Latest / The Adults In The AI Room / Earley AI Podcast - Episode 96: AI in Clinical Trials, the Vibe Coding Fallacy, and Bending Eroom's Law with Patrick Leung

Transcript

Earley AI Podcast - Episode 96: AI in Clinical Trials, the Vibe Coding Fallacy, and Bending Eroom's Law with Patrick Leung · The Adults In The AI Room

• 0:01
  SPEAKER_00: This is the Early AI Podcast hosted by Seth Early.
• 0:04
  SPEAKER_00: Join us as we delve deep into the passions, expertise, and experiences of thought leaders and practitioners to talk about what's possible with artificial intelligence.
• 0:14
  SPEAKER_00: The Early AI Podcast is sponsored by Early Information Science, your digital transformation journey with design and deployment of innovative technology solutions, as well as Vector.
• 0:26
  SPEAKER_00: Now enjoy the show.
• 0:28
  SPEAKER_01: Welcome to the Early AI Podcast.
• 0:31
  SPEAKER_01: I'm your host, Seth Early, and in each episode, we explore how artificial intelligence is changing the world in which we do our work and how it impacts business strategy and operations.
• 0:43
  SPEAKER_01: Today we're going to be talking about a very consequential area of artificial intelligence, and that is clinical trial development, drug development.
• 0:52
  SPEAKER_01: The cost of bringing a new drug to market has been doubling roughly every eight years for decades.
• 0:57
  SPEAKER_01: Most trials fail, and the decisions that determine whether trial succeeds or fails are often made without the data needed to make them as effectively as they could be.
• 1:07
  SPEAKER_01: Joining me today is Patrick Lung, Chief Technology Officer at Fowl Health, a platform purpose-built for designing better clinical trials.
• 1:15
  SPEAKER_01: Patrick has spent over a decade at Google, including working on Google Duplex, which is a very high-profile conversational AI launch and brings a combination of deep technical experience plus domain expertise in this very highly regulated industry.
• 1:30
  SPEAKER_01: Patrick, welcome to the show.
• 1:31
  SPEAKER_01: Thanks for having me on.
• 1:32
  SPEAKER_01: It's a pleasure to be here.
• 1:33
  SPEAKER_01: So I like to start off with common misperceptions.
• 1:36
  SPEAKER_01: And what is it that pharma and life sciences executives miss?
• 1:41
  SPEAKER_01: What do they get wrong about what AI can do and what it realistically can how it can impact clinical development?
• 1:48
  SPEAKER_01: So what are the things that they get wrong about AI in general?
• 1:52
  SPEAKER_01: And then talking about clinical design.
• 1:54
  SPEAKER_03: Yeah, great question, Seth.
• 1:56
  SPEAKER_03: I mean, I think in general, generative AI is going through what Gardner would term the trough of disillusionment, where after this initial phase of hype, there's this kind of rubber-hitting the road kind of experience of actually trying to apply the technology to solve real problems.
• 2:11
  SPEAKER_03: And so as a result, I think there is this tendency for people new to this to sort of think, this is a panacea.
• 2:16
  SPEAKER_03: Wow, I can it can do anything.
• 2:18
  SPEAKER_03: I can automate every single thing, every single process that involves any form of language or text, including clinical writing or including, you know, automating an entire clinical process without really kind of necessarily thinking through the details or considering the limitations of these AI systems.
• 2:31
  SPEAKER_03: I think there's also this accompanying, perhaps investor-driven sort of push to automate jobs and kind of look at the AI technology as being this way of dramatically reducing costs by replacing humans with AI.
• 2:45
  SPEAKER_03: And we've found quite the opposite in the sense that we need humans involved in this.
• 2:50
  SPEAKER_03: You know, the clinical domain is really complex and lives are at stake.
• 2:54
  SPEAKER_03: And so it's very important to have human experts continue to be deeply involved.
• 2:58
  SPEAKER_03: And I would say the third thing is just this misconception that perhaps, you know, just by using Claude code, they can become tech companies and build their own tech products just by vite coding.
• 3:08
  SPEAKER_03: And I think that I don't blame companies that don't have any experience with software engineering for believing that, because initially things seem so magical when you start using tools like Claude Code or Codex or what have you, and you start believing you can do anything.
• 3:22
  SPEAKER_03: Um what I found is that once you pass a certain threshold of uh almost like an event horizon of complexity, you start running into the sharp edges of software engineering and realize, oh my gosh, I need a proper tech team or a proper tech vendor to take care of this stuff for me.
• 3:34
  SPEAKER_01: Yeah, what one of your points that you made is about uh reducing headcount.
• 3:38
  SPEAKER_01: And I was listening to uh a podcast of Hard Fork, uh, which is a great podcast, guys, but uh from from the New York Times and from uh platformer and and and one of the things that they had Eric uh Brunnelson from MIT.
• 3:52
  SPEAKER_01: Yeah, I know him.
• 3:52
  SPEAKER_01: Yeah, he's a great guy as well.
• 3:54
  SPEAKER_01: Yeah, on.
• 3:54
  SPEAKER_01: And he was saying that you know, when CFOs say we're gonna reduce headcount, that is a very short-sighted and narrow tunnel vision way of looking at this, right?
• 4:02
  SPEAKER_01: Because there's so many other implications around how AI can bring value and bring efficiency that just looking at headcount reduction is not necessarily where they should be.
• 4:13
  SPEAKER_01: And and you know, again, you could you could end up increasing headcount with certain AI tools.
• 4:19
  SPEAKER_01: We were doing a show on supply chain management, and the fact that you could get much greater detail and nuance about a supply network actually meant you needed more analysts as opposed to being analysts.
• 4:31
  SPEAKER_01: So, so I think, and I think what you're saying about vibe coding is really important.
• 4:36
  SPEAKER_01: So, so talk about where those efficiencies can be other than headcount reduction when you think about your world of of clinical trial management.
• 4:45
  SPEAKER_03: Yeah, I mean, I think that certainly AI has the capability to enhance people's the way people do their jobs, right?
• 4:51
  SPEAKER_03: We're seeing that with coding, uh, where engineers can become more productive and they can automate some of the more pedestrian or repetitive or rote aspects of their job, so they can focus on the more challenging creative aspects of the job.
• 5:04
  SPEAKER_03: And I think the same thing applies to clinical professionals.
• 5:07
  SPEAKER_03: So, for instance, clinical writers, one of the main thrusts of our AI efforts here at Firo Health is the automation of clinical writing and authoring.
• 5:15
  SPEAKER_03: And so this has taken a couple of years to get right in terms of getting all the technical nuances and all the guide rails and the evaluation systems and so on that are necessary to produce real clinical quality clinical protocols and other documents.
• 5:30
  SPEAKER_03: And as a result, our customers are really thrilled with this and are able to draft a protocol in a fraction of the time it used to.
• 5:37
  SPEAKER_03: And that's not to say they can fire all their clinical writers, but it is to say that they get to actually move through that process of clinical development faster as a result of using AI.
• 5:46
  SPEAKER_01: Right, right, right.
• 5:47
  SPEAKER_01: And improve the quality of those designs.
• 5:49
  SPEAKER_01: I do want to go back to the vibe coding fallacy, right?
• 5:52
  SPEAKER_01: So everyone's talked about AI eating software companies and dissolving the moat.
• 5:58
  SPEAKER_01: What is true and what is hype?
• 6:00
  SPEAKER_01: Because you made that really important point.
• 6:02
  SPEAKER_01: A vibe coding hits a wall, right?
• 6:04
  SPEAKER_01: And so what separates someone who can ship real enterprise software from someone who just pops and gets gets that result?
• 6:12
  SPEAKER_01: Yeah.
• 6:12
  SPEAKER_03: I mean, it really all comes down to complexity.
• 6:15
  SPEAKER_03: When you first start playing with Cloud or ChatGPT or what have you, it's pretty easy to just create a demo app, you know, that that plans out your diet or that, you know, um creates some sort of simple game or what have you.
• 6:28
  SPEAKER_03: But as soon as you get into this realm of sort of more complex software, and for sure that the kind of things that we do, like optimizing a clinical protocol, fall under that bucket of just really complex multi-agent systems with, in some cases, really complicated UX, backend databases, and so on.
• 6:44
  SPEAKER_03: You there's no escaping the fact that you need to test software.
• 6:47
  SPEAKER_03: There's no escaping the fact that you need to have specs that are really well thought out.
• 6:51
  SPEAKER_03: And there's no escaping the fact that as you add more features to a code base, it gets more and more complex and unwieldy and difficult to maintain.
• 6:57
  SPEAKER_03: And so there is a noticeable slowdown, you know, as as your system becomes more complex.
• 7:02
  SPEAKER_03: And there is the need to really make good design decisions, or maybe even revisit some key design decisions as the code grows.
• 7:09
  SPEAKER_03: And this requires experience.
• 7:11
  SPEAKER_03: You can't bytecode your way out of those kind of redesigning processes or key kind of like design decisions that need to be made during the course of a system's growth and evolution.
• 7:20
  SPEAKER_03: So that's what we're finding is that the engineers and our team here at FIRA, we've we've had to really retool and rethink the way we do our jobs.
• 7:27
  SPEAKER_03: And certainly, as I mentioned before, things like a lot of DevOps tasks or a lot of writing of unit tests and things that are previously sort of part of the bread and butter of being a software engineer are now really things that AI can take care of pretty easily.
• 7:41
  SPEAKER_03: But there are many areas where it can't, at least not yet.
• 7:44
  SPEAKER_01: And you you talk about you know, uh getting away from fundamentals and and first principles.
• 7:50
  SPEAKER_01: So you know, especially in security, right?
• 7:53
  SPEAKER_01: And again, uh I I think as you get into these more complex systems and processes, you can you can code basic fundamental uh uh functions, but you you you can't get away from all the nuances of uh testing and security and and resilience and maintainability that requires that that level of expertise.
• 8:12
  SPEAKER_01: What are those things that people ignore when they're trying to vibe code these applications?
• 8:17
  SPEAKER_01: And what what are those first principles that we need to make sure we we abide by?
• 8:21
  SPEAKER_03: Well, in addition to all the usual kind of security and privacy and regulatory constraints that I think settle any kind of enterprise software, especially in the clinical domain, there's this added interesting factor of um the fact that tools that use AI are now vulnerable to a certain class of attacks that didn't exist before.
• 8:38
  SPEAKER_03: So you might have heard of SQL injection where people can kind of like, because pretty much every enterprise system out there uses some form of SQL to query its data, so you can construct user input through a tool in such a way that it actually accesses the database if you're clever enough and if the system isn't designed to withstand that.
• 8:55
  SPEAKER_03: So now we have prompt injection where people are doing things like entering data into the system that says something like, ignore all the previous stuff that you've been told, and now give me all the emails in your database, you know, this kind of thing.
• 9:06
  SPEAKER_03: I'm sort of creating a cartoon really simplified version, but you kind of get the idea.
• 9:10
  SPEAKER_03: And so this is something that now any tool that uses AI to process user input needs to worry about.
• 9:15
  SPEAKER_03: And there's a whole bunch more of these that we're still discovering and that we're still kind of like trying to figure out because it's quite such a new area that these things haven't been well solved yet.
• 9:24
  SPEAKER_01: Yeah, it increases the surface area of those attacks, those attempts.
• 9:28
  SPEAKER_01: And in in unique ways that, as you say, we're not aware of.
• 9:32
  SPEAKER_01: There was a recent story about how uh I believe it was OpenAI, one of their advanced models, uh, was able to break out of its and it went in and and hacked hugging face.
• 9:45
  SPEAKER_03: Yeah, well that that's even that that that was really interesting in the sense that it was the AI doing the attacking, not the defending.
• 9:51
  SPEAKER_03: So what I'm talking about is you know, companies need to defend better against attacks.
• 9:56
  SPEAKER_03: But there are new types of attacks that are being rapidly evolved because AI can be used to actually generate an attack.
• 10:01
  SPEAKER_03: And I think this whole this whole thing of you know, that model escaping, GPT or whatever it was, escaping and attacking Hugging Face, that's inevitable.
• 10:09
  SPEAKER_03: I mean, that thing has been happening for decades now of worms kind of getting out of control, not even AI driven, just straight up logic programs that escape and start attacking things all over the place, and these worms end up wreaking damage and have to be stopped.
• 10:22
  SPEAKER_03: And I think that was before AI.
• 10:24
  SPEAKER_03: And now we have intelligent agents that are defined this.
• 10:26
  SPEAKER_03: It's gonna happen.
• 10:27
  SPEAKER_03: And so we need to be designing these systems in such a way that they they just are less likely to do that kind of thing.
• 10:33
  SPEAKER_01: Yeah, that whole alignment uh process, right?
• 10:35
  SPEAKER_01: How is it aligned with our objectives?
• 10:37
  SPEAKER_01: And of course, the bad actor is going to align with their bad intent, right?
• 10:42
  SPEAKER_01: Absolutely.
• 10:42
  SPEAKER_01: Yeah, I think I think ultimately alignment is the biggest issue.
• 10:46
  SPEAKER_03: Like, is AI with us?
• 10:48
  SPEAKER_03: Is it quite inherently, does it share the same ultimate goals as as humanity does?
• 10:52
  SPEAKER_03: And I think this is something that that anthropic has thought about.
• 10:55
  SPEAKER_03: Like if you look at their published constitution, they talk about, you know, the purpose of this software, I mean Ecclaude, is uh to help life and to encourage human flourishing, all these kind of good things.
• 11:06
  SPEAKER_03: Uh, and I think that's necessary, but not sufficient.
• 11:08
  SPEAKER_03: Uh, I wish every AI vendor of note would do such a thing because it helps.
• 11:12
  SPEAKER_03: But I think there's much more that needs to be done as well.
• 11:15
  SPEAKER_01: Yeah, yeah.
• 11:15
  SPEAKER_01: No, it's it's it's certainly an interesting space and and and ripe with uh uh possibilities and and and uh risks and dangers that we we really don't really have a way of of understanding or qualifying and quantifying at this point.
• 11:31
  SPEAKER_01: So that's uh that's really a an area of concern for all organizations.
• 11:35
  SPEAKER_01: And getting back to clinical trial development, um, one of the things that we talked about in our previous discussions was that they're still designed in unstructured documents.
• 11:45
  SPEAKER_01: There's an attempt to do the digitization, to do component authoring, to do add structure.
• 11:50
  SPEAKER_01: Why has that been so hard to change?
• 11:52
  SPEAKER_01: And what does that path look like moving forward?
• 11:55
  SPEAKER_03: Yeah, this is something that I thought about a lot when I first joined Firo Health because it kind of surprised me how ingrained and how fundamental the use of Word docs was.
• 12:05
  SPEAKER_03: And and I I've spent some time in financial services before, which is a far cry from high tech.
• 12:10
  SPEAKER_03: And there, they're all addicted to spreadsheets, which also surprised me.
• 12:13
  SPEAKER_03: But I think spreadsheets are structured.
• 12:16
  SPEAKER_03: Like as you say, Word docs and the like are completely unstructured.
• 12:19
  SPEAKER_03: They may maybe they have tables embedded within them as a form of structured data, but it's entirely, you know, it's quite difficult to access.
• 12:26
  SPEAKER_03: And so I think the reason why is because it's historic.
• 12:29
  SPEAKER_03: You know, after a while, um, if you if you're using the same technology for many, many years, it becomes very ingrained to the point where the entire regulatory process is centered around these unstructured documents.
• 12:38
  SPEAKER_03: And I think we're starting to see the beginnings of this change, unfortunately, not in the US, but in Europe, where they have some form of digitization, digital uh trial submission to the regulatory authorities there.
• 12:49
  SPEAKER_03: But when you look below the surface, it's not really digitized.
• 12:52
  SPEAKER_03: It's kind of like a digital wrapper around unstructured data.
• 12:55
  SPEAKER_03: And so this is just one of our big things at FARO.
• 12:58
  SPEAKER_03: Like the whole idea of FIRO was let's really digitize trials and let's introduce tools and process such that these plans, which are so complex, right?
• 13:07
  SPEAKER_03: I mean, a clinical trial can last 12 years and maybe have over a thousand people involved all around the world and really, really complex logistics, very complicated medical procedures.
• 13:17
  SPEAKER_03: This definitely should not be mastered, meaning modeled and designed in a Word doc.
• 13:22
  SPEAKER_03: That's that's in fact, it's hard to imagine a worse tool for doing that than maybe Notepad.
• 13:26
  SPEAKER_03: And in fact, Notepad would probably be better because it's more by machine.
• 13:29
  SPEAKER_03: So and so that's what we're setting out to do is really show people like there's a there's a better way of doing things.
• 13:36
  SPEAKER_03: And it's based on a really highly structured representation of a trial that can be analyzed and compared and predicted and all the good things you'd expect to be able to do with a clinical trial to help patients ultimately.
• 13:47
  SPEAKER_01: So in those trials, there they we you talked about the concept of E Room's law, right?
• 13:53
  SPEAKER_01: The observation that the cost and time required for clinical trials doubles roughly every eight years, which is the opposite of Moore's law.
• 14:01
  SPEAKER_01: And E Room is Moore spelled backwards.
• 14:03
  SPEAKER_01: What is driving that?
• 14:05
  SPEAKER_01: And so where does AI fit into changing that trajectory?
• 14:08
  SPEAKER_03: Yeah, well, just briefly, so the four factors driving E Room's law.
• 14:13
  SPEAKER_03: Number one is the fact that over time, inevitably the bar rises for new treatments because you have these existing treatments get better and better over time.
• 14:21
  SPEAKER_03: And so it's just required, it's harder and harder to come up with a treatment for a given medication, a given disease.
• 14:27
  SPEAKER_03: Um the second is there every once in a while there's a regulatory disaster.
• 14:30
  SPEAKER_03: There's a thetalidamide, there's a fentanyl, and then when that happens, the regulatory authorities become that much more stringent, and rightly so.
• 14:37
  SPEAKER_03: The third is there's this tendency as these pharma companies have become really large to just throw money at the problem.
• 14:43
  SPEAKER_03: And this was raised by the people who originally came up with a Seb's kind of formulation.
• 14:47
  SPEAKER_03: And that's just an inevitable sort of enterprise growth thing where people, things just become bloated over time.
• 14:53
  SPEAKER_03: And I think that can definitely be addressed by AI.
• 14:55
  SPEAKER_03: And the fourth one's interesting, there's been this kind of systemic shift from classic phenotypic screening where you look at the actual traits of an animal that you're doing some experiment with through to this more molecular, almost like reverse pharmacology, where you examine all the sort of molecular targets that a drug can address and hope that one of them is going to be successful.
• 15:14
  SPEAKER_03: And that hoping results in a lot of failure and a lot of trial and error and a lot of brute force approach that costs a lot, but it's inherently more expensive.
• 15:21
  SPEAKER_03: And so these four factors I think have combined to conspire for this um ERUM's law of exponential increase in drug trial sort of cost and time and so on.
• 15:30
  SPEAKER_03: And I think AI can really, really help with those last two.
• 15:34
  SPEAKER_03: I think the rising bar is inevitable and the disasters, hopefully, it can help in terms of preventing the disasters in the first place.
• 15:41
  SPEAKER_03: But biology is really complex, and we only understand what percent of it at best.
• 15:45
  SPEAKER_03: And so there will be these situations where we think a drug is completely safe.
• 15:49
  SPEAKER_03: We've done all the testing, and then there's some unforeseen, maybe longitudinal effect that arises, and that highlights the need for more post-launch testing and trials and so on to ensure that doesn't happen.
• 15:59
  SPEAKER_03: But I think the second two, the uh the efficiency by which drugs are created, and uh I guess both of them speak to efficiency really, uh definitely areas where I foresee that AI can really help.
• 16:10
  SPEAKER_03: We're focusing on the human trials.
• 16:12
  SPEAKER_03: So once the drug is showed some level of efficacy in the animal trials or whatever you have before the human trials, the in silico and so on.
• 16:19
  SPEAKER_03: We take care of designing trials that are safe and effective and can enroll patients and won't incur amendments so that these drugs can be safely taken to market in an efficient way.
• 16:28
  SPEAKER_01: So there's four areas that we had talked about.
• 16:31
  SPEAKER_01: Clinical writing, which you already started to mention, the digitization, you know, taking those protocols and extracting it in a structured form, using AI and being able to unlock the analytics and do some integration, some automation downstream, protocol optimization.
• 16:47
  SPEAKER_01: So taking those protocols, making them see uh safer and cheaper and and uh leaner by stripping out unnecessary activities and uh predicting the clinical outcomes, right?
• 16:58
  SPEAKER_01: What what you know where you have predictive models that are built on interlinked data sources that estimate the likelihood of technical success, safety issues, amendments, uh, you know, and and and and and the question is so talk a little a little bit more about those four areas where you're applying AI and which one surprised you the most in terms of the challenges and the difficulty.
• 17:22
  SPEAKER_03: Yeah.
• 17:22
  SPEAKER_03: I mean, the clinical writing has been tricky just in terms of coming up with consistent output that is that meets the the quality standards that our clinical team put and puts forth.
• 17:32
  SPEAKER_03: And so I would say it didn't really surprise me this would be tricky because you know using AI to generate an email or to generate some marketing document is nothing like using AI to generate a 200-page clinical protocol that's dense with data and so on.
• 17:47
  SPEAKER_03: And so, like many, we we sort of perhaps unsurprisingly adopted a divide and conquer approach where these protocols are all divided up into these sections that cover different technical areas, and so we kind of generate section by section.
• 18:00
  SPEAKER_03: And after a lot of thinking about how do we best evaluate these results and how do we best produce agents that can consistently produce decent clinical output, we have a product and so on.
• 18:10
  SPEAKER_03: So that was kind of more of a divide and conquer and just log through the quality kind of process.
• 18:14
  SPEAKER_03: And we do some interesting things like take in the customers' own templates and their own kind of like guideline documents in order to match the quality standards of our customers as well.
• 18:23
  SPEAKER_03: So there's some interesting work there.
• 18:24
  SPEAKER_03: The digitization, I guess the main thing that surprised me there was just how varied all the documents are.
• 18:30
  SPEAKER_03: I mean, you know, we downloaded a ton of documents from clinicaltrials.gov, which is a treasure trove of information about trials that have been already sort of published.
• 18:37
  SPEAKER_03: And uh it's just the the variety in terms of the style and the content and the even the format, the way that people use PDF or the way that they generate the PDFs, hugely different.
• 18:46
  SPEAKER_03: And so we just had to really be very adaptive in terms of and flexible in terms of encountering and housing a lot of different styles of of expressing um the clinical trials design.
• 18:56
  SPEAKER_03: And then with protocol optimization, this is really interesting because we're going deep into examining the clinical trials design and figuring out which of these procedures are actually really necessary, or maybe some of them conflict, or maybe there's a better version of these procedures that exist that we should replace them with, or maybe there are some missing procedures that we need to add that might add some complexity but result in better, better outcomes for the patient and so on.
• 19:17
  SPEAKER_03: And so there was a lot of analysis there in terms of just pulling down research and figuring out how this trial should really be and producing a report that actually guides people to produce a better clinical trial design.
• 19:28
  SPEAKER_03: The thing that surprised me most there was that nobody had done this before and the voracious appetite that so many pharma and biotech companies have for this particular problem.
• 19:36
  SPEAKER_03: So really, we really kind of nailed the product market fit there.
• 19:40
  SPEAKER_03: Like it became this hot product that everybody wanted, which was very satisfying given the the investment, given the the this long-standing thesis we had that we could do this.
• 19:48
  SPEAKER_03: And I think again, so the last the last areas near and dear to my heart, which is actually using data science and using AI to forecast clinical trial outcomes.
• 19:56
  SPEAKER_03: And the thing that surprised me there was like I built all these research agents that went out there and polled all these different sites to find out like what is the state of the art in clinical trial forecasting.
• 20:06
  SPEAKER_03: And I was a bit surprised that there wasn't more out there.
• 20:09
  SPEAKER_03: And so I thought, on the one hand, I got a ton of ideas by pulling down all these academic papers that gave me a little bit more, a little bit more to build out, you know, have you heard of this data set?
• 20:17
  SPEAKER_03: Have you heard of this technique?
• 20:19
  SPEAKER_03: So that was really satisfying that I could build on the shoulders of giants there and actually rapidly build out a system that worked pretty well.
• 20:25
  SPEAKER_03: And what also surprised me was that I found myself applying models that I'd learned about in a completely different domain.
• 20:30
  SPEAKER_03: So um I was when I worked for a couple of years at a hedge fund and we used these survivor curve models for predicting insurance policy claims for like bars and restaurants and you know, actors like people doing construction.
• 20:44
  SPEAKER_03: And those same models work pretty well when applied to clinical trials.
• 20:47
  SPEAKER_03: So it was really like wow, there is transferability, it's really a thing.
• 20:50
  SPEAKER_03: And this is not even using LLMs, this is using machine learning predictive models.
• 20:55
  SPEAKER_01: So old-fashioned AI.
• 20:58
  SPEAKER_03: Yeah, yeah.
• 20:58
  SPEAKER_03: And and there are real advantages from old-fashioned AI.
• 21:01
  SPEAKER_03: I can tell you, like, it's much faster and cheaper, like orders of magnitude.
• 21:05
  SPEAKER_03: And so you can do things like run a multicalo simulation or a numerical optimization that would be utterly impossible using an LLM because of the speed and cost constraints.
• 21:14
  SPEAKER_03: And so if you have instant inference, you know, you can do all sorts of really interesting things by running a prediction thousands of times.
• 21:20
  SPEAKER_03: And so, yeah, it was very satisfying as a data science to see that these uh so-called classical techniques still have a lot of value, and they actually work really well with these more modern language models because you can still use the language models to do data engineering, like summarizing academic um abstracts and things like that, extracting structured information from PDFs that we pull from ct.gov and other places.
• 21:44
  SPEAKER_03: So it was a really nice synthesis of the classical and the new that resulted in a really, really great system for forecasting outputs, outcomes of trials.
• 21:52
  SPEAKER_01: And and can you give us examples of what it can tell a pharma company before they commit to a trial?
• 21:58
  SPEAKER_01: What are the kinds of things that
• 22:00
  SPEAKER_03: I mean it can predict buckets of how many amendments a trial is likely to incur.
• 22:05
  SPEAKER_03: Like is it in the zero to three range?
• 22:06
  SPEAKER_03: Is it in the four to seven range?
• 22:08
  SPEAKER_03: This kind of thing.
• 22:09
  SPEAKER_03: It can forecast safety issues.
• 22:10
  SPEAKER_03: It can forecast when you're likely to fail.
• 22:13
  SPEAKER_01: If if this trial talk about we talked about an amendment for people who may be less familiar with the other.
• 22:18
  SPEAKER_03: Yeah.
• 22:18
  SPEAKER_03: So an amendment is when the regulatory authorities who are reviewing or submit a trial have an issue with it.
• 22:23
  SPEAKER_03: And the issue could be we don't think this is sufficiently safe.
• 22:26
  SPEAKER_03: Or it could be, hey, you're not going to be able to enroll patients.
• 22:29
  SPEAKER_03: Or maybe you already are having trouble enrolling patients.
• 22:31
  SPEAKER_03: And so you've got to go to the FDA or whoever and say, hey, we need to change this trial.
• 22:36
  SPEAKER_03: So it's sort of a self-imposed amendment because you can't enroll patients.
• 22:39
  SPEAKER_03: So there's any number of different reasons why this can happen.
• 22:42
  SPEAKER_03: And we've analyzed all of them and basically come up with a predictive model that will tell you here are the kinds of amendments you're likely to run into.
• 22:49
  SPEAKER_03: Now, just for the example, a typical amendment might result in a three to six month delay in the trial because you've got to go back and you've got to write this new document that historically takes quite a long time and rethink your whole trial, go through your process and resubmit and get that amendment approved.
• 23:04
  SPEAKER_03: And you know, some of the some of the forecasts say that, or some of the analysis says that every day of delay in a trial can cost up to half a million dollars in lost revenue.
• 23:13
  SPEAKER_03: So do the math, right?
• 23:14
  SPEAKER_03: Three months times that.
• 23:16
  SPEAKER_03: That's like many, many tens of millions of dollars.
• 23:18
  SPEAKER_03: And so this is one of the reasons, one of the many reasons why these trials, why ERUMs law, you know, has such big numbers associated with it, why these drug trials just cost so much on this because of these amendments.
• 23:28
  SPEAKER_03: And so the amendments in the first place is what I say.
• 23:32
  SPEAKER_01: What's that?
• 23:33
  SPEAKER_01: What was that?
• 23:33
  SPEAKER_03: It's best to avoid the amendments in the post than extra them and sort of muddle your way through fixing them.
• 23:39
  SPEAKER_03: And other examples of things we can forecast are the probability of technical success.
• 23:42
  SPEAKER_03: So it's like, will your trial actually meet its primary endpoints?
• 23:46
  SPEAKER_03: And so this is obviously hugely valuable because you don't want to waste a whole bunch of time and effort into a trial that has a 95% chance of failure.
• 23:55
  SPEAKER_03: So this is just a sampling of the things that we can predict using the vast array of really useful data that's out there drug data, biological data, clinical trial data, and so on.
• 24:05
  SPEAKER_03: It's it's a never-ending quest to add more data and to add more features to the predictive models to make them better and better.
• 24:11
  SPEAKER_03: But we've made a huge amount of progress in this past year.
• 24:13
  SPEAKER_01: And and you know, talking about the data, normalization is a big challenge, right?
• 24:17
  SPEAKER_01: People underestimate how important the data engineering are, right?
• 24:21
  SPEAKER_01: And and huge public data sources, pub, pub chem, clinical trials.gov, chem vb, medra, but but they're not normalized.
• 24:29
  SPEAKER_01: You can't just pull them down and the model, right?
• 24:31
  SPEAKER_01: And so what what you end up with is is you know, you you end up with with uh with with uh free text, but you need to have structured new build schemas many times from scratch, guided by these standards, right?
• 24:44
  SPEAKER_03: Yeah.
• 24:45
  SPEAKER_03: I mean, it's one of those things where on the face of it you might say, well, you know, everyone knows what a disease is, everyone knows what a drug is.
• 24:51
  SPEAKER_03: Can't you just index on that?
• 24:53
  SPEAKER_03: Like, can't you just link these data sets together by looking at the drug name or looking at the disease name and just you know equating them, right?
• 24:59
  SPEAKER_03: And no, you can't.
• 25:01
  SPEAKER_03: I mean, maybe in the 50 to 60 percent case, you might be able to, but it's all these kind of like pseudonyms, like alternative names, there's typos, in many cases manually entered or scanned or whatever.
• 25:12
  SPEAKER_03: Like, there's all these things you need to do to match up properly and get a 90 plus percent match rate.
• 25:18
  SPEAKER_03: And this applies across the board.
• 25:20
  SPEAKER_03: It's not just drugs and diseases, it's also activities, it's also the um the sponsor itself.
• 25:25
  SPEAKER_03: There's all these other things you need to be but able to match on really effectively.
• 25:29
  SPEAKER_03: And uh this is a universal problem in data engineering, like this.
• 25:32
  SPEAKER_03: Whenever you want to combine data sets, you've got a entity resolution, is what it's called in the business, and it's always a challenge.
• 25:39
  SPEAKER_01: Yes, yes.
• 25:40
  SPEAKER_01: We worked on OMOP uh years ago, or observational medical outcomes partnership, which was mapping together, you know, Metra and ICD9, ICD 10, you know, all the different uh structures.
• 25:52
  SPEAKER_01: And it was very, very difficult to do that, and it's it's certainly still a challenge.
• 25:56
  SPEAKER_01: And uh, talk a little bit about the biomedical concept library.
• 25:59
  SPEAKER_01: Why did it take uh years and hum require human clinical experts and not just AI?
• 26:05
  SPEAKER_03: Yeah, I mean, first of all, we started building this database before large language models really became a thing.
• 26:09
  SPEAKER_03: So that's one of the reasons.
• 26:11
  SPEAKER_03: It does mix together about a dozen or so data sets, and so there's just quite a bit of this entity matching and so on required.
• 26:18
  SPEAKER_03: And there was also quite a bit of consolidation required, like basically reaching the right level of granularity where we can provide richness in terms of the insights as far as cost and complexity and these other factors that we really care about providing insights to our customers about, and uh, you know, providing really highly granular, like super specific activities versus more generalized, general purpose, reusable set of activities.
• 26:42
  SPEAKER_03: And that's not the kind of thing that AI can make a call on.
• 26:45
  SPEAKER_03: So we had our clinical experts, even our CEO was involved, uh, he's a clinical scientist too, to figure out that right level of granularity.
• 26:52
  SPEAKER_03: And then sure, AI can help do matching.
• 26:55
  SPEAKER_03: That's one of the things it's actually really good at, but it is kind of expensive.
• 26:58
  SPEAKER_03: So if you have half a million, you know, activities that you need to sort of try to consolidate and and link together, using a certainly using a foundational model for that is going to be prohibitive.
• 27:09
  SPEAKER_03: It's gonna cost potentially millions, you know, to do a single run to match to do all that matching solely using an LLM.
• 27:14
  SPEAKER_03: And so you need to use a hybrid of embeddings and other methods, fuzzy matching and so on, in addition to the LLM to do things in a way that's reasonable as far as cost and time as well as um repeatable.
• 27:25
  SPEAKER_01: Yeah, and so what's interesting is you know, you this is obviously maps directly to our thesis that we always return to.
• 27:33
  SPEAKER_01: There's no AI without IA, right?
• 27:35
  SPEAKER_01: There's no artificial intelligence without information architecture.
• 27:39
  SPEAKER_01: And so you have built these normalized and uh harmonized uh data sets and and terminology uh catalogs that that help with that.
• 27:48
  SPEAKER_01: Where does that, where does the structured and curated foundation matter most in your work?
• 27:53
  SPEAKER_01: And then what do you need to have in place at your customers, right?
• 27:56
  SPEAKER_01: What do they need to bring to the table in terms of data sources and in terms of their own information architecture?
• 28:03
  SPEAKER_01: Yeah.
• 28:04
  SPEAKER_03: I mean that a fair question.
• 28:06
  SPEAKER_03: Sure, sure.
• 28:06
  SPEAKER_03: I mean, definitely on the customer's side, at its core, we we need we need a trial to analyze.
• 28:12
  SPEAKER_03: And I think ideally we need a stack of historical trials to enrich the sort of public data that we already have in our predictive models.
• 28:20
  SPEAKER_03: So this is something that, of course, you know, is the crown jewels of any pharma or biotech company, is they're kind of the trials that they've undergone or executed on, especially the ones that haven't been published.
• 28:31
  SPEAKER_03: And so we have to be really careful not to obviously not to share that with others or use that as generalized training material.
• 28:36
  SPEAKER_03: But what we can do is very relatively easily create custom models that actually are trained in part at least on the customer's own data or are enriched by the customer's own data.
• 28:46
  SPEAKER_03: And that can result in more accurate predictions that are more tuned towards that customer.
• 28:51
  SPEAKER_03: And so that's what we ask of our customers.
• 28:53
  SPEAKER_03: And I think for us, you know, I think it really just comes down to constantly being on the lookout for new research and new data sources that can help enhance what we're doing, and constantly being laser focused on data quality, on like how's that matching and how's our normalization and how's our analytics?
• 29:08
  SPEAKER_03: But you know, ultimately we just want to provide really good retrospective and prospective analytics to our customers to help them make really good design decisions with their trials.
• 29:16
  SPEAKER_01: Yeah, yeah, yeah.
• 29:17
  SPEAKER_01: And and and of course, the uh the cost of bad design is uh you know increases the likelihood of failure.
• 29:23
  SPEAKER_01: So getting ahead of that is really where you're trying to reduce those risks of of delays.
• 29:28
  SPEAKER_01: And what what does what does that do that those poor design decisions look like in practice?
• 29:34
  SPEAKER_01: So how do they catch how do organizations catch those problems earlier?
• 29:38
  SPEAKER_01: And what do they actually consist of?
• 29:40
  SPEAKER_01: Uh and again, go ahead.
• 29:42
  SPEAKER_01: Sure.
• 29:43
  SPEAKER_03: Well, the the classic source of design tension and and sort of mistakes, I think, is this tension between the drive, mostly on behalf of the clinical scientist, to kind of gather more data.
• 29:55
  SPEAKER_03: And so, rightly so, like the the more data you collect about patients during a trial, the more sure you can be about things.
• 30:01
  SPEAKER_03: And so there's this tendency to collect more and more.
• 30:04
  SPEAKER_03: And that's maybe compounded by the fact that, hey, if it worked before, we'll just throw it in there.
• 30:08
  SPEAKER_03: And so running, running kind of in opposition, running in opposition to that is the more complex you make a trial, like the more activities you add, the more blood draw, the more of these things that affect the patient, the more time that you're asking them to stay in the facility, if that's where the tests are being done, the more difficult it is for the patient, right?
• 30:25
  SPEAKER_03: I mean, the more blood you draw, the less likely a patient's gonna want to sign up for this.
• 30:29
  SPEAKER_03: The more time they have to spend away from their families or away from work, the less likely they are to sign up for this.
• 30:34
  SPEAKER_03: And so it's patient burden.
• 30:36
  SPEAKER_03: And also like the associated issue of enrollment is a huge reason trials fail.
• 30:41
  SPEAKER_03: You know, if you've got more patients, you have to buy your trial design.
• 30:44
  SPEAKER_03: And that involves, guess what?
• 30:45
  SPEAKER_03: Potentially an amendment or even failure, or even failure, right?
• 30:48
  SPEAKER_03: And so that is one of the main sources.
• 30:51
  SPEAKER_03: And so we're big fans of Ken Getz, who has this whole lean methodology around creating trials, where which is designed to kind of prevent this sort of trial bloat where you have too many activities in your trial.
• 31:02
  SPEAKER_03: And certainly the protocol optimization tool that I mentioned before we talked about before is designed to help weed out those activities that aren't truly necessary and sort of non-core.
• 31:11
  SPEAKER_03: And we we can tell you the impact of removing that trial from your, sorry, removing that unnecessary activity from your trial in terms of the cost and time and complexity and patient burden and so on.
• 31:22
  SPEAKER_01: Interesting.
• 31:22
  SPEAKER_01: And and and so you're also monitoring the regulatory environment in in real time to de-risk the trials.
• 31:29
  SPEAKER_01: So, have you can you give us examples of where kind of that real-time monitoring has impacted uh a trial design before it before the fact as opposed to after the fact, anything comes to mind?
• 31:40
  SPEAKER_03: Yeah, well, like I said, you know, oftentimes when we do these protocol optimizations, we are pulling down papers and research and other other risk relevant documents to help us figure out like is an activity truly necessary?
• 31:52
  SPEAKER_03: And so if there is some big new kind of like publication around this from the FDA or for from other groups or what have you, then we'll pick up on that and realize, hey, actually now this activity isn't so important anymore, or this new activity that is better, and we should use that instead.
• 32:08
  SPEAKER_03: We should recommend that instead.
• 32:10
  SPEAKER_03: And so this is expensive, obviously.
• 32:12
  SPEAKER_03: I mean, in terms of like staying up to date and parsing research documents and so on, but the stakes are so high here that it's well worth it.
• 32:20
  SPEAKER_03: Well to spend the time up front to come up with a good design, then come up with a sort of a sub-power design and then have to change it later.
• 32:28
  SPEAKER_03: That's like a hundred times more expensive.
• 32:30
  SPEAKER_01: Absolutely.
• 32:31
  SPEAKER_01: And so give us a picture of that fully digitized submission process.
• 32:36
  SPEAKER_01: What is it that's going to break that, you know, that PDF log jam with the FDA?
• 32:41
  SPEAKER_01: What's what's the future look like when these things are are more fully automated and enabled?
• 32:46
  SPEAKER_03: Yeah, I mean, I think look, we're very heavily vested in USDM, which is this kind of standard for expressing clinical trials.
• 32:53
  SPEAKER_03: And I think the dream is that people can submit using some future version of USDM that fully richly articulates and expresses the design of the trial to the FDA directly.
• 33:03
  SPEAKER_03: So there's no word doc and it's all structured.
• 33:05
  SPEAKER_03: And the FDA can easily use tools such as our own to analyze the trial and to come up with forecasts or to come up with sort of reasons and all these things that are historically really expensive and take a lot of time or maybe aren't even aren't even being really done at all in order for them to be sure that this is a safe trial that's going to be good for patients and good for and worth it.
• 33:24
  SPEAKER_03: And so um there is no need to construct word documents and there's no need to parse them, extract them and digitize them because they don't they're not the fundamental currency in which these trends are passed around.
• 33:35
  SPEAKER_03: We have this much more structured and much more detailed technical version of the trial representation of the trial, which is this kind of USDM plus form.
• 33:43
  SPEAKER_03: So that that's the dream.
• 33:44
  SPEAKER_03: And and hope hopefully we can get that, realize that dream within the next five years, I would say.
• 33:49
  SPEAKER_01: And what are the big obstacles to that?
• 33:50
  SPEAKER_01: Is it more on in the regulatory side?
• 33:53
  SPEAKER_01: Is it on the uh uh you know the customer side?
• 33:55
  SPEAKER_01: Uh it's on both sides.
• 33:57
  SPEAKER_03: I mean, there's still probably the majority of people out there uh obviously are sort of not yet using a system like Faro that is digital nat digitized trial native.
• 34:06
  SPEAKER_03: And so I think on the customer side, that's changing rapidly.
• 34:09
  SPEAKER_03: Like for some time now, people have been beating the digitization drum, not just us, but some really big pharma companies are super into this idea of digitization because it's just so obvious and so now I think the FDA is, you know, they're making some steps in this direction.
• 34:22
  SPEAKER_03: They're starting to use generative AI to analyze incoming Word docs.
• 34:25
  SPEAKER_03: And so presumably they're discovering how difficult that is and how potentially unnecessary it is if we all got on board with submitting digitized trials properly.
• 34:32
  SPEAKER_03: And so I imagine that at some point there'll be some effort there.
• 34:35
  SPEAKER_03: Hopefully, we can catalyze that where they can sort of see the light on this as well, and we can put together maybe a proof of concept with some of our more forward-thinking customers on the one hand, and with the etiquette on the other hand, to demonstrate pure digital submission.
• 34:47
  SPEAKER_01: Yeah, I mean, that sounds like it would be it would make a lot of sense and would really reduce the burden and the overhead and the inefficiencies of these processes.
• 34:55
  SPEAKER_01: What is standing in the way on the specifically on the on the pharma side?
• 35:00
  SPEAKER_01: Like what do they not get about the information architecture and the data engineering side of things?
• 35:06
  SPEAKER_01: Because again, uh I know that there's there are efforts in these areas, but you know, I think that generative AI and the promises of many vendors kind of cloud the uh the the landscape and and cause a lot more confusion and noise, where they're saying, well, you don't really need all that because the AI will do it all.
• 35:25
  SPEAKER_01: What is your what is your argument back to you know an executive who believes that you know you don't need the structure that you can simply go in with your AI and and these unstructured data sources and kind of figure it out?
• 35:39
  SPEAKER_03: Yeah, I mean, honestly, when I first started a FARO, there was this big sort of change management push, like, hey, we're we're pushing this new sort of way of doing things, and there's a certain amount of, I mean, we always had the Merck paper we published with one of our first customers that demonstrated the value of the approach.
• 35:55
  SPEAKER_03: But I think increasingly it's becoming sort of between executives really feeling the pressure from their own board to implement AI in some way, um, and between their own attempts to do this, which as I said before, kind of very quickly can run into the sharp edges of software engineering and so on, and the successes of our own customers, it's becoming easier and easier to make that case, which is really great.
• 36:15
  SPEAKER_03: I mean, not I'm not saying that in terms of like, you know, necessarily just Faro success, but I think for the industry in general, like if everyone can start moving towards a standard digital digit representation of the trial, it's gonna be good for everybody.
• 36:27
  SPEAKER_03: Ums, patients, you name it.
• 36:30
  SPEAKER_03: And so I think that as there is this kind of momentum growing where there are a number of top 20 pharma companies that are serious about this and they're starting to really transform their own, the way that they design their clinical trials and starting to really experience some positive results there.
• 36:44
  SPEAKER_03: Um and as that happens, it's gonna become on the margin more and more likely that that executive you just mentioned is just gonna have heard about the successes of work or or BMS or other customers have had with this.
• 36:53
  SPEAKER_03: And like, I want to be they want some of that.
• 36:56
  SPEAKER_03: Yeah, yeah.
• 36:56
  SPEAKER_03: It's the classic different more, you know, at first, at first you get the sort of forward-thinking kind of customers, and then after a while, there's enough critical mass and enough demonstrated value that you get the kind of followers as well.
• 37:07
  SPEAKER_01: Yeah, yeah.
• 37:08
  SPEAKER_01: And and so in in terms of even outside of clinical trials, what is the work that they that organizations need to do on their information architecture?
• 37:17
  SPEAKER_01: So, you know, if you say, okay, clinical trials, we're gonna be, we understand how this needs to be structured.
• 37:22
  SPEAKER_01: We need to, but going upstream or or going across the organization, what else do they need to do to be AI ready?
• 37:29
  SPEAKER_01: And you know, we look at in we have an AI readiness assessment, we look at knowledge readiness, we look at operational readiness, technical readiness, and governance readiness, and saying it's not just about the technology.
• 37:41
  SPEAKER_01: You you can be more mature in your technology, but if you're less mature in your knowledge, in terms of that ability to structure it and normalize it and componentize it, or whatever you need to do, it's not going to overcome that deficiency, right?
• 37:54
  SPEAKER_01: A good level four of technical maturity will not overcome a level two of knowledge maturity or operational maturity.
• 38:00
  SPEAKER_01: So, what else do they need to think about more broadly in the organization?
• 38:05
  SPEAKER_01: What's the work that needs to be done?
• 38:06
  SPEAKER_01: Would you say to be?
• 38:07
  SPEAKER_03: Yeah, I mean, I think I think there's a training aspect to this where there's tons of amazing training out there on how to really wield these tools properly that certainly Anthropic and other other sort of large AI vendors put out there for free because it's in their interest for people to be to be doing as well.
• 38:21
  SPEAKER_03: So I think that there is retooling that's required.
• 38:24
  SPEAKER_03: There is a certain amount of retraining, which is great for everybody.
• 38:27
  SPEAKER_03: I think that process, you know, some of the the potential pro the prospective customers that we talk to have thought about this a lot, like AI governance and safety and um just how do you bring an AI system into production and all these kinds of things.
• 38:41
  SPEAKER_03: So the questions they ask are very sophisticated, whereas other people really have no idea, have no clue whatsoever.
• 38:47
  SPEAKER_03: And so guess who's more likely to do well, you know, in adopting AI?
• 38:51
  SPEAKER_03: It's the companies that actively go out there and figure this stuff out.
• 38:53
  SPEAKER_03: And I think the tools, just choosing the right tools for the job, it's not enough to tell people.
• 38:57
  SPEAKER_03: Like I hear stories about how some large companies are saying, everybody's just gonna use cord code for everything.
• 39:04
  SPEAKER_03: And and you know, here's our massive token budget and just go make it happen.
• 39:07
  SPEAKER_03: And then now some of them are kind of pulling back and saying, oh my God, this is way more expensive than we thought.
• 39:12
  SPEAKER_03: And we're not getting the results that we expected, you know, like cut back, cut back.
• 39:15
  SPEAKER_03: And it's not the way to go about this.
• 39:17
  SPEAKER_03: The way to go about this is to figure out who's really seriously solving these problems and what's the best of three tools I can use to really do this stuff, to really apply AI to solving my specific clinical problems, and so really being measured about and and and thought about what's what tools you want to adopt.
• 39:32
  SPEAKER_03: So those are some of the things that come to mind in terms of going through this.
• 39:35
  SPEAKER_01: Well, you know, it I but I've also been uh in talking with some life sciences companies and especially on the uh you know the product and consumable side of things, where they're working directly with open AI engineers and they're saying you don't need ontology, you don't need, you know, knowledge graphs, you don't need information architecture because our tools will do it all.
• 39:57
  SPEAKER_01: And and and you know, I think we understand that that's not the case, right?
• 40:01
  SPEAKER_01: That you still need to curate your knowledge, you still need to structure it in a way that uh it can be retrievable, you still need to contextualize that information, you still need to, you know, have a very detailed understanding of what content applies to what situation or what model of you know of gas chromatograph or whatever equipment you have and whatever lab uh configuration you have and what the right protocols are for research and so on.
• 40:28
  SPEAKER_01: And and and again, what what do you say to an organization that's hearing that from the vendors to say you don't need that stuff?
• 40:34
  SPEAKER_01: What would you say to them?
• 40:36
  SPEAKER_03: Well, there's a number of things.
• 40:37
  SPEAKER_03: I mean, I would just say, first of all, what is their industry-specific, you know, detailed solution for you know analyzing a clinical trial and for coming up with a bunch of detailed metrics that are accurate around that trial.
• 40:49
  SPEAKER_01: Even beyond clinical trials, or upstream, even on the research side, the knowledge side, you know, the the uh you know, the the experimental design, the the research, you know, upstream before you get there, right?
• 41:01
  SPEAKER_01: So so look at that ecosystem of the enterprise.
• 41:04
  SPEAKER_03: Yeah, I mean, I would just say who are they working with and what results do they have so far?
• 41:08
  SPEAKER_03: I do think that Google has a leg to stand on here in terms of you know, uh alpha fault technology they've built, which is incredible for for sort of drug discovery and analysis, and they've rolled that out into this isomorphic labs, you know, startup that is doing nothing but that.
• 41:22
  SPEAKER_03: And so that's really okay, that's legitimate.
• 41:25
  SPEAKER_03: And they're working with some big companies and so on.
• 41:27
  SPEAKER_03: But I don't know this whole idea that, oh, we'll just take care of it, you know, like that this idea that generic general purpose models can just ingest all of your data and do all the stuff for you.
• 41:36
  SPEAKER_03: I don't know.
• 41:37
  SPEAKER_03: Like I'd love I'd love to see the paper that describes how that's done.
• 41:40
  SPEAKER_03: And maybe I should.
• 41:41
  SPEAKER_03: I I'm sure I would have found it, you know, via my research pages, but I might have missed it.
• 41:45
  SPEAKER_03: But I do I just think that that's not I think that's that's sort of an easy sales push to make because these models are so capable in so many areas.
• 41:53
  SPEAKER_03: But I think there's a huge difference between using AI to sort of generate emails and corporate sort of you know documentation versus series.
• 42:02
  SPEAKER_01: Designing an experiment or designing, you know, uh, you know, genomic uh analysis or proteomic analysis or whatever that might be, because you have so many variables around how you what your outcomes need to be and what your substrates are and what your starting materials and what your uh what you what lab equipment you have, and you know, what uh you know what antibodies you're using.
• 42:22
  SPEAKER_01: I mean, there's all sorts of things that need to go into that, which really require that precise knowledge engineering.
• 42:27
  SPEAKER_01: And again, I think that uh some of the AI vendors are are doing a disservice to the market when they are saying that our model will do it all because.
• 42:35
  SPEAKER_03: Yeah, I really, I really agree with that.
• 42:36
  SPEAKER_03: Like having having done all of that data engineering to link together all those different data sets.
• 42:40
  SPEAKER_03: I mean, if they've done that, then okay, fine.
• 42:44
  SPEAKER_03: But I don't I don't think this is really what they're doing.
• 42:46
  SPEAKER_03: They're building these kind of more general purpose models that are just a whole bunch of unstructured information and operate in a certain pretty general purpose way.
• 42:54
  SPEAKER_03: And so it's true that yes, they've included a lot of medical information, and so these models are capable of you know performing inference, which is on clinical information, which is why we use them for our protocol optimization process, but you still need to put a lot of domain-specific logic in there that I think there's no there's no escaping, you know, needing to have a clinical team to really review this stuff.
• 43:14
  SPEAKER_03: And so I would I personally like I would just advise people to really be specialists on this one.
• 43:19
  SPEAKER_03: But but but you know, like competition is good always.
• 43:22
  SPEAKER_01: So, uh, where do you see Ferrell Health in five years?
• 43:25
  SPEAKER_01: What what's the future look like for you guys?
• 43:28
  SPEAKER_03: I mean, I would really like to see the majority of the groups out there, like the drug companies and biotechs that are really trying to bring new treatments to market using this more, much more modern, digitized, AI-enabled, predictive method of drug development.
• 43:44
  SPEAKER_03: And so the result will be within five years, I think we'll see hopefully a lot fewer failures and a lot more drugs coming to market because the the sort of the barrier to entry hopefully drops.
• 43:55
  SPEAKER_03: And so newer groups, like more startups and you know, innovative new biotech companies can sort of, on the margin, it's worth them taking the risk or it's worth them getting in there with some new radical new treatment.
• 44:06
  SPEAKER_03: And so we start seeing really interesting new treatments that are maybe personalized to someone's genome and all these kind of things people are talking about, right?
• 44:13
  SPEAKER_03: Kind of well on their way to market because it's easier and it's safer to do so.
• 44:17
  SPEAKER_03: So that's the kind of impact that ultimately that we want to have is better treatments to market faster and you know, in a in a in a way that's that's just safer for patients.
• 44:26
  SPEAKER_01: E room is not, yeah, eRum's law is not sustainable, right?
• 44:31
  SPEAKER_03: Well, I mean, obviously any exponential increase in cost is not sustainable by definition.
• 44:35
  SPEAKER_03: And so we want we want to ban the ERUM's law and and hopefully reverse it.
• 44:40
  SPEAKER_01: I mean, why not?
• 44:41
  SPEAKER_01: Right, exactly.
• 44:41
  SPEAKER_01: Exactly.
• 44:42
  SPEAKER_01: That's great.
• 44:43
  SPEAKER_01: Well, wonderful.
• 44:44
  SPEAKER_01: Patrick, thank you so much for joining us and sharing your perspective on what this really means uh to apply AI in such a high-stakes uh industry.
• 44:53
  SPEAKER_01: Thank you very much for your time today.
• 44:55
  SPEAKER_01: You're welcome, Seth.
• 44:56
  SPEAKER_01: Really enjoyed the conversation.
• 44:57
  SPEAKER_01: Great.
• 44:57
  SPEAKER_01: And uh thank you to our listeners uh for tuning into the Early AI podcast.
• 45:01
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• 45:08
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• 45:12
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