:20:03] And I've seen it.

[00:20:03] It's more I've been aware of the importance of, again, not being fascinated by a technology for the sake and the beauty of the technology,

[00:20:14] which don't take me wrong as an engineer, like there's something inherently fascinating about tech.

[00:20:19] And for me, at some point, and it was early on in my career when I started realizing I don't want to be the kind of person that has a hammer and sees every problem as a nail.

[00:20:29] But I really want to figure out what's the best tool set to actually solve that particular problem.

[00:20:36] And then there's the second piece to that, which is I think it's like it's an Einstein quote, right?

[00:20:41] Like if you have a fixed amount of time to solve a problem, like really spend most of the time really understanding what the problem is and then think of the solution.

[00:20:50] That has been a like, again, an evolution through my own mistakes around me that really just has been the lens that I see the world, maybe if you wish.

[00:21:01] So in general and with the especially when retinal screening and for diabetic retinopathy, you start to think about what has been one of the biggest issues.

[00:21:13] Here is a test and it's reimbursed.

[00:21:15] No one was like if you speak to people about the fear of losing their eyesight, like that is huge.

[00:21:21] So people are motivated.

[00:21:24] There is insurance coverage.

[00:21:26] Someone will really will pay for this test and people don't get screened.

[00:21:29] So you're starting to poke and ask for what's the rationale.

[00:21:34] Patients sometimes are not aware.

[00:21:35] They're not aware until they're in pain.

[00:21:37] But then also it's the especially when you're starting to think about diabetes as a disease and the correlation to the socioeconomic status access.

[00:21:48] It's already hard for someone who's working on an hourly rate, et cetera, to actually take time off to go see their doctor on the first place.

[00:21:56] And then they have to take a second day off to go and see an ophthalmologist.

[00:22:01] There are just so many barriers to actually getting there.

[00:22:04] So bringing the test closer to the patient where we know 80 to 90 percent of people living with diabetes will see a primary care doctor.

[00:22:14] About 40 will actually go and see an ophthalmologist.

[00:22:17] All of these things are just starting to make like a natural kind of just logical kind of determination that, my gosh, we have to figure out how to actually make that happen.

[00:22:29] And we speak with people.

[00:22:30] I've always maybe I'll just I say this and I have to be thoughtful of how I say it.

[00:22:34] And we have applied consumer product development principles to medical device development.

[00:22:42] And I have felt very strongly about I don't want to be building products that are not like don't have that same beauty of it should be intuitive.

[00:22:52] You should pick it up like these are busy professionals running the test.

[00:22:55] Like, why wouldn't it be as simple as turn this damn thing on and let it guide me and be very easy to operate?

[00:23:03] So I guess a mixture of things.

[00:23:05] So complicated way to answer your question.

[00:23:07] No.

[00:23:07] Well, because I've done a little bit of venture, but I've also been a plant manager and operations managers.

[00:23:12] I'm a little bit of a nerd.

[00:23:13] I'm sitting here saying, OK, so she's looked at where the inputs are.

[00:23:16] And they were the general practitioner and the ophthalmologist.

[00:23:18] She covered that.

[00:23:19] And then her process has people associated with it, materials of the process itself.

[00:23:24] And you said my stage gate is qualifying people.

[00:23:27] So you check that box.

[00:23:29] And then as in terms of an output, I've got a report.

[00:23:32] If no one's there to read it, there's not enough capacity to read it.

[00:23:34] It doesn't have impact.

[00:23:35] And you have a way of reading it that gives impact.

[00:23:38] So I'm thinking you checked all the boxes.

[00:23:41] And a lot of people don't think that way.

[00:23:43] And I bring it up because they just, you know, as you're talking very nationally,

[00:23:46] I just want to point out to people the complexity behind it and the elegance of its simplicity.

[00:23:51] So thank you.

[00:23:52] That was very MBA.

[00:23:54] I apologize to the audience.

[00:23:55] So where are you in the process of this?

[00:23:57] Are you in clinical trials?

[00:23:58] Have you launched or?

[00:24:00] So there are a couple pieces to the puzzle and they are in slightly different stages.

[00:24:05] So we have developed the what we're calling the identified camera, which is the fully automated system.

[00:24:12] We leverage AI and automation to make sure we can capture good quality images.

[00:24:17] We submitted to the FDA.

[00:24:19] We submitted 510K.

[00:24:21] And the FDA said that without the diagnostic or screening algorithms, we can actually market the device as an exempt.

[00:24:29] So we are preparing for that.

[00:24:32] And we're very excited about it.

[00:24:34] So we're working with a contract manufacturing organization.

[00:24:37] We are setting up our quality management system to be a commercial entity and register with the FDA as a medical device manufacturer.

[00:24:45] And very excited, preparing for our first commercial launch and starting to help patients and start working closely with healthcare professionals.

[00:24:52] At the same time and in parallel, we've been using the camera systems in clinical studies to collect data to finish the tuning of the first AI product for screening purposes for diabetic retinopathy.

[00:25:07] And we are also preparing for what is going to be, let's call it, the pivotal studies that will support the 510K submission for Identify DR.

[00:25:17] This is like a little bit we're trying to build our brand.

[00:25:20] So, yeah, that's where we are with that.

[00:25:22] Now, do you envision yourself over time being able to also incorporate yourself into a precision medicine model?

[00:25:29] For sure.

[00:25:30] That's the idea.

[00:25:31] That's the, again, like, I won't say the dream because I really like dream has something like very elusive about it.

[00:25:37] But that's really where we want to get to.

[00:25:39] So how do you collect the data to make that model?

[00:25:42] Okay.

[00:25:43] So here is the, and this is a little bit of a principle, too, that we thought about more like from the cancer diagnostics that was a space before, which is when you collect the first data from your patient or your healthcare customer, and of course they consent, you can look at their data longitudinally.

[00:26:02] So you can build a way to just start screening and say, am I looking at any differences?

[00:26:09] So you can imagine some of the things that you were saying that your ophthalmologist pointed to you in terms of just tortuosity or other things, maybe exudates and things in the retina.

[00:26:23] Maybe you wouldn't worry based on the age of someone or everything else, like things are looking in a particular way, or maybe there were some changes because nothing has been triggered.

[00:26:33] But now you have maybe a younger individual that all of a sudden has something that wasn't there six months ago.

[00:26:39] So you can see how the power of this to personalize and say, there is some change that is correlated on a population scale to potentially increased risk.

[00:26:52] And now for you, that ended up changing and it triggered you to actually like something might have happened.

[00:26:58] Could it be that you haven't been controlling your A1C?

[00:27:03] Could it be that you haven't been controlling your blood pressure?

[00:27:05] Could it be that, you know, you stopped exercising or sleeping or whatever else?

[00:27:10] So that's a little bit how I'm thinking about it, that it can become almost like a map if you wish, right?

[00:27:16] Like you have this very detailed map with features that if it changes, we monitor changes and it can become a sort of like a piece in the puzzle of keeping people healthy.

[00:27:26] So truly like reinforcing that.

[00:27:29] There's a loop in that too, because I work with a lot of pharma companies and precision medicine companies.

[00:27:34] So the first phase is exactly that, right?

[00:27:36] You identify there's 10 steps that we know what to do with and we have an ability to solve it.

[00:27:42] And it provides a doctor who's seeing many people to say, hey, something unique is happening here.

[00:27:47] Give it a little more attention.

[00:27:49] But the data over time also says perhaps between step one and step two, there's a new step 1.5.

[00:27:55] And that actually helps the pharma industry and the device industry, because a lot of times they can't identify the market for that subtlety.

[00:28:02] And the precision medicine systems are now actually going to provide the data that says there's something important happening here.

[00:28:08] We're not sure what, but you guys can look into it.

[00:28:11] We can quantify it.

[00:28:12] And now we can determine the impact on our patient population and even part of the public health.

[00:28:18] It's fascinating.

[00:28:19] So what thought leaders or innovators in AI do you follow?

[00:28:24] Because you obviously have to be pretty cutting edge as a person.

[00:28:27] Yeah, I follow a lot of people and organizations.

[00:28:30] And I think I'm also just very grateful for some of the people that I've gotten to work with at Google that now have gone on to either be on the VC world or startups, etc.

[00:28:44] I almost feel like even if we look actually at one of our investors, Foresight Capital, and if you see the work that they are doing in general and startups, they're starting their therapeutics.

[00:28:55] I think speaking of AI for drug discovery and rethinking how we do clinical trials, there are just so many that I almost feel like I'm almost horrified.

[00:29:05] I'm like, I'm going to say a few names and then I will miss the others.

[00:29:08] But as I said, I'm fortunate that even in my early days at Google X, I had partnered up with folks like Jeff Hubert was one of my early mentors, like Vic Bajaj.

[00:29:20] And that's like now he's at the VC front.

[00:29:23] So these are people, you know, his team, like these are a lot of former colleagues of mine that I admired.

[00:29:29] So they're just a group of incredible people, like obviously Philip Nelson and the DeepMind folks.

[00:29:36] Like Google, like this is, it just has been, again, I'm fortunate to have gotten to experience working with incredibly talented people in this space.

[00:29:44] I feel very strongly about using tech to make an impact and help people around them.

[00:29:51] And yeah.

[00:29:51] So our audience likes to ask where people like you go and they wonder if they're an AI magazine or something that you find to have a lot of content that's helpful.

[00:30:03] I don't actually go to magazines.

[00:30:05] Like I follow a ton of people on LinkedIn.

[00:30:08] So that's probably where I get a lot of my news and staying on top of things.

[00:30:13] I'm not a big Twitter fan, which I know, like maybe I shouldn't say out loud, but this is like I have a Twitter account.

[00:30:18] I feel because I have to almost like if it makes sense.

[00:30:21] I think we're in the same place right now.

[00:30:23] Yeah.

[00:30:24] There's not a lot of content going on during elections that are valuable.

[00:30:27] Right.

[00:30:27] Yes.

[00:30:29] But yeah, I think it is primarily if I were to actually say where I get things, it is primarily my news feed and my, which has, you know, again, like AI has been inherently embedded to help cater to my things that I like and trends and tech in general.

[00:30:46] And then, yeah.

[00:30:48] That's awesome.

[00:30:49] So what do you see as the biggest opportunity and threat as you commercialize this business?

[00:30:54] What are the things that keep you up at night?

[00:30:56] And what are the things that excite you?

[00:30:58] Ah, okay.

[00:30:59] So I'll start with what excites me, which is, of course, the ability to start helping patients.

[00:31:05] Like that is like there's nothing that at least they can make me feel better.

[00:31:10] The things that keep me up at night is, you know, we're a startup.

[00:31:14] Timing the thing sometimes is just very important.

[00:31:17] So we have a lot of really incredible commercial milestones next year and I am fundraising.

[00:31:24] And so, of course, like in my mind, like there's the balance of supporting my team, making sure we meet all of our milestones because staying on track to our schedule is really critical.

[00:31:36] Making sure that a lot of the early discussions we're having with health systems and people who will be potential customers to pilot and then scale like that.

[00:31:47] That needs to move forward on time.

[00:32:19] You know, we don't speak to as many startup people in this podcast, but I think I wrote a book on startups.

[00:32:28] And if you haven't written a book before, you just end up in a place that you never thought of.

[00:32:31] And one of the things that came out is you have to balance satisfying the customer, the investor and the acquirer of a liquidity event.

[00:32:39] And it's a simultaneous equation.

[00:32:41] And the challenge for the CEO is it's really hard to be the inside and the outside person at the same time.

[00:32:47] You never have enough resources to have enough people to cover the inside.

[00:32:50] How do you balance that?

[00:32:52] Well, first of all, I have built a really incredible team.

[00:32:55] So there is like I do have partners for specific things and I trust my team tremendously.

[00:33:01] But I also it means that I need to be there to support them and make sure that.

[00:33:05] So there is a part of my time that is always dedicated to check ins with the team, whether it's like four teams still involved with product development and whatever needs to happen.

[00:33:15] Like we we still have gotten through the finish line.

[00:33:18] But also having built the relationships with the team that they know that if it happens that I'm traveling and maybe I don't have as many meetings with them as I usually do this week, they know to text me or ping me to say there is something like we really need you to weigh in on.

[00:33:35] So there is a little bit of the relationship and the trust there.

[00:33:37] The rest, it's more like I think you can do some of this where you're like, it's OK, like I I'm going to push through because right now it's also like a game of numbers.

[00:33:49] Like the more time you have with more people, the more exposure or awareness you're creating.

[00:33:55] And I have found a way to sort of like do things like, you know, I'm going to health conference in a week or so.

[00:34:02] Well, I will be speaking to multiple types of key stakeholders.

[00:34:06] I'm making time to balance and make sure I really want to learn from potential customers.

[00:34:11] And I also get them to learn about us, do some information gathering and exchanging, et cetera, and also speak with potential investors.

[00:34:19] So it's working.

[00:34:21] I have a lot of energy.

[00:34:22] So like maybe that's the other thing that does help with this thing.

[00:34:24] It's like I'm just really, truly excited about what we're doing.

[00:34:28] So speaking with someone about what we're doing, it's almost like it gives me energy back.

[00:34:32] So somehow it is all working.

[00:34:35] So you had said something that I was someone who's done startups.

[00:34:39] It's very personal.

[00:34:41] And you can when you help a patient, you actually know it's Mrs. Jones or Mr. Jones.

[00:34:46] You're very specific versus when you're in a big company with a thousand sales reps, you don't get that connectivity.

[00:34:52] And that also drives a lot of energy and a lot of passion.

[00:34:55] And I can certainly see that in you.

[00:34:57] So congratulations.

[00:34:59] Thank you.

[00:34:59] Is there anything else you'd like to share with the audience?

[00:35:01] I think, no, I mean, in general, like I'm just very excited.

[00:35:05] I consider myself extremely fortunate to be at a place that I work on something, as you said, that I really feel super passionate about.

[00:35:14] And that's maybe like a general advice that I give to people more on the, you know, if they're contemplating, like, is this ever worth it or et cetera, et cetera.

[00:35:25] It's like, you know, if you're doing something that you're passionate about, it's almost like it doesn't feel like work.

[00:35:30] Yes.

[00:35:30] I mean, there's about, like I say, it's like 20% of my job that I'm like, really?

[00:35:34] Why are you doing this?

[00:35:36] Yes.

[00:35:36] I do love my job.

[00:35:38] So working on something you're passionate about helps you make more contributions.

[00:35:43] And it also makes sure that you are a happier and more pleasant person to be around and to work with.

[00:35:49] Well, thank you for being our guest today.

[00:35:50] I appreciate it.

[00:35:51] Thank you for having me, James.

[00:35:52] I enjoyed spending time with you.

[00:35:54] My pleasure.

[00:35:56] Thanks for tuning into the Chalk Talk Gym podcast.

[00:35:59] For resources, show notes, and ways to get in touch, visit us at chalktalkgym.com.