Boombostic Health Podcast - Episode 3: How Ryght.AI is Shaping the Future of Clinical Trials with Generative AI

"Imagine hundreds more patients are getting on a specific therapy that can save their lives and they're doing it much sooner, and they're doing it more efficiently. Most patients don't even know of a study that's perfect for them. They don't know it even exists." - Simon Arkell OLY , CEO & Co-Founder of Ryght.AI

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Episode Spotlight: Turbocharging Clinical Trials with Generative AI

1. Revolutionizing Healthcare with AI: Simon Arkell, founder of Ryght.AI, leads the use of generative AI in clinical trials, potentially speeding up drug development, improving patient outcomes, and streamlining healthcare.
2. Inspiring Journey: From Olympian pole vaulter to tech entrepreneur, Simon's story showcases innovation and resilience, driving health tech advancements and inspiring entrepreneurs and tech enthusiasts.
3. Future of Medicine: The discussion explores AI's potential to transform healthcare, making treatments more accessible and efficient, crucial for those interested in medicine's future and AI's role.

Here is a clip from Bradley and Simon's discussion - Click here to listen to the full episode!

From Olympian to AI Pioneer

BRADLEY: Welcome to Boombostic Health. We've got another bold innovator joining us today, Simon Arkell. I may refer to him as Ark. He's sort of a famous Australian, which we'll learn more about. But Simon has ventured over the past decade plus into applying artificial intelligence in healthcare.?

There couldn't be a more overheated area of the healthcare industry, but it also is something that when applied very practically and effectively can accelerate healthcare innovation, making patient care better, discovering new therapeutics, and also ideally making it so that there's a better bottom line. We've got this massive outstrip of demand relative to the number of caregivers we have. Something's got to give, and I think there's a lot of promise with AI.

Simon’s background, kind of a fun fact: he was an Olympian pole vaulter from Australia, and actually he can talk to you. He'll make sure you know that he was the left-handed world record holder for some period. So why don't you touch on that? But then, you know, as importantly, he's taken his intense focus and applied it to healthcare to improve care for lots of patients, has had a series of really cool companies, and I'm extremely excited about what he's building at Ryght.AI

Simon, thank you so much for being on the show.

SIMON: Hey, thanks for having me, Brad. Good to see you again.

BRADLEY: And I'm here in our Chicago studio today. Simon, where are you coming in from?

SIMON: So I'm in Laguna Beach, California, which is where the global headquarters of Ryght.AI are.

BRADLEY: Okay, perfect. You were supposed to be here today, but as is the life of an operator. So glad we can still make it happen remotely. Thanks for being here.

So Simon, why don't you start off? Just give us an introduction so our viewers can better understand what your background is and how you've gotten to where you are today.

SIMON: Thank you for asking. You and a number of other people seem to quote that very random fact, but I'll have you know, I was a national record holder, nine national records, went to a couple of Olympics, world champs, et cetera. The only thing anyone ever remembers me for is that I had the left-handed world record for 30 years, and that is not even a record. It seems like the one memorable fact, and hey, I'll go with it. So why not?

BRADLEY: It's like a Guinness book record, you know?

SIMON: It's actually not. Funny you should mention that. I have a friend who thought that was so impressive so he wrote to the Guinness Book of World Records, and said, hey, I've got a friend who has the world record for left-handed pole vaulters. The Guinness Book of World Records wrote back and said, sorry, no, that's too obscure.

BRADLEY: Well, in any event, it's pretty awesome that you're an Olympian and also awesome that you're an entrepreneur. So talk to me about what you're doing at Ryght.AI.

SIMON: Yes, so this is having come to the US in the 80s on an athletic scholarship and staying in the US. I fell in love with tech and doing startups. I had to start companies when I couldn't get a full-time job while I was training, and then that just kind of transferred. This is my seventh company, fifth venture-backed software company and the last three have been in health tech, specifically analytics or AI, as it now is called.

Ryght.AI is a fairly new company we started at the beginning of 2023. We've got a great team of world-class operators who've come together after a recent success. I was one of the founders of a company called DeepLens, and we used the old AI, which was natural language processing and even machine vision AI, to match patients to clinical trials. It's a pretty narrow segment, and we only focused on community oncology in the US. We had a great outcome in that we were acquired by Paradigm, which is a company that was founded during the peak ZERP days, zero interest rate days. They raised a ton of money, gave a chunk to us, and then are on the way with that asset and doing quite well, as I understand.

When I left Paradigm and realized that this chat GPT thing was different to anything I'd previously seen, a few guys and I started to research really what the implications could be on health care and life sciences. It actually turned out that any of the limitations that we previously had in NLP were really overcome in a very big and dramatic way with this new technology, which of course we call generative AI now and so we started Ryght.AI to focus on a much broader mandate than we did at DeepLens. That is to provide software to biopharma, so biotechs and pharma companies, but also to grow a site network of sites that could be using this.

These sites are research sites, they could be small community sites, they could be large academic medical centers, they could be country systems in other countries around the world. We have a vision to effectively bring together all of the stakeholders in clinical research, which is a very big, bad, broken industry, $81 billion industry globally just on the clinical trial side. So many areas for improvement that previously were unaddressable that are now addressable with generative AI. We're on the way. We're venture-backed. We've got a team of 20, and we're really doing quite well.

BRADLEY: Yes, you know, I've been impressed by the announcements of new team members. I think you recently added a new chief medical officer.

SIMON: We did. We added Chadi Nabhan, MD, MBA,FACP , Dr. Chadi Nabhan, he's a very well-known hematologist oncologist who's also based in Chicago. He previously was the CMO, after operating in clinical research at academic medical centers like University of Chicago, he was the CMO at Cardinal Health, and then he ran the Precision Oncology Alliance, which is a group of systems around the world for carousel life sciences. So he really knows his stuff. He's come on and basically just making incredible strides for us as we plug in and create a global site network.

BRADLEY: That's awesome. Well, he'd be uniquely positioned to help you with that. I'd love to have him on the show sometime.

SIMON: Yeah, he has a really well-known podcast, and he's a well-known author as well. So his podcast is called Healthcare Unfiltered, and he's got a couple of books out too. You guys can compete over audience sizes, if nothing else.

The AI Hype and Reality

BRADLEY: Well, I think we need everybody pushing as hard as possible to make healthcare move in the right direction. So glad there are others out there doing the rising tide thing.

AI is the most overused term in all of healthcare, and also as an operator entrepreneur, I've been around tech for a long time, and we've been applying advanced data science that now all seems to fall into this bucket of AI for a long time. There's a lot of benefit to the fact that now there's this mind share about why this is important, but it's also kind of overheated, right??

So what do you folks do that, like, explain what you're doing that actually is generative AI? Because I don't think, it's sort of like everybody started saying cloud 15 years ago, no matter what, even if they had some kind of cobalt that they were putting lipstick on a pig, now everybody says AI, and they don't even know how to spell it half the time. So maybe just explain, like, how are you practically applying AI in a real way that is generating this leap that you couldn't do before?

SIMON: My first venture backed software company was in 1998 and we added a dot com to the end, because that was what you did back then.

BRADLEY: Right, everything was dot and cloud.

SIMON: In AI, I think that you can delineate the market of vendors that is between companies that use AI from someone else as a feature to maybe improve their product, or give them the ability to say we're an AI company, which they're typically not. Those that really are developing technology that is somewhere in the stack within AI. Now where we focus is in a couple of areas.

There are ways and problems that can be addressed with artificial intelligence that are much different as I mentioned earlier. But I think there's a problem in creating just a little GPT or a little copilot that could sit on someone else's infrastructure, because that's not always going to be the model that you may want to use. It's not necessarily the only problem you're ever going to want to solve with AI.?

We believe that there is a need for a platform that's future proof so that our enterprise customers, which are the biotech and the pharma companies, can solve real problems. That's not going to be with some little GPT that you ask a question of in a chat bot. Maybe the interface should be completely different and look like a traditional application or something new that we haven't seen before, but leverage generative AI because it's such a different paradigm.

We built a platform that is future-proofed in that it sits above the ever-changing landscape of different models and services in AI. Every single week, we read about and hear about new models that are coming out. So of course, OpenAI, they just raised $6.6 billion the week before. They came out with O1, their model, and it does some incredible reasoning and it's the latest greatest right now. But the month before that, it was Lama 3.2, which just came out.?

That's open source. Okay, open source with secure endpoints is very different to picking OpenAI through Microsoft Azure as an example. Then you've got Anthropic, but there are literally 1 million models that are in HuggingFace, which is basically the open source repository for these language models right now. So if you're an enterprise, how do you have an AI strategy that doesn't think about the future and your ability to leverage all of the very best technologies as they come on board?

We created a platform that sits as an abstraction above all of the commercial and open source models and allows you with a single interface of APIs, SDK, no-code builders, but also our own applications, to use the very best models underneath without having technical debt so that when there's a brand new model in that's much better for that particular application, you don't have technical debt where you have to unplug from one model and then plug back into the other.

So that's on the model layer. The other thing which we expose through the APIs is the incredible amount of domain-specific data that is out there. So we all know about PubMed and ClinVar, clinicaltrials.gov, and there are dozens and dozens of others. All of the data that is so required in order to build a very accurate application or copilot that is not hallucinating, not giving you the wrong answers, but is tuned for the specifics of the use case in a very unique industry, you need to expose all of those.?

They're basically just drag and drop and usable through the API. And with that, you can create very accurate and powerful applications without spending three years on all of the plumbing. We provide the plumbing so that our paying customers can develop these apps or they can use the apps that we've developed, and we're developing a whole range of these different applications.

Revolutionizing Clinical Trials

BRADLEY: So when I think about the buckets of this AI landscape. You've got the hyper-scale infrastructure players. In order to play in that rare air takes billions and then you have applications that are integrating AI capabilities into them in order to power up some feature or a workflow or something. Then you have sort of this layer of enablement where there are tools that help enterprises take advantage of AI for their specific vertical.

That's how I think about the buckets. It sounds like you are an enabling tool that not to marginalize the term tool can be misinterpreted, but it's a tool that's bringing together not only the models that are sitting on whatever the infrastructure is, but also bringing content in that's training the models that then it's sort of fit for purpose. Specifically your purpose is for these, the clinical research that's happening in pharma, which the end game there is to make it faster and easier to unlock new therapeutics. Is that how to think about it?

SIMON: Yeah, absolutely. So if you're a mid-sized biotech, maybe have a $2 to $3 billion market cap, you may have five or 10 kinds of therapeutics that are in process. Some could be commercially available. The others are in the clinical research, anything from phase one, two, three. If you are running these trials, you're gathering a lot of data as to obviously the response of the patients who are on the trials, maybe the operations and all of this stuff that helps.?

If you can put your arms around this data, it can help you much more efficiently move from one phase to the next because the ultimate goal is to, from day zero, when you first identify this molecule as a particular potential drug, you have to do the competitive analysis, which requires pulling a huge amount of data from the open source, if you will, public source data and your own internal proprietary data, mash that up and it's all unstructured and you need to make sense of it.

So we have one application, which is around clinical trial intelligence and now you can say, okay, if I'm going to take this molecule targeted at triple negative breast cancer, then what are the competing studies and what's the market size for me to go after that particular indication?

Next, I want to write the protocol I need to generate a whole ton of documents for the FDA. We have a medical writing suite of applications that allow documents that typically would take an organization between 15, 20, even 30 hours to develop, like an informed consent document, and squeeze that first draft down to just a 20-minute process.

The next one is identifying sites globally that could be potential and predicted to be high probability of success sites for my clinical trial, who are not just treating the patients that are right now the only indicator of whether I pick a study as a sponsor these days or a CRO. But is that site predicted to be successful? What if there are a lot of patients at a particular site in Belgium or this triple negative breast cancer, but they're running seven competing studies right now?

Turbocharging Clinical Research

BRADLEY: To net it out, though, you all are, you're creating or have created, it sounds like, this accelerant that throughout the entire journey of clinical research, you've made it so you've added this turbo charger where everybody who's involved, you get them out of the ditch digging work and the manipulation of spreadsheets and research nonsense that's just replicating. It's not really the discovery part. It's like the foundation and that's just sort of automagically there. Then these experts are able to really play at the top of their expertise. Is that fair?

SIMON: I think it is fair. But, you know, we didn't want to be a medical writing software company. That just seemed like it wasn't challenging enough. We believe that that will be commoditized over time. Being a platform company is super dangerous because you've got, you know, Amazon, AWS, Azure and others that are coming up with tools, etc. Domain specificity is still a unique selling proposition for us.

You may hear it first, but we may or may not be announcing a site network strategy in the next couple of months. Think of a scenario where you have thousands of sites that are running this technology and you're the CRO or the sponsor. Now, instead of sending out faxes and emails with attachments to a list of 400 sites that you may have purchased from somewhere or worked with before, or maybe you went to medical school with the guy who's running that site. Now you can see in real time, like I think of a real time heat map and a report that tells you exactly which sites are perfect for your study for the following seven reasons And here's the predictive model projection or prediction as to why that site can be successful for your study.

It's no longer about how many, how many patients are you treating? It's about is your site predicted to be successful in this particular study? Because the input variable to that model is of course, how many patients you have, but it's how many competing studies that is the PI who's been successful in the past, still there, et cetera.

BRADLEY: So we may or may not have just heard a potential future announcement of how this is going to revolutionize the way that you're able to identify those individuals who should participate in studies because of the fact that they meet all these characteristics. Is that what I'm hearing?

SIMON: All I'm telling you is that having tech to write documents and do things more efficiently is one driver of this technology, and that's where SaaS vendors like us can generate a real business. But the big unlock is whether you can have a real-time exchange of value without having to send a fax.?

We operate in an industry where fax machines are still used every day. There's no better industry to put AI against than an industry like this that has such complex manual workflows. The big opportunity is that you come out with that blockbuster drug in 11 years instead of 13 or 9 years instead of 11. When this is a multibillion-dollar-a-year drug, the ROI is obvious.

BRADLEY: That's the motivating factor, just economically, because we talk a lot about the business of healthcare here. There are a lot of great ideas and innovations, but if there's no way to get them paid for, they end up dying off. In this case, there's a massive reason to engage here because it could bring a blockbuster drug to market years sooner.

Oncology Focus and Patient Impact

SIMON: We've all been affected by cancer, and this is across all diseases, of course, but we choose to focus on oncology initially. Imagine hundreds more patients are getting on a specific therapy that can save their lives and they're doing it much sooner, and they're doing it more efficiently. Most patients don't even know of a study that's perfect for them. They don't know it even exists.

BRADLEY: You may know this, but my mother was sick when she was 55 and through lots of diagnostics, I first misdiagnosed her and then figured out, oh, she was feeling all these issues because she had stage four colon cancer and was bleeding internally, and went through the conventional treatment path, surgery, chemo, kept the spread at bay for a second and then had to find a trial.

I was blown away, and this was back in 2000, but I was blown away that the oncologist was basically just looking through his Rolodex to try to figure out if he could call somebody who might have some kind of trial. There was literally no automation or visibility at all, and it was certainly very concerning and not very helpful, and I think as importantly as the fact that she lost her own life, which was catastrophic, and there were many steps that could have happened to prevent ever even having colonoscopy, which public service announcement, get a colonoscopy. It's a really easy way to save your life, and I wish she had done that, but she hadn't.

But then looking at the actual impact on accelerating these new therapies being developed, if you don't have people getting in the trials, you don't have a chance to make that happen. So I saw this firsthand where I guarantee somewhere there was a trial that would have been ideal for my mom's situation, but we had no visibility into that, and it sounds like you're saying, could we dynamically connect those dots in a relatively perfected way so that you don't have those people fall through the cracks so you have better chances for survival, but also you are better able to get these drugs approved.

SIMON: Right now, the CRO or a sponsor, if they do it themselves, will send emails out to 500 sites saying, hey, do you have patients for this particular trial, and do you want to consider it? What if you just knew where they were, and there was an exchange, a handshake electronically using the AI? It'd be amazing.

BRADLEY: I mean, it would save months. It'd be incredible, and that's how it should work, right? There's an economic buyer in that case who can fund this. So gosh, I hope it takes off like it sounds like it should.

So tell me, Simon, where does this all go? Like fast forward 10 years, how has healthcare been transformed by AI? And that's a big question, but just a couple of sound bites of, I think this will be different and better because of AI, and that'll be different and better. Then I also would like to take it over to what are the ways that this could go wrong? You have to talk about that, right? But yeah, start with what's the upshot? How does it look in 10 years?

The Next Decade of AI in Healthcare

SIMON: You know, generative AI is only two years old. So it's staggering to see how far we've progressed just in two years since GPT 3.5 came out. That's when open AI became famous. Of course, the tech was around for years before that that led up to that, but the accuracy wasn't there. If you consider how much has been achieved up to the point of, you know, GPT-01 recently with reasoning models where it literally can go back and forward and reason with problems and the accuracy of the output, the complexity of the workflows are just so much better. You imagine what's going to happen 10 years from now. I mean, this stuff is only accelerating. I mean, even Moore's law on steroids has seen the cost and the speed of inference come down. Costs come down by 85% every 12 months, but the performance is just through the roof.

I think we're going to just see this incredible acceleration. I think we're going to see mainstream adoption because, you know, in our industry, the drug companies, CROs, they're asked by the CEO, you know, come up with an AI strategy and what they do. I heard this recently. It's quite funny. They get an open AI account and that's their AI strategy. But of course, you know, vertical AI is a big deal because of the nuance of this industry. If you can figure that out, you can find the unlock. If you can find that unlock, you're going to change outcomes in a huge way.

Recently, a company that did this sort of thing, it's a very similar approach to Riot actually, in the legal space, had been operating for two months. They were getting really fast adoption by lawyers who were needing it for all of their manual briefs and online data sources, everything I've talked about. They got acquired for $550 million two months after starting.

Now, if you can find product market fit, which I think is possible much quicker than traditional industries, and you get mainstream adoption, that critical mass utilizing this technology is going to create these huge kinds of sea changes in what's possible. 10 years from now, you're going to see real-time matching, you're going to see patients getting on therapies that were only envisaged five years ago, not 15 years ago and you're going to see survival rates go through the roof. I think that's all because AI, if it's used and it's used properly, is going to make that possible.

I think you get there by being super focused in the vertical. Companies like us, and there are a number of companies that are doing AI all over the place, but clinical use as well as research use, and we're focused on clinical research right now. But you're going to see that real-time exchange of value without a manual human workflow in the way. And of course, you'll always have what they call RLHF, which is reinforcement learning from humans with human feedback. You always want to make sure the human is managing the outputs and making sure everything's accurate but I think there's going to be massive acceleration and the leverage of smart people to do things at and above that high grade.

A Vision for Bionic Caregivers

BRADLEY: Well, if you think about it in an old school sense, being able to access all the literature that's in all the books that are most up-to-date in the medical school library, that it wasn't feasible to do, but ideally you'd have physicians who were doing that. In this new world, it actually makes it so that whatever the newest, latest way is that it’s clinically vetted and in the literature to maximize the impact of diagnostics and treatment, that should be available to everyone.

So when I envision this future in 10 years, you've got these bionic kinds of caregivers that are not miserable in their jobs because they're getting to do the stuff that they trained to do. They're driving proactive healthcare. They're getting compensated for doing things the right way. I think there's a whole convergence of factors that combines the incentives and the possibilities with technology and the people that will create this really bright future. And it's awesome to see how you guys are participating in that.

SIMON: Yeah, thanks. On that note, we just signed a very big and strategic deal with the University of Adelaide in South Australia, my hometown is Adelaide, and we've got a number of big government initiatives we're working on, one of which we've already ingested an entire book around pain management, which now professionals in that space are using to refer to and ask questions of instead of this thousand page book sitting gathering dust on their shelf that's now on their phones. So imagine that that data is at your fingertips to every junior doctor who's now upskilled to your point, rising tide. You've upskilled everyone to be the very best and you've taken a lot of the manual kind of boring stuff out of their day.

BRADLEY: I think the human feedback in that loop is key and I think that's also something a lot of people don't understand when they consider the possible perils of a 2001 Space Odyssey, where the thing goes haywire and everybody gets killed off. I think the reality is, that's like a medical device, like the Da Vinci robot just doing surgery without a doctor involved, like it doesn't happen, but there's a lot of automation there. I think this power up this bionic environment for healthcare, it'll make everything better for everyone.

Thanks so much, Simon, I absolutely covet the work that you're doing. I'm extremely appreciative to be a friend and also to be playing a role with you in what you're building for the future. Excited about a lot of collaboration and the value we can unlock together Also, we'll be watching closely to see how you're transforming this whole clinical trial space. If anybody can make it happen, you can. Thanks for being on Boombostic Health.

SIMON: Thanks a lot, Brad.

The Verdict: Legal and Regulatory Insights

AI and Malpractice: A Double-Edged Sword

BRADLEY: I want to welcome Emily Johnson , our resident healthcare legal consultant, who is here to talk about AI and healthcare with her segment, The Verdict with Emily. We had this great conversation about AI and healthcare with Simon, the founder and CEO of Ryght.AI, where they're applying this technology to advance clinical trials.

I know, Emily, you've had some experience with artificial intelligence and things that are critical to make sure it's applied in a compliant way in healthcare. We'd love to hear from you, just share some of the experience that you've had with the real world application of this technology and what some of the things are that you should be looking out for as you get into the business of applying AI to actual clinical delivery.

EMILY: Sure. So, I mean, I think it's come up in a few areas of my practice. There is the clinical utility of AI and how that can help enhance clinical decision-making. I think the sort of corollary to that is what impact does the use of AI have on a physician's malpractice coverage.

There was a study conducted, I think it was by Johns Hopkins, with respect to use of AI. The study found that actually physicians are most likely to use AI when they already know the answer. They're not using it for sort of these intended purposes of AI to enhance clinical decision-making. They're just using it as a crutch for things that they know on a daily basis.

Then the question becomes, if they use it for something that is particularly difficult or if it is related to some sort of innovative treatment, when and how does malpractice factor into that? If you use AI and it is a substandard or non-standard level of care, does the physician open themselves up to additional malpractice as a result of that? And I guess the flip side of that is if a physician refuses to use AI, are they not leveraging all existing areas or tools that are available to them to enhance clinical decision-making?

The Gray Area of AI in Diagnostics

BRADLEY: So there's kind of a gray area right now. Theoretically, the technology provided in a properly trained AI model could identify, for example, on a slide that was trying to determine if somebody had cancer.

That potentially AI could scan that and identify some type of nuance that is different, you know, some type of nuance that they wouldn't have seen otherwise. And then the question is, is that accurate or not? That kind of thing, right? Yes.

EMILY: But with respect to breast cancer specifically, what I have been told is that for pathology slides, the usage of AI to enhance the read, to see deeper into the tissue and sort of find those issues that might not be readily apparent to the naked eye are leading to, and I'm not the physician, this is just what I've heard, but are leading to treatment of those conditions, whereas ordinarily the physician might take a wait and see approach. And so there are sort of secondary cancers that can result when you treat something prematurely when, you know, you could just monitor it and not intervene with medication.

BRADLEY: Interesting so there's probably some trepidation on the part of the actual physicians as to whether they should use this technology or not.

EMILY: Correct. We're seeing a lot of that. In fact, I have a lot of pathology clients who enter into research agreements and they are concerned whether the usage of AI on a final pathology report impacts their malpractice because it's sort of like an overlay onto the pathologist's original interpretation.

BRADLEY: So that would actually relate to pharma, right? Like in clinical trials. So somewhat dovetails with what Simon was talking about earlier. If you're using this technology to generate information that could inform, for example, what patient could be eligible for a clinical trial, you're saying that's like a layer of analysis that is in addition to what you would do in your ordinary course of care delivery.

EMILY: Correct.

BRADLEY: For the physician, they have to make the choice like, is it worth it for me to do that? Because it might open up another angle where I could be liable for something from a malpractice perspective.

EMILY: Right. Exactly. And so the question is, do you incorporate that into the pathology report or do you keep that something separate? Or is that something that maybe the ordering clinician adds after receiving the pathology report? Maybe there's like some sort of interlay overlay there.

The Digital Shift in Pathology

BRADLEY: So we saw that laboratory medicine has become more digital over time. Everybody has a lab information system. They're storing data digitally. They're sending and receiving orders and results electronically. But the side of the laboratory that has been pathology is still slides. They still have a microscope and they're looking at the slides that still get filed somewhere and it's not digital and so digital is required in order to really leverage AI. Is that true?

EMILY: Yeah. I would say it's true. Absolutely. I mean, I think you saw folks try to do it a few years ago just based on the information in the pathology report. But to make that jump to a different level of clinical utility that AI can bring, you need the digital slide.

BRADLEY: Well, and I think in radiology, there's a lot of work already being done with AI because it is largely already digital and there's a picture archiving and communication system they use that's already digital. So well, would you say what you're seeing, there is a lot of movement toward caregivers trying to figure out how to apply AI?

EMILY: It's interesting with pathologists, they move pretty slowly typically. I think there's an interest in doing it. They want to do it, but they're afraid of change. I think it's sort of threading that needle of trying to figure out how it fits into their practice without exposing them to additional risk.

BRADLEY: Well, maybe there are some models to connect between pathology and pharma as well that could help accelerate this and use it in the best possible way. So awesome.

Well, thanks so much as always for joining Boombostic Health as our consultant in the legal area. I really appreciate it.

Thanks again for joining Boombostic Health. It was great having you. I enjoyed having my friend and business partner, Simon Arkell on to talk about the innovations that are being unlocked by Ryght.AI for advancing clinical trials and also covering with Emily, our resident legal consultant, how AI should be considered from a regulatory perspective specifically in the area of pathology, but also how that relates to pharma and other potential uses of this very promising technology.

It's a very formative time, so we all need to be mindful of how to push the envelope as fast as possible, but also consider the ramifications from a regulatory and a legal perspective. Thanks so much. We'll see you next week.

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