On this episode of Navigating Forward, host Lisa Thee sits down with Dr. Michael Blum, Chief Digital Transformation Officer at the UCSF Center for Digital Healthcare Innovation, and Founding CEO of BeekeeperAI. Lisa and Dr. Blum discuss the potential of artificial intelligence and machine learning in healthcare to enhance our health and improve patient outcomes. Dr. Blum also shares how his medical expertise and background in engineering led to BeekeeperAI - a secure collaboration platform for algorithm owners and data stewards that will accelerate AI Innovation in Healthcare and the future of precision medicine.
Navigating Forward podcast is brought to you by Launch Consulting. www.launchconsulting.com
At a crossroads of uncertainty and opportunity. How do you navigate forward? This podcast focuses on making smart choices in a rapidly changing world. We investigate the challenges of being at a crossroads and finding the opportunities that arise out of disruption. Listen, in on future forward conversations with the brightest luminaries, movers and shakers, let's navigate forward together and create what's next.
Lisa Thee (00:25):
Hello everyone. And welcome to the Navigating Forward podcast. My name is Lisa Thee and I'll be your host today. We love to collect the world's best luminaries, visionaries, and innovators to bring conversations to you about their area of expertise. Today, I have the luxury of interviewing the CEO and co-founder of BeekeeperAI, Dr. Michael Blum. For the UCSF hospital system, he is focused in on helping the Center for Digital Healthcare Innovation and making sure that he brings his medical expertise, along with his expertise in engineering to innovate healthcare and to improve patient outcomes. And we're thrilled to have you here today. Thank you, Dr. Blum.
Michael Blum (01:04):
Thanks, Lisa. Pleasure to be here with you.
Lisa Thee (01:07):
So can you tell us a little bit about your background? Where are you from originally?
Michael Blum (01:11):
I grew up in Southern New Jersey outside Philadelphia. So, right there in the, the shadow of the University of Pennsylvania and spent my whole childhood down there until I left for college and went up to Boston.
Lisa Thee (01:28):
And is there anything about your childhood that you think shaped the work that you do today being at the intersection of technology innovation and healthcare?
Michael Blum (01:36):
Oh yeah. Well, so, my father was a huge, huge impact on me. He was a doctor and he was initially an academic hematologist and then became a private practice hematologist. And so I think, watching his life and just, you know, observing and seeing, you know, how he worked and, and what really lit him up, hearing about, you know, stories about his patients, especially when he took care of kids and seeing the, the great experience when, you know, he'd be able to really help someone or in the, you know, sad times when he couldn't was really, I think that was formative, but also he was, he was very much independent and, you know, he could take care of anything, fix anything, improve, anything, build anything. And so I grew up basically doing all of that with him. So, there was nothing I couldn't take apart and, sometimes put back together and sometimes it worked. But it was just the way I grew up was there was nothing you couldn't do. There was nothing you, you know, couldn't fix or any project you couldn't take on. And I think that became a big part of me, of you just do things.
Lisa Thee (02:52):
Sounds like the apple doesn't fall too far from the tree there. So, both of you sound like Renaissance men to me.
Michael Blum (03:00):
Yeah. Well, thank you.
Lisa Thee (03:02):
<laugh> So for someone that doesn't know about your area of expertise and what you're solving with BeekeeperAI's mission, can you tell us a little bit about what the mission of the company is and what you're bringing to the market?
Michael Blum (03:13):
We're at this incredible time in, in healthcare where we're seeing the impact of really powerful new technologies that have really transformed other industries and transformed our way of life. So artificial intelligence and machine learning have been with us for, you know, better part of a decade or so now. And they've, you know, changed us in the ways that if you think back to when you, you know, had to go to the bank or when you had to go, you know, rent a DVD or things like that, you had to go to the store, even the fact that we don't have to do any of those things anymore. They're all cause of the way that, you know, artificial intelligence and machine learning have impacted our lives. That's not true in healthcare. We're just very, very early in the implementation of those technologies in healthcare.
Lisa Thee (04:04):
So you saw an opportunity that was, healthcare is right for disruption by bringing some of those technology innovations closer to the clinical setting.
Michael Blum (04:12):
Well, so in the, in healthcare, we're, you know, it's because it's a very human undertaking, a very complex administrative undertaking. It takes a longer time for technologies to penetrate that hopefully will be improving as the technologies become more generalized and easier to bring in to, to the healthcare environment. But however, you look at healthcare is a really complex undertaking. You have a highly regulated industry that deals with people at the most critical junctures of their lives with fairly dangerous things that we do to people sometimes in order to try to save them. So there's a lot of, a lot of activity going on, very complicated clinical and administrative undertaking. So it moves and it changes slowly, which on some, you know, some aspects is really good. It prevents us from making mistakes by moving too quickly, but in a world where people are used to, things moving more quickly and much better levels of experience and service and increasing expectations, healthcare is becoming, you know, further and further away from where we would like it to be at the same time, the cost pressures on healthcare, both for what patients are having to pay and payers are having to pay in the cost of delivering the new technologies is getting to be a bigger and bigger problem.
Michael Blum (05:28):
And our ability to safely deliver the improvements is not where people would, you know, really expect it to be. So we really need the, you know, these AI machine learning technologies to really have a significant impact, reduce the burden, reduce the, the manual burden that goes on in healthcare a lot. So as we started working at the Center for Digital Health Innovation to start leveraging these new technologies in, in our collaborations, we learned that, one of the big barriers to developing AI technologies that can really impact healthcare broadly and in a generalizable way and apply to large groups is you have to have very large data assets. You have to have access to very diverse, high quality data assets. When you're developing these machine learning models. It's pretty straightforward these days because of excellent toolkits, to develop a machine learning model that performs pretty well on a very narrow data asset and therefore on a narrow slice of patients.
Michael Blum (06:32):
And we've seen that there are many, many good examples of, algorithms that perform at a relatively high levels of accuracy on the narrow populations that they were trained on. But when you try to generalize them, the accuracy falls off sometimes to the point where it's actually highly inaccurate. So the lack of access for the developers to these very broad, highly diverse data sets, is a problem. And the reason that it's hard to get access to the data is the data's very sensitive. And the groups that we call data stewards, who are usually the large academic health centers or large community health delivery organizations, their primary responsibility is to keep that data safe. It's highly sensitive data that could be very negatively impactful on patients if it was released. So they have to keep that data private and safe at the same time, they need to leverage that data to support their mission. They have to use it to enable discovery and enable new medications, new devices to better the care of their patients. So they've got this conflict between keeping the data private and safe and pursuing their mission. So, as we were thinking about how that is really casting this freeze on AI development at the Center for Digital Health Innovation, we said, there's got to be a better way. How can we really approach this differently? And that became the development for the, the BeekeeperAI platform.
Lisa Thee (08:01):
So as the Chief Digital Transformation Officer, you had competing goals that you got to see in your clinical practice as an acting cardiologist, as well as trying to bring forward more informed tools to help accelerate the patient experience and have it be more frictionless in terms of what, how the patient experience as a healthcare system, as a whole. What I have, read historically around some of the problems that you're trying to address is that to get FDA approval today, it's a very long process and a very expensive process to make sure that you can prove that your model will perform as well as it does in Boston as in Bangalore. Correct. Can you help us understand a little bit of the impacts of what it's like today to try to get FDA approval for clinical usage of an AI model?
Michael Blum (08:48):
So there are two pieces to that and, and you're absolutely right. It is a very long process. And one of the initial barriers in that process is getting access to the data sets that you need in order to validate that your algorithm or your model will perform well. It'll generalize across patients broadly. And that's what the FDA's expectation is that that this model will perform as expected across a broad array of patients, essentially all the patients that it's expected to see. And as you point out that could be a very broad population. So that means your, your validation data sets. And even before that your training data sets need to include that broad population or representative, segments of that population. So what's that like now? Well, it's nearly impossible. At UCSF, I was the Chief Digital Transformation Officer, even as UCSF when we were developing these models at the Center for Digital Health Innovation, it took a long time to get access, to that level of data across the nation or globally, we're working with GE Healthcare as a partner to develop some technology that now runs on their portable chest x-ray equipment.
Michael Blum (10:00):
And it took us, you know, a better part of a year as two large organizations with a very important project to get access to that amount of data. It's also very costly. So the current metric is it takes organizations around 18 to 24 months and cost between a million and a half and $3 million to assemble data sets that are broadly representative enough to create a, an adequate training and then validation environment for a model that's going to be involved in either diagnostic or therapeutic purposes. So one that's going to be used to diagnose the condition like the ones we develop for portable chest x-rays or something that's going to be related to a drug or a new digital diagnostic takes years to get access, and then to use that data.
Lisa Thee (10:49):
So what I'm hearing is for people that are focusing their energies on innovating in healthcare in order to have something that could be run in a clinical setting, we're talking about millions of dollars and years of time to even be able to test whether that algorithm could be useful to frontline person, that's trying to help a patient in distress.
Michael Blum (11:10):
That's exactly right. And if you go from there to, you know, life sciences, biotechnology, and pharma, who's developed a new drug or new molecule that could be really helpful to patients that year and a half that it takes to get through that process. And any time that's added to that is hugely expensive. The patients aren't getting that, and that's millions of dollars a day of value that the, that the company isn't able to deliver.
Lisa Thee (11:34):
With that in mind. Can you help me understand a little bit about what Beekeeper is doing to start to address some of those challenges?
Michael Blum (11:40):
Sure. So Beekeeper is a novel platform. That's all built around security and trust. So the concept is that we can improve access to data for algorithm developers, by making the data stewards much more comfortable that their data is safe and secure. The way we do that is by kind of changing up the whole data access conversation. Currently, you make a legal agreement and some business agreement to access the data, and then you send it to the algorithm developer. We've created a, a platform where the data does not need to move. So the data stays with the data steward. The data is never seen by any third party. So there's no risk of, privacy breach or data loss. So the data stays in the data-stewards protected, you know, HIPPA compliant environment currently in their Azure cloud environment. And then we use secure compute enclaves as the compute technology that brings the algorithms to the data, allows them to run and return performance results, but they, no one ever sees the data. So the algorithm developer doesn't see the data. The algorithm only operates on the data in a protected environment where none of the data can leak.
Lisa Thee (13:00):
So in this model, what you're able to leverage is technologies, including cloud technology, federated learning, and confidential computing and zero trust environments, bringing all of those innovations together in one space to make it so that as somebody who is trying to innovate in healthcare can prove that patients with, personalized precision medicine can benefit from certain treatments by being able to query the information without actually giving up the HIPAA protected information about individual patients. Is that right?
Lisa Thee (13:34):
Exactly. You said that better than I could <laugh> so that's exactly right. We can maintain the privacy protections for the patients. The data stewards can leverage their data assets to improve healthcare improved delivery. And they also can create a new revenue stream where they, what was previously a big cost center for them now allows them to participate in, in these new innovations and new developments. And they can work with partners who they, you know, may have not been able to work with in the past, because now the data is safe.
Lisa Thee (14:07):
Yeah. And what's so exciting about it from my point of view, coming from, the hardware side of things earlier in my career at Intel is that it really took the innovation of the most recent processors to even make this feasible in the cloud right at scale. And you guys are the first movers to actually take advantage of it and, and really improve the healthcare system that today suffers from $20 billion a year in medical mistakes and is missing opportunities to do more precise medicine, to improve outcomes for patients.
Michael Blum (14:41):
Yeah. It's one of those stories where it's all about being in the right place at the right time. So the concept of confidential computing is not brand new. What is brand new is the power of compute and the ability to really run, take these encrypted data sets and the encrypted algorithm, de-encrypt them in at run time in processor memory and then compute on them and everything stays completely protected and say, even a corrupted operating system wouldn't really allow the data to, to be pilfered or taken or inappropriately accessed. So you have to have all of that compute technology, which is just now available and in Intel's latest processors. And that kind of pushed us over the edge to being able to really make the whole, you know, the whole environment work. Right.
Lisa Thee (15:28):
And what's fascinating about that is a lot of workflows have been managed by legal to protect information and make sure that everybody's aligning with regulations with this AI innovation and confidential computing, cyber security innovation. You're able to remove some of those workflow guards that are in place by people to keep the information safe, because it's so sensitive and replace that with automation. And so can you share a little bit about what kinds of benefits patients will see in the future by allowing to have more AI in clinical settings? Can you give some examples of how that might show up in someone's life? If someone they care about is ill?
Michael Blum (16:09):
Even before I get there, I think you, you touched on a critically important point is the way this stuff works now is people try to protect the data with legal agreements and they take very long to get in place. And a lot of times people can't come to agreement on those and projects that just don't get done because of it, it takes, you know, sometimes a year to craft a legal agreement that theoretically protects the data. But at the end of the day, there is no protecting of data. If the data's moving, once the data's out of someone's hands, you know, off your system, you can't protect it anymore. So there's more reason that even with agreements, people are still very, you know, concerned about it and Beekeeper eliminates all those concerns. So what, what can we do with it? I think that the, the real excitement here is in, in the world of AI and in the world of democratized use of these tools, we don't have to predict anymore what we can do.
Michael Blum (17:01):
We're going to open it up to a huge world of developers who are going to develop new technologies that we haven't thought we needed yet, and that are going to remarkably improve patient experience. But I will give you a couple examples, you know, from my, my head as a clinical cardiologist. So I frequently have discussions with patients about risk of a cardiovascular event, risk of a heart attack, risk of a stroke. And a lot of the medications that we use, the statin medications dramatically reduce the relative risk of a stroke, but sometimes they have side effects and people don't like to take medicines. And so I have to have this discussion of, well, let me tell you about what your, your risk is of something bad happening. Similarly, people have atrial fibrillation, they can develop blood clots that can give them strokes. So I have to have a discussion of, well, here's what your risk reduction of the stroke is.
Michael Blum (17:50):
But, here are all the side effects. Here's the bleeding risk. And so on all of those numbers that I talk about, and the risks I talk about with patients are very abstract to them because they're built off of clinical trials that have broad populations in them, and they don't really refer to them as an individual. They don't tell you okay, for an African-American individual, who's 56 years old lives in this place, has this family history takes these other medications. This is the specific risk to them. If you take this medicine, it will lower your reduce of a heart attack or a stroke over, you know, the next three years. All I can tell them now is the whole population that was in these studies over 10 years. This is how they did, and that's not relevant to people. That's not motivating enough to get someone to really do something, to start exercising, to lose weight, to change their diet, to take a medicine every day.
Michael Blum (18:44):
It's very hard to get someone motivated based on a whole population and 10 years' data. Well with the new technologies and new things that are evolving like proteomics, I'll be able to tell you your one and a half year risk is this. And then by changing your behavior, we can watch and we can reassess. And we can say, "oh, look at that." It's, "you've gone down from a, a 7% risk of a stroke in a year or a heart attack in a year down to a 2% risk. Good for you. That's an accomplishment. Let's keep up with that kind of work and keep improving things." That's very impactful to people seeing the change and seeing, being able to have a discussion that applies to them as an individual is, is hugely impactful.
Lisa Thee (19:26):
Yeah, it's a, it's a taking into account, their genes, their environment, and their conditions, and being able to personalize recommendations based on their genome and all their factors.
Michael Blum (19:38):
It's that precision medicine. Right? Right. That's precision medicine. I know about you. I know about all the data and now I can go to a very vast array of data. And how does that apply to you? That happens once you get Beekeeper in place to develop those algorithms.
Lisa Thee (19:53):
And so this isn't replacing doctors or specialists in their field, this is augmenting the information that they have to give it a more personalized answer to their patients, right? You still need a clinician. You still need somebody with expertise that can help walk the patient through their options. You still need the intuition investigation that humans provide, but the machines are able to provide some of that, tedious, repetitive mathematical tasks that help support reasons to make certain choices and decisions.
Michael Blum (20:25):
That's exactly right at the end of the day, the doctor job, or, you know, any clinician's job is that human interaction with the people to understand their, their situation, to empathize and to show them what a pathway forward could look like. That would be better for them. allow them to enjoy more of their life and, and have, you know, more joy out of life. The reality is though that the science and the data within healthcare are exploding at a rate much faster than any individuals can consume all that data, turn it into knowledge and convey it as wisdom. So we need, we need computers to do that for us, but we need computers that work in the background as, you know, as an assistant to us, and then as a trusted partner and all that requires AI to really make that happen. And we're not there yet, you know, for that same data problem.
Michael Blum (21:17):
So the way I look at this in, in the future world is that I have this trusted partner of the computer. They're helping me providing me with information and insights as I make those really difficult decisions with my patients about how they want to proceed and, and which pathways they want to go down when they have difficult choices, how I can make sure that I'm compliant with all the latest guidelines to provide them with the best care without me having to sit and go back. And re-review all these guidelines and look at all their medications that the, you know, the machines ought to be able to provide me with that information.
Lisa Thee (21:54):
They can be working in the background to bring you the right information at the right time to make sure that you are working alongside the patient, with the most relevant information to them.
Michael Blum (22:04):
Lisa Thee (22:06):
So with that in mind, you guys have launched your first product. Can you tell us a little bit about what that is and, how people can access it?
Michael Blum (22:15):
Sure. So EscrowAI is the first product to come out on the Beekeeper platform. EscrowAI is now available in the Microsoft Azure marketplace, which is incredibly exciting for us as, as a new company. Yes. So EscrowAI, you know, it's, it's a great name, cause it describes exactly what the product does. You know, in California, when we sell any piece of property, it goes into an escrow transaction where the property's protected, the financials are protected. And so this is the analog of that with data. So when there are two parties, an algorithm developer and a data steward who want to do a project together, but they want to protect it. They use EscrowAI, they're on the Microsoft Azure platform. The data steward puts their, their data that is going to be used in the, in the collaboration, into the Beekeeper platform, which runs in their environment and the algorithm developer uploads their algorithm to the BeekeeperAI environment, the two come together and that algorithm runs against the data it's pretty straightforward and it eliminates a year and huge cost in terms of getting access to that data.
Lisa Thee (23:25):
And could you give some examples of customers that may participate with the Beekeeper EscrowAI product so people can hear their types of job titles, or here are the types of problems and know this is the place where I can go to get some assistance?
Michael Blum (23:39):
Yeah. And, and it's amazing as we started down this road, we thought of the projects we were working on. So we thought about working with big device manufacturers and developing, you know, imaging, AI and things like that. Well, it turns out it's all across healthcare, everywhere you go, people need access to data. So you think of a large pharma, who's got a new drug and there's a digital diagnostic that goes along with it. They're going through a regulatory approval. And the FDA wants to see your, you know, EMA and Europe wants. What's the data that they develop this on. And how did you develop it? Beekeeper facilitates that. And EscrowAI makes that completely secure and it keeps all the regulatory artifacts as you go through the process. So it's great for, for pharma, similarly devices, both large device manufacturers, as well as, device vendors, need to document that their devices work, that their devices are safe and that they've been developed on data sets that allow them to be broadly applicable.
Michael Blum (24:38):
So Beekeeper not only allows access to that data, keeps it safe, but it also supports going through that regulatory process. Sometimes it's just a marketing process. You need to prove to a payer that your new intervention actually works well, that's the same thing. You've got to validate that algorithm. And that means you need a lot of that. You need access to a broad array of data, you know, back to, finding the data stewards on the Beekeeper platform, agreeing to a project and then running the project through. So it, it really applies to every angle that you can of be a startup. It could be device, it could be pharma. It enables much more rapid research collaborations. Now you don't need these extensive agreements between research partners because the data is safe. We simplify the contracting from what used to be a 40 page contract is now down to a standard four page agreement that everyone is already familiar with. So it facilitates the entire thing. The, the simplification of contracting turns out to be one of the major benefits of it. Like we talked about before.
Lisa Thee (25:43):
That's wonderful. And in that model, where pharmaceutical companies, original equipment manufacturers for devices, startups that are doing novel algorithms and research, hospitals are all seeking this information. Can you talk a little bit about what makes Beekeeper special in terms of the data? Because as we all know in artificial intelligence, your company is as good as your data mode. <laugh>, you need a lot of information to train things. Well, can you talk a little bit about some of your partnerships and what makes you guys special in terms of the access to high quality information?
Michael Blum (26:19):
Yeah. And it's, they are super important distinctions as you point out. So, the primary distinction is that BeekeeperAI allows you to get to the actual primary healthcare data. So when you're developing an algorithm, you want to develop it against the real data that you're going to run it against, but it allows the data stewards the data holders to keep that data in their protected cloud. So no data getting sent anywhere. That's one of the things, but again, it's the primary data. Most of the attempts to allow access to data now deal with deidentified data. So some groups try to provide access to large data lakes of de-identified data, or people try to use synthetic data to allow access to the health data. Our belief is that if you want to develop a high quality algorithm and you want to validate, you need to do it on the real data with de-identified data, you're changing the data by de-identifying it.
Michael Blum (27:17):
Plus it's been shown pretty conclusively that there's no such thing as de-identified data in the current age, that data that has been deidentified can be re-identified by use of other social data. So for those reasons, we're, we're not big fans of de-identified data and similar synthetic data, while it can't be re-identified theoretically, it's still different than the original data in its creation. It has been made to be different. And you can create aberrations in, in how the artificial intelligence algorithms and the models behave because it's not primary data. And so you don't really know what's going to happen. That's not where you want to be when you're taking care of a patient, you want it to be the real thing.
Lisa Thee (28:00):
Yeah. Stakes are too high to be making mistakes like that.
Michael Blum (28:04):
Right? So that those are the, the critical, you know, big differences is platform keeps data safe. You don't have to move the data outta your cloud. You never have to worry that it's going to end up in someone else's hands. And it's the primary data, which is what, you know, the industry needs access to, to develop the high quality algorithms.
Lisa Thee (28:22):
So looking at it from that Chief Digital Transformation Officer, you're allowing healthcare systems to innovate without the fear of ending up in the headlines.
Michael Blum (28:31):
Lisa Thee (28:31):
For having data breaches of HIPAA compliant information.
Michael Blum (28:35):
Yeah, exactly. I mean, as you know, the, the job of the Chief Digital Transformation Officer is to accelerate the innovation, to allow us to create and bring new innovations in more quickly. But we've been very limited that we spend much too much time figuring out how to not get in trouble. This the platform allows you to pursue the mission to improve healthcare for each patient while improving healthcare globally, which is what the UCSF mission is, allows us to do that as we continue to keep the data very safe.
Lisa Thee (29:05):
Can you talk about one of your proudest achievements so far?
Michael Blum (29:09):
I would say, one of the proudest achievements coming out of the Center for Digital Health Innovation. Well, it's always hard. Like if you ask me my proudest clinical achievement that goes in one bucket, proudest innovation, achievement, another, but I think the, from the innovation perspective, the, the achievements are nice because they affect so many people. And in, in this example, in our work with GE Healthcare, where we developed AI models that work on their, on their chest x-ray equipment, and they say lives at the point of care. And it's knowing that we were a part of, you know, a project that actually is running around the world now and really impacting patients in critical care situations and saving lives on a daily basis. That's like, you know, for me as a clinician and as an innovator, as an entrepreneur, knowing that something is out there around the world, saving people's lives, it doesn't get any better than that.
Lisa Thee (30:05):
Absolutely. So, as we've discussed before in prior conversations together, innovators live in the future, not in the present, right. So where do you see us in five to 10 years when Beekeeper has democratized high quality information for algorithms, and we've gotten many more novel algorithms into clinical hands, through FDA approvals, what do you see is the impact to humanity when we have this at scale?
Michael Blum (30:32):
Yeah. So there are two questions in there. One of them is, is what does it look like when Beekeeper is successful and I'll know BeekeeperAI is successful when people are talking about, oh, I want to do this really cool project. And I need a lot of data. And someone says them, oh, just go use Beekeeper for that. And then becomes, you know, maybe it won't be like BandAid or Kleenex, but BeekeeperAI is the way you, you do these kinds of undertakings. Well, no Beekeeper
Lisa Thee (30:56):
Come join the hive!
Michael Blum (30:57):
Exactly. Use the hive. What is, you know, what does better look like, you know, globally for healthcare and, and, for our patients, it's when the discussions that I have with my patients, have been transformed to, I can tell them for them as an individual, I can really have those precision medicine discussions around which medications, which lifestyle modifications, which interventions we should do. Do we need to do a cardiac catheterization? Do we need to fix your heart valve? How is that going to really change your experience? And it's not just me in my clinical space, but broadly that, that these lifesaving interventions that we're seeing, I mean, we're seeing healthcare in such a fantastic time. They're lifesaving things. You know, there are diseases that were, you know, death sentences 10, 20 years ago, and they're treatable now, but they're not treatable to everyone everywhere. A lot of people don't have access to them. A lot of times they don't get diagnosed because their clinicians don't have access to the latest information. That is where the AI revolution is going to really impact healthcare. It's going to make the patient experience much better by getting you to the right person, much more quickly. It'll enable self-service. So patients can take care of, a lot of the administrative components getting to the right places. Those will happen better. That's all going to be powered by AI, but it's all going to happen much faster because of BeekeeperAI.
Lisa Thee (32:24):
Yeah. And I've heard from one of your partners, with Chiron medical, one of the, the speculations is that the cure for cancer, isn't going to be a pill. It's going to be early detection. And when we have better AI to understand, much earlier in disease progression, what adjustments need to be made, we can really improve patient outcomes.
Michael Blum (32:43):
Absolutely. I think detection gets much move much earlier in the chain because people have access to newer screening technologies or knowing how to use the current screening technologies much better than we do now. So detection happens much earlier. Prevention is much better and that decreases the cancer burden. And then there are new molecules coming along all the time that make dramatic impact on some cancers that haven't been treatable before. So 10 years down the road, healthcare is going to look very different. And 10 years after that, we won't recognize it.
Lisa Thee (33:14):
Well, I look forward to 10 years from now, knowing someone in my life is still here because healthcare system was able to intervene and the trauma of losing them is prevented. And that's why I've been so honored to be alongside you on this journey for the past couple of years, for people that are interested in, connecting with BeekeeperAI and being potential customers or partners or funders, where's the best ways to find you to keep track on what you're doing?
Michael Blum (33:41):
Sure. Well, the, the easiest way to start, is just going to our website, which is BeekeeperAI.com. You can also find us in the Azure marketplace and we have a consultation up there where we can help you with developing any projects or educate you more on, on the BeekeeperAI platform. And you can contact us off of either of those. And we love to have the discussion. we are talking to funders right now, as we transition out of our early stage into a series, a investment and happy to talk to those who want to help from a financial perspective in bringing this to life.
Lisa Thee (34:17):
Well, I can vouch, this is a very fundable team and, we're excited to see where you go from here. So thank you for your time today, Dr. Blum.
Michael Blum (34:25):
Thanks, Lisa. Really appreciate the conversation.
Hey everyone. Thanks for listening to the Navigating Forward podcast. We'd love to hear from you. At a crossroads of uncertainty and opportunity, how do you navigate forward? We'll see you next time.