Everyday AI Podcast – An AI and ChatGPT Podcast - EP 287: Harnessing AI Technologies for Next-Generation Clinical Trials

Episode Date: June 5, 2024

Clinical trials can sometimes take years and cost many of millions of dollars. Why is that? And how can AI help bring faster and better outcomes?  Saurabh Jain, Executive Chairman at TrialKey.ai,  joins us to discuss how we can harness AI for clinical trials. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Saurabh questions on AI in clinical trialsRelated Episode: Ep 107: How AI Turns Clinical Trials into Medical KnowledgeUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. State and Limitations of Traditional Clinical Trials2. Role of Generative AI in Modern Drug Development3. AI in Clinical Trials4. Exploring ChatGPT in Clinical TrialsTimestamps:01:30 Daily AI news04:20 About Saurabh and TrialKey.ai09:33 ChatGPT transforms unstructured data to structured.10:43 Addressing hallucinations, incomplete datasets, and novel drugs.15:16 Challenges in drug development and commercialization balancing.18:30 Challenges of AI in research investment and focus.24:15 Balancing AI and human ethics in medicine.28:23 AI's potential in clinical trials, despite challenges.Keywords:Generative AI, clinical trials, advanced AI, drug discovery, Treatment Market, AI news, Elon Musk, NVIDIA GPU chips, Tesla, XAI, US Treasury, Janet Yellen, Saurabh Jain, Trial Key AI,Large language model, COVID-19, drug development, Jordan Wilson, AI simulations, drug market approval, FDA regulations, GPT technology, chat GPT, AI computer technology, pharmaceutical companies, machine learning, rare diseases, trial design, phase 3 trials, trial success prediction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. Clinical trials can be slow and antiquated and expensive.

And sometimes it can be a lot of guesswork. But guess what? Large language models and generative AI have completely changed the way that companies can discover new drugs and bring new treatments to the market for all of us. So I think a lot of times when we think about generative AI, we, think about productivity and we think about, oh, you know, now we can have all of this unstructured data and we can use it in ways to, you know, boost our company or, you know, grow our career. But it's about more than that, right? It's also about improving, you know, the quality of our lives and

offering new opportunities to people. So that's what we're going to be talking about today on Everyday AI. So what's going on, y'all? Thanks for joining. My name is Jordan Wilson and I am the host, and this is for you. Everyday AI is your daily guide to grow your company and grow your career to keep up with everything that's happening in the world of generative AI. So if you're listening on the podcast, thank you as always. Make sure to check out your show notes. We always have related episodes. Way to reach out to us. Send us a message if you want. And if you're joining us live, thank you as well. So before we get into today's conversation, let's first start by going over the AI news. And as a reminder, we will be recapping today's show and everything that

happening in the world of AI in our free daily newsletter. So make sure you go to your everyday AI.com and sign up for that free daily newsletter. All right. So let's talk about what's happening in the world of AI news today. So first, AI employees are banning together to warn the public. So a group of current and former employees at top AI companies just published a letter voicing concerns about the dangers of unregulated, advanced AI and are calling for more oversight and transparency from companies.

So this is signed by some current and former employees from companies such as OpenAI and Google DeepMind, but this group is concerned about the potential for AI to cause serious harm, if not properly regulated, including inequality, manipulation, and even human extinction. Wow. All right. So they believe that employees are in a unique position to hold companies accountable, but are often restricted by non-disclosure agreements and lack of whistleblank. lower protections. Yeah, pretty big topic there. So interesting that this group of employees is coming out and speaking about it.

All right. Next in AI news, well, Elon Musk has confirmed why he diverted so many Nvidia GPU chips from Tesla to his other company, X-AI. So Elon Musk has confirmed that he did redirect thousands of GPU AI chips from Tesla to his company's X-Corp and X-AI. So the move was made what he said due to a lack of space for the chips and potential content. with Nvidia. So he redirected 12,000 Nvidia GPU chips from Tesla to XAI. And he said it was due to a lack of space for chips. But many are talking that it is due to his ongoing battle to have a little bit more voting control at Tesla.

So interesting that he's, you know, taking from his left hand and feeding the right. All right. Last but not least, the U.S. Treasury Secretary is sending warnings about AI as well. A lot of AI warnings today. So Treasury Secretary Janet Yellen warns of both opportunities and risks associated with the use of AI in the financial system. She also noted that current AI models can be opaque, produce biased results, and lack proper risk management frameworks.

And regulators, including the financial stability oversight council, are monitoring the impact of AI on financial stability. So, yeah, we will have a lot more on those stories and everything else. in our newsletter. So make sure you go to your everyday AI.com and sign up for that free daily newsletter. And you know, always hit me with a reply. I read them all. So let me know. All right, but today we're not here to talk about the AI news. Well, we at least got that out of the way. But we are here to talk about how AI is changing and creating really the next generation of clinical trials. So I am excited today to bring on my guest. There we go. We have Sarab Jane,

who is the executive chairman of Trial Key AI. Thank you so much. for joining the show. Absolutely pleasure. Thank you, Jordan. All right. So, hey, you know, for everyone out there, could you tell us a little bit about yourself and what you all do at Trial Key AI? Yeah, totally. Look, I'm software engineer by trade. Spend a lot of my time on the tech side of things, and then these days, there's a lot more on the commercialization of tech. So right now, I'm about half a dozen company boards, and one of the companies I'm super interested in is Trial Key. So the problem that we try to solve is what we do is we got 350,000 clinical trials. We built our own custom large language model to get about 700 variables of each trial. So these are things like, you know,

the condition, the patient, the sponsor, the sites, the medical components within that clinical trial. So it turned all this unstructured data into structured data, and then we use that to try to predict the outcome of clinical trials. And three, three and a half years later with a whole bunch of investment, we can do that now with about 90% accuracy. So it sounds super crazy, but we can predict whether a trial is going to succeed or not before it starts with 90% accuracy. Yeah, and maybe before we dive into that a little bit, talk about things like unstructured data and improving clinical trial accuracy,

maybe can we talk a little bit about what this has looked like traditionally, right? So kind of before generative AI and before large language models, I mean, can you tell us a little bit about, well, first of all, why do we need clinical trials? What do they do and how have they historically worked in years or decades past? Yeah, totally. So just some of the real foundational things around clinical trials. So you're going back about 100 years ago, a couple of hundred years ago, anyone could claim anything about any medication proper on a bottle.

And some stuff works, some stuff didn't, some people died, and the government thought, hey, this would actually totally regulate this. And this is when medicine started to really come together when it became like an evidence-based pursuit. So you'll have, you know, animal trials and you'll have person human trials, and you'll have phase one. Phase one is much more around safety, is what we're doing, giving this drug or treatment to somebody causing any harm. Phase two is more on efficacy. Hey, does it actually work? And then phase three is efficacy, but at a much, much larger scale.

Then sometimes you have phase four or you go direct to market. So the idea is this rigorous process takes about a decade for most drugs, for most drugs from start to finish. It's designed to weed out stuff that doesn't work, that causes harm to people. So then, you know, the stuff that does get into people, more often they're not, it works quite well, and it doesn't cause any unknown side effects. That's wild to me to think that, you know, sometimes it can take a decade or more, right? So what we're saying is in theory, a pharmaceutical company or a group of scientists might actually have something that could help today. But it could take 10 years, right?

Oh, totally. And look, I'm generally quite an optimistic person. So one of the great silver linings around COVID was it was kind of awesome, like all the humanity banded together to solve a single problem. And you have like a thousand labs around the world trying different COVID trials. And we kind of got something out to market in a tenth of the time that normally would have happened. Sure, it probably took like insane amount of resources and insane motivation to do. But that's the only time that there's actually been like a quick drug to market.

Otherwise, it's just a decade or problem. It takes a decade from inception to something being on the shelf that's actually treating people. So, you know, speaking of the kind of time around COVID, that kind of coincided, you know, a little bit after, you know, kind of this surgence of large language models, you know, so GPT technology became, you know, available in 2020, you know, started rolling out in, you know, popular customer-facing applications such as chat GPT in 2022. So, you know, how, you know, I'm curious because you do have a background, you know, in this area. In general, I mean, I think some people saw chat GPT and they're like, oh, this is kind of a toy for, you know, writing blog posts or something

like that. How would you say that in your field, generative AI and in large language models were initially perceived? Totally. So, but so I said like chat GPT, I did not think in my life, that would be a solved problem. I didn't think that someone would be able to solve the cheering test. I thought that would be like something my kids would do and my kids' kids would do.

So when I did my thesis, about 20, 25 years ago, it was all about predicting cancer and patient. Back in the day, Java 2.0, you had to hand, roll everything, 80,000 lines of code. And you could build a model that was about 50% accurate because we only had structured data. So it's only structured data in fields

that a clinician actually broke into like an exhaust spreadsheet, like this field means this, different field means that. And the really cool thing that ChatGPT did it let us go from unstructured data to structure data. So for example, you upload a clinical trial with the chat GPT and you ask it what compound was used. And it goes as a thing and it comes back and it says, great, this was a compound that was you. You'd ask it how many patients were in this trial? Where are the sites?

Where are the locations? Who funded it? So all of a sudden, you can't have access to all of humanity's knowledge that you can now put into a format that you can actually put into a decision engine. And, you know, what were some of the early concerns? around using large language models as well. You know, obviously there's things that we all deal with, right? Like hallucinations and, you know, is this model actually, you know, using the data that we give it, you know, context windows, etc.

You know, what were some of mainly, you know, just the industry kind of went through in general when it came to implementing tools like large language models in clinical trials? So a couple of things. One is all around the hallucination or having blanks. so having incomplete data sets. That you don't, that's kind of hard to solve on the LLM side. You can kind of hyper tune them and make them more focus, but you're always going to have a bit of that.

So what you want to do is you want to solve that on the actually position of engine side, the predictor side. The other kind of concerns, I guess we had, or the issue had was how does this work for novel drugs? Like, how does this work in a data set, which, you know, the world has never seen before? And then we get a whole bunch of work, and I'm kind of something interesting stuff that we found out around that,

that around that. But that were the things, like, how do you do with scenarios that are not having before? And then how do you deal with kind of incorrect data coming through? And how do you think so far, you know, because those are, you know, obviously things that you have to take into consideration. How has the industry kind of responded to some of those concerns? Yeah, totally. I mean, can keep in mind that probably no one that we know is kind of doing AI, clinical trial prediction, designed to the internet that we are. So it's still very new. to the industry. Like we only launched a marker two or three months ago. So it's super early days. We only got our first set of paid customers using this. But generally what you find,

it's a completely different approach. So the way traditionally works is like a lot of these things are based on like a clinician with years worth of experience about a certain drug and certain disease. Now the downside of that is no single clinician can have like a complete nullies set. But no one can actually know everything that's happened in every clinical trial over 3,000 trials over 20 years, but an AI tech computer totally can. So AI will find connections that a person will never see. So when we show people our tech, the first thing is surely that's not right. Surely that's not believable.

That does not make sense. That's even a solvable thing. But when you bring up their trial and talk you their experience and some of the stuff the data shows, that's kind of when you get them on side very, very quickly. just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form

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See it today at firefly.adopi.com. Yeah. And, you know, speaking of, you know, you mentioned some of those things, you know, taking in this data from, you know, all of these, you know, 35,000 clinical trial. Right. So all of these, or sorry, 350,000 clinical trials, all these different variables and, you know, improving the accuracy of clinical trials. what does this ultimately mean for, you know, on the front end, right?

Like how can this influence, you know, whether it's pharmaceutical companies, you know, research organizations, how can this data and kind of what your company is working on, how can this influence them and maybe in good ways or potentially bad ways on what they maybe should or shouldn't focus on? Probably the even the thesis that we have is that everything about how humans interact with drugs and devices is already known. Like that knowledge already exists in the world, except it just happens to be in a crappy kind of way across the 300,000 clinical trials. We've just bought that together in a single AI model. So now if I'm a pharmaceutical company, I can get an insight into things that I actually just didn't know before. So I'll be claiming to fame now is we can predict whether a trial succeeds with about 90% accuracy before. you start. Super crazy, super not credible, but that's actually what we can do now. So if you think

about that from a pharmaceutical perspective, like we think there's three main use cases. One use cases that will probably just make free because of like good for the world is for patients. If you have a loved one who needs to once got on a clinical trial, we'll help to figure out which trials to get on, which is most likely going to succeed. So then they have the best chance. If you're a pharmaceutical company and you've got 100 trials across different drugs, we'll help you figure out which trial are most likely going to succeed. So you can over-index and over-invest in them. And you might call out the ones at least likely to succeed.

Or if you're doing a new trial, we'll help you through January of AI design the best possible trial. Then the last use case, which I didn't see coming, but it's so a thing is for the investment community. So we did this for a company last week. We're going up to funding, Osteoarthriders company. We did a model for them to predict their likelihood of success of completing that trial.

So when they go off to invest it, they can say, great, we've got an expedient chance of succeeding this much more than our competitor said. Please invest in us. And then on the flip side of that, something the CTO and I do for fun right now is we invest in pharmaceutical with our own cash. You buy before a company announces clinical trial inflection, share price goes out, and you saw afterwards. Yeah. And, you know, something I'm curious about when we talked about this, you know, briefly before the show, you know, this can obviously. help companies save a lot of money. Number one, yes, it does bring drugs and treatments faster to the market, which helps humanity, right? It helps us all, you know, hopefully live higher quality

of lives. But what about, you know, companies that may be too focused on the commercialization piece, you know, are there going to be maybe now, you know, drugs or types of diseases that maybe just go undiscovered or unrearched, mainly because, you know, hey, we are able to now make more money or we're able to just focus on, you know, these bigger problems that have a larger pool of potential customers and clients.

So I guess how can companies balance that? And is that something where, you know, smaller or rarer diseases maybe aren't going to get explored at the same rate? Totally. So what tech like ours does, it makes it like your, like you need perfect capital.

allocation in the market where money goes, it's like meritocracy, right? If you're most likely going to succeed, you'll invariably get more investment and more funding. And most farmers, superfluic companies are not philanthropic enterprises, right? They're there for a profit motive. So the kind of things you want to figure out is how likely it might to succeed. How does that compare against my competitor set? And so we've applied machine learning to figure out like who's your competitor set, like who's trying to solve the same problem, albeit with different ways.

Some people might have surgery. Some might have devices. someone have drugs, some might have any other interventions. And who's most likely going to succeed? And when do you think they're going to succeed? Because the theory is you want to get out to market with the most, if you have an invasive intervention model or ingestion method, you have to take a needle versus the tablet

versus a cream. You're going to find it's going to be harder to get to market. If someone else solves that problem with a much easier ingestion method or another model kind of thing, they'll probably succeed. So allow you to see, okay, I'm actually going to succeed after these guys. they're more likely to be sophisticated than I am. So I like you to kind of choose, well, is this the best use of my capital? What that means for rare diseases.

So I mean, I'll use COVID as an example. So this is, so this company started a year before COVID. And there were about 400 vaccine candidates at the time. And we predicted Pfizer and the donor is number one and two. And actually Denica is number seven. And history is shown, we published that out. We've sent those results out, got published. And that's kind of what happened.

So what that meant is not because of us, because of people were really known back then, but those two, the three succeeded. Now they've been over indexed in terms of funding. The Moderna is investing a lot in MRNA and they've been able to get a lot more kind of cash because they've done that. The other part around kind of rare conditions, I think what's going to happen, unfortunately, is the money will go to the problem that's easiest to solve that has the biggest payback versus its competitors. So you'll have things like diabetes, weight loss, cardiovascular cancer will be over indexed in terms of probably investment. And some of the rare genetic disorders will probably just get less attention. What is, I guess, what new problems then might arise, right?

Like might there, you know, kind of before generative AI and before large language models could predict the likelihood of something working, it seemed like, I don't know, from an outsider's perspective, I don't know much about the space. but you would think that companies would be investing equally in all of these different areas and all of these different problems and diseases to kind of see which one worked. So if large language model can actually take out some of that guesswork, I see how it's a good thing, but then how can you still, you know, whether it's the industry, your company or, you know, society, how can we still make sure that people are researching and investing in these areas

that are these rarer and maybe less profitable or less with a smaller kind of pool of people that are suffering from these diseases. Yeah, so what you'll probably find is that clinical trials with tech like us will iterate a lot quicker. So let's just say it takes right now a decade, right, to go from start to finish. What you'll find is it might still take that long, might share a few years off, but you'll be doing less trials. So right now, humanity does about 35,000 trials a year.

I'll probably drop to 5 or 10,000 trials. but it leaves lots of bandwidth to stop go and solve other things and as long as there's a good capital model or incentive or some way that incents people to go solve some of the other problems that will probably still happen because luckily not everyone does this for a pure financial motive people have personal experience and those kind of things but you'll find some of those rare conditions they'll probably get funding now when they couldn't otherwise get funding because they'll be able to run a clinical trial and a i.m. awesome. I think we're going to solve this problem.

There's 72% chance of success. Face line is 10%. You know, one thing, you know, I'm curious about. So, you know, you said that, you know, your company has essentially, you know, built a model that helps you, you know, pull out and extract all this data from 350,000 clinical trials, you know, 700, you know, different variables. What are some of the things kind of regardless of, you know, how this can be used in the future? What are some of the things that your company has found when looking at all of that data?

Because I'm sure that there's kind of like what you said earlier, I'm sure there's connections that large language models can make that humans can't. So what are some of the things that you've found in your process of creating this model for others to use? Let me ask you a question. And it's a trick question. What do you think is more important in a phase three trial? the actual drug or the way the trial is succeeded. So the way the trial was designed,

what do you think is more important on whether a drug works and get through approval or not? I'm going to, well, trick question. I love to. Yeah, so I'm going to guess the second. I'm going to say how it's used, because regardless of the drug, if it's not used correctly,

it's maybe not relevant. That's my guess. That is totally it, right? Look, it makes no sense. I'd actually say, oh, my God, the drug is so much more important because if the drug doesn't work, nothing else matters. But you're fine.

There's about 80% of the probability of success in about how the trial was succeeded. So how the trial was designed. The drug only accounts to about 20% of the likelihood of success. Because keep in mind, in phase two, you kind of prove some level of efficacy. That's how you kind of get to phase three. It's not the main dish.

And that's totally crazy. It's totally not credible, but that's actually what the data shows. The other thing that I reckon the data has shown us as well is there's lots of orphan drugs. Drugs that actually were really good and were going to be super efficacious, have a lot of efficacy, but they never got taken to market because they had a crappy trial designer. The principal investigator, the doctor and the chief medical officer didn't design a great trial

or didn't have enough funding or couldn't do what they wanted to do. And they never got some amazing drug to market. You know, so why? One thing I'm curious about is, you know, I kind of think right now, people who are doing knowledge work, right? So they're reading, you know, PDFs and they're creating outlines and pitches and presentations. To me, it seems kind of wild if people aren't using generative AI in their day-to-day work. When it comes to clinical trials and the future, might that be how we view clinical trials in a couple of years that if companies aren't using large language models,

if they're not using digital twin simulations, like how do you kind of see the future of this playing out? Yeah, totally. As I said, there's about 35,000 trials that happen in the world. I reckon that's going to drop to like 5,000 and there'll be millions and millions of trials that will happen in an AI simulator. And at some point in time, we can have ethics committees that will be like it's completely unethical to test a drug in a human person unless it's been done in an AI simulator. So the analogy I gave you beforehand was it's a bit like learning how to drive, right? I learned it drive like 20, 25 years ago.

It was super dangerous. I'm so like it never killed anybody. But everybody is 17 and learns to drive, but horrible, right? And my kids will probably never learn to drive in a real car. They'll go to the DMV, they'll learn how to drive in this simulator. They'll have to pass a testing simulator. And we'll talk about how back in the olden days he was super unsafe and let people learn on the roads. And the exact same thing were happening for clinical trials at some point, it would be like,

that's crazy, right? In the dark ages, we actually tested drugs in people before in the AI world. Yeah, it's kind of crazy to think about it, but I can see both sides of that, right? So I can see how, you know, oh, it seems wild to not test certain drugs on humans if humans are the ones that are ultimately taking them. But kind of the AI side of me says, okay, it might be a completely. waste of time and you might be withholding, you know, great new discoveries from the population that can help if we are using humans. You know, I'm curious, you know, you said that you,

you know, 20-some years ago wrote your thesis on, you know, AI and being able to detect cancer. So, you know, where do you even fall personally on that? Do you ever struggle with, you know, balancing this concept of using generative AI and using large language models with the human in you that's been doing this for decades? Totally. I mean, I think so you always still do a trial in a person because you can't simulate everything in the human body in AI, and humans are so different and so unique.

But I think what will happen is before it's done in a person, it'll be done in AI. And that will call out a lot of trials that can never happen or not be safe or probably not succeed. And then only the best one will be done in people. So it just kind of means that you put less people through unnecessary trials, right? You put less people in harm's way of drugs that action shouldn't take or drugs that are probably not going to succeed. The other thing that it does

do for people as well is one of the models that we've built, it's all around trying to figure out, hey, I have this drug candidate. What are the conditions this could solve? So I might have a drug that works great for, I don't know, hair loss, for example. But hey, it helps with acne as well. So before you do a trial in people for acone, you can actually run that through an AI simulator and we'll recommend all the other conditions we think this therapeutic could actually work for. So it might actually really expand out the scope of different treatments in different areas as well.

That's, you know, super, super interesting. And maybe could you, for those of us unaware, because, you know, we talked about, oh, it could take maybe a decade in some cases for, you know, a good discovery or a good drug to finally go to market. So where are we at today, you know, for companies who are, you know, using, whether they're, you know, using your service or one of your competitors, what does that look like today in terms of, you know, months, years, and also cost? And then what can you see that, you know, being in the future?

So kind of walk us, you know, current day companies that are using it. And then in the future, what that looks like in time and money. Yeah, totally. Look, unfortunately, it still takes a decade. FDA legislation regulation is not kind of caught up to AI. For what it means is when you start the journey, you can be more confident that you've got a model or a clinical trial that's going to succeed.

So theoretically, there'll be less failures along the way. Like, we'll get that meritocracy that we spoke about. But I think what will happen over time is as we get more and more generative AI, we design more trials from scratch for doctors or for clinicians. We'll hopefully speed up the process a bit, call off a few months here, a few months there. But I reckon it's going to be 10 years, maybe 15, before you get like the FDA to consider clinical trials in AI

as a proxy for what actually happens in people. The one exception I'll actually give is one of the things we're working on now with the client, it's what they call real world evidence. So instead of you're running your trial, go collect every, all the evidence, what's happened in this area across the last 10 years, collate that into a virtual trial and submit that through for approval.

So the TGA, that's the authority in Australia, similar to the FDA, they've started to accept some real-world evidence trials. They don't have the same kind of bar as a clinical trial, but it's a great way to start for some alternatives, some of those kinds of things. So that's, yeah, it's interesting, right? And also a little disheartening maybe, right, that all of these, you know, technologies are helping this field advance, but, you know, still

the slow turning wheel of, you know, a big organization like an FDA can still slow down things for everyone else. So, you know, we've talked about a lot of things here, but, you know, as we kind of wrap up today's show, we've explored a lot of different ways that, you know, AI technologies can really help in the future of clinical trials. And we've talked a little bit about, you know, some of the school and antiquated processes, but, you know, maybe what's the one takeaway that you hope that people can keep in mind as they look at the future of how AI is used in clinical trials? Yeah, so if I take a set back, not just in clinical trials, but like, you know, any kind of similar problem you said. So what we've actually done, and this is what the co-founder

genius was, he's found a world clinical trials in an example where there's a thousand variables or 700 variable in our case, you have a known outcome, and you want to figure out what variable leads that known outcome. So we just happen to apply that kind of to clinical trials. And basically anywhere where you've got a whole bunch of variables that lead to an outcome, you can now, for the first time maybe using AI, actually figure out what variables lead to that outcome. And I think that generic solution set will now solve a lot more problems around the world,

I think. I love to see it. You know, another great example today on how generative AI is really shaping our future in a good way. So this was a great conversation. So thank you so much, Sherob, for joining the Everyday AI show. We really appreciate your time. All right. And hey, as a reminder to everyone, we covered a lot today.

So make sure to go to your EverydayAI.com. Sign up for the free daily newsletter. We'll be recapping every day. everything that we talked about and more. You can find out a little bit more about the company there that we talked about today, trial key AI, as well as keeping up with everything that's going on in the world of generative AI. So thank you for joining us today. We hope to see you back tomorrow and every day for more everyday AI. Thanks y'all. Meet Firefly AI assistant. Now live in Adobe Firefly, the Allman One creative AI studio. Just describe what you want to create in your own words

and the assistant handles the rest, orchestrating multi-step workflow. across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time.

See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, Visit your everyday AI.com and sign up to our daily newsletter so you don't get left behind.

Go break some barriers and we'll see you next time.