Ep 287: Harnessing AI Technologies for Next-Generation Clinical Trials

The Evolution of Clinical Trials With AI

Traditional clinical trials are a laborious, time-consuming, and costly process. However, the advent of Generative AI has begun to rewrite the narrative, ushering in a new era of speed and efficiency in pharmacological research and development. As transformative as it's been, it's the utilization of AI in predicting clinical trial outcomes that has industry professionals buzzing– imagine drug companies being able to forecast the results of their trials with an impressive 90% accuracy.

AI Fast-Tracking Drug Approvals Amidst Pandemic

The Covid-19 pandemic has necessitated unprecedented global cooperation in the field of pharmaceutical research, significantly accelerating speed-to-market for vital drugs. This collaborative approach, coupled with AI-powered innovations, has propelled the prospect of rapid drug approvals from theory to reality. Even though the current FDA regulations result in an average of a decade-long wait for drug approval, embracing AI-assisted trials could shave off a considerable chunk of this timeline.

Generative AI: The Future of Knowledge Work and Clinical Trials

If current trends continue, there may be a seismic shift from physical clinical trials to AI-based simulations. While the human body's complexity and uniqueness present a formidable challenge, these AI-powered simulations could efficiently filter out superfluous trials, leading to safer, better-targeted human trials. This shift aligns with the pharmaceutical industry’s need for operational efficiency, treatment scope expansion, and improved patient safety.

The Promise of Real-World Evidence (RWE) Trials

Real-world evidence trials, though not yet widely adopted, are earning acceptance in some quarters. In the ideal scenario, AI could bolster the confidence in the outcome of these proof-of-concept trials, leading to broader acceptance and implementation.

Opportunities in Capital Allocation and Rare Disease Study

AI-driven clinical trials could also revolutionize how pharmaceutical firms allocate capital. By using machine learning to predict the likelihood of a competitor's success and track funding trends, companies could efficiently deploy resources. What's more, the speed and efficiency of AI-assisted trials could prompt more research into less common diseases — conditions often passed over due to lack of profitability.


The current momentum of regulatory adaptation may seem slow, but there's no denying that AI technologies are propelling clinical trials into an era of unprecedented advancement. With AI's potential to offer universal solutions, it's poised to address a variety of industry-wide challenges. Embracing AI technology and its advancements is not just a smart move, but a necessary one for those looking to remain at the forefront of the pharmaceutical industry. The future is here, and it's powered by AI.

Topics Covered in This Episode

1. State and Limitations of Traditional Clinical Trials
2. Role of Generative AI in Modern Drug Development
3. AI in Clinical Trials
4. Exploring ChatGPT in Clinical Trials

Podcast Transcript

Jordan Wilson [00:00:15]:
Clinical trials can be slow and antiquated and expensive, and sometimes it can be a lot of guesswork. But guess what? Well, 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 in ways to, you know, boost our company or, you you know, grow our career. But it's about more than that. Right? It's also about improving, you know, you know, the quality of our lives and offering new opportunities to people. So that's what we're gonna 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.

Jordan Wilson [00:01:09]:
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's happening in the world of AI in our free daily newsletter.

Jordan Wilson [00:01:41]:
So make sure you go you go to your everyday ai.com and sign up for that free daily newsletter. Alright. 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. Alright.

Jordan Wilson [00:02:28]:
So they believe that employees are in a unique position to hold companies accountable but are often restricted by nondisclosure agreements and lack of whistleblower protections. Yeah. Pretty, pretty big topic there. So, interesting that this group of employees is coming out and speaking about it. Alright. Next in AI news. Well, Elon Musk has confirmed why he diverted so many NVIDIA GPU chips from Tesla to his other company XAI. So Elon Musk has confirmed that he did redirect thousands of GPU AI chips from Tesla to his companies X Corp and XAI.

Jordan Wilson [00:03:02]:
So the move was made, what he said, due to a lack of space for the chips and potential conflicts 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. Alright. Last but not least, the US 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.

Jordan Wilson [00:03:45]:
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 everydayai.com and sign up for that free daily newsletter. And, you know, always hit me with a reply. I I read them all, so let me know. Alright. But today, we're not here to talk about the AI news.

Jordan Wilson [00:04:17]:
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 Saurabh Jain, who is the executive chair chairman of Trial Key AI. Thank you so much for joining the show.

Saurabh Jain [00:04:37]:
Absolutely. Pleasure. Thank you, Jordan.

Jordan Wilson [00:04:39]:
Alright. So, hey, you you know, for everyone out there, could you tell us a little bit about yourself and what you all do at Trial

Saurabh Jain [00:04:47]:
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 a lot more on the commercialization of, so right now, I'm in about half a dozen company boards, and one of the companies I'm super interested in is our trial key. So the problem that we try to solve is, what we did 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 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.

Saurabh Jain [00:05:32]:
And 3, 3 and a half years later with a whole bunch of investment, we can do that now with about 90 to accuracy. So it sounds super crazy, but we can predict whether a trial is gonna succeed or not before it starts with 90% accuracy.

Jordan Wilson [00:05:45]:
Yeah. And and maybe before we dive into that a little bit and talk about, you know, things like unstructured data and improving, you know, 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 and how have they historically worked in years or decades past?

Saurabh Jain [00:06:15]:
Yeah. Totally. So just some of the real foundational things around clinical trials. So you go back about a 100 years ago or a couple of years ago, anyone can claim anything about anybody can even pop it on a bottle. And some stuff works, some stuff didn't. Some people died and the government thought, hey, this was actually totally regular like 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, then you'll have first in human trials, then you have phase 1.

Saurabh Jain [00:06:45]:
Phase 1 is much more around safety. A, is what we're doing, giving this drug or treatment to somebody causing any harm. Phase 2 is more on efficacy. A, does it actually work? And then phase 3 is efficacy, but at a much, much larger scale. Then sometimes you had phase 4 or you go direct to market. So the idea is this rigorous process would take about a decade for most trials, 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 than not, it works quite well, and it doesn't cause any unknown side effects.

Jordan Wilson [00:07:22]:
That's that's wild to me to think that, you know, sometimes it can take a decade or more. Right? So, you know, what we're saying is in theory, you know, a a pharmaceutical company or a group of scientists, you know, might actually have something that could help today, but it could take 10 10 years. Right?

Saurabh Jain [00:07:41]:
Oh, totally. And and, look, I'm generally quite an optimistic person. So one of the great silver linings around COVID was, it was kind of awesome. I like all of humanity banded together to solve a single problem and it had like a 1,000, labs around the world trying different COVID trials and we kinda got something out to market in a 10th of the time that normally would happen. Sure, it probably took like insane amount of resources and insane 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.

Jordan Wilson [00:08:17]:
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, surgeons of 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 ChatGPT in in 2022. So, you know, how you you know, I'm I'm curious because you do have a background, you know, in this, in this area. In general, I mean, I think some people saw chat gbt and they're like, oh, this is kind of a toy for, you know, writing blog posts or something like that. So how would you say that in your field, generative AI and in large language models were initially perceived?

Saurabh Jain [00:09:04]:
Totally. So, let's just say, like, ChatGPT. I did not think in my lifetime that would be a solved problem. I didn't didn't think that someone would be able to solve the Turing 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 in patient. Back in the day, it's Java 2.0, you had to hand roll everything 8,000 lines of code. And you could build a model that was about 50% accurate because we only had structured data.

Saurabh Jain [00:09:33]:
So it's only structured data in fields that a clinician actually broke. It's like an Excel spreadsheet like this field means this, this field means that. And the really cool thing that ChatGPT did it let us go from unstructured data to structured data. So for example, you upload a clinical trial to ChatGPT and you ask it what what compound was used? And it goes, does its thing and it comes back and said, great. This is the compound that was used. You'd ask how many patients were in this trial? Where were this? Where were the sites? Where were the location? 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 into a decision engine.

Jordan Wilson [00:10:12]:
And, you know, what were some of the early concerns around using large language models as well? You know, I you know, obviously, there's things that we all deal with. Right? Like hallucinations and, you know, is is this model actually, you know, using the data that that that we give it, you know, context windows, etcetera. 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?

Saurabh Jain [00:10:43]:
Couple of things. One is all around the hallucination or having blanks, having incomplete datasets. That you don't that's kinda hard to solve on the LLM side. You can kind of hyper tune them and make them more focused, but you're always gonna have a bit of that. So what you wanna do is you wanna solve that on the actual precision engine side, the the predictive 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 dataset which, you know, the world has never seen before? And then we did a whole bunch of work and I'll kind of take some of the interesting stuff that we found out around that. But then they got all the things, like, how do you deal with scenarios that have not happened before? And then how do you deal with the kind of incorrect data coming through?

Jordan Wilson [00:11:23]:
And and 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?

Saurabh Jain [00:11:34]:
Yeah. Totally. I mean, keep in mind that probably no one that we know is kinda doing, AI clinical trial prediction guide to the to the extent that we are. So it's still very new to the industry. Like, we only launched the market like 2 or 3 months ago. So it's super early days. We only got our 1st set of customers using this. But generally, what you what you'll find, it's a completely different approach.

Saurabh Jain [00:11:56]:
So the way traditionally works is like all of these things are based on like a clinician with years worth of experience about in a certain drug and certain disease. Now the downside of that is, no single clinician can have, like, a complete knowledge set. But no one can actually know everything that's happened in every clinical trial over 350,000 trials over 20 years, but an AI tech computer totally can. The AI will find connections that a peep 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.

Saurabh Jain [00:12:33]:
But when you bring up their trial and talk to their experience and some of the stuff the data shows, that's kind of when you get them on-site very, very quickly. Yeah.

Jordan Wilson [00:12:41]:
And, you know, speaking 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, 35,000 clinical trial. Right. Right. So all of these or or sorry, 350,000, clinical trials, all these different variables and, you know, improving the accuracy of 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 you know, research organizations, how can this data and 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?

Saurabh Jain [00:13:25]:
Tell me. Well, I mean, probably the the even the thesis that we have is that everything about how humans interact with drugs and devices is already known. But that knowledge already exists in the world, except it just happens to be in a crappy kind of way across 22,000 clinical trials. We've just pulled 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 what I've been claiming to fame now is we can predict whether a trial succeeds with about 90% accuracy before you start. Super crazy, if 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 3 main use cases.

Saurabh Jain [00:14:05]:
One use case is that we'll probably just make free because it's going good for the world is for patients. If you have a loved one who needs who wants to go on a clinical trial, we'll help figure out which trial to get on, which is most likely gonna succeed, so then they have the best chance. If you're a pharmaceutical company, and you've got a 100 trials across different drugs, we'll help you figure out which trials 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 generative 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 out to fund the, osteoarthritis company.

Saurabh Jain [00:14:50]:
We did a model for them is to predict their likelihood of success of completing that trial. So when they got off to invest it, they can say, great, we've got an expedited chance of exceeding this much more than our competitors 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. They you buy before a company announces a clinical trial inflection, share price goes up, and you sell afterwards.

Jordan Wilson [00:15:16]:
Yeah. And, you know, something something I'm curious about when we talked about this, you know, briefly before the show, you know, this this can obviously help companies save a lot of money, number 1. Yes. It does bring drugs and treatments faster to the market, which 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 are there going to be maybe now, you you know, drugs or, types of diseases that maybe just go undiscovered or unresearched mainly because, you know, hey. We are able to now make more money or we're able to just focus on, you know, these these bigger problems that have a 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?

Saurabh Jain [00:16:15]:
Totally. So, what Tic Like Ours does that makes it like like any perfect capital allocation in the market. When money goes if I'm very choppy, right, if you're most likely gonna succeed, you'll invariably get more invested and more funding. And most pharmaceutical 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 am I 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 in different ways. Some people might have surgery, some might have devices, some might have drugs, some might have the other interventions. And who's most likely gonna succeed and when do you think they're gonna succeed? Because the theory is you wanna get out to market with the most, if you have an invasive intervention model or ingestion method, I.

Saurabh Jain [00:17:03]:
E. Have to take a needle versus the tablet versus the cream. You're going to find it's gonna 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 gonna succeed after these guys, they're more likely to succeed than I am. So I'd like you to kind of choose what is this the best use of my capital. What that means for rare diseases? So I'm gonna use COVID as an example. So this is so this company started about a year before COVID, and there were about 400 vaccine candidates at the time. And we predicted Pfizer and Moderna is number 1 and 2 and AstraZeneca is number 7.

Saurabh Jain [00:17:42]:
And history is showing we push that out. We've 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 we were really known back then, but those 2, the 3 succeeded. Now they've been over index in terms of funding. The Madonna is investing a hell of a lot in mRNA, and they've been up 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 easy to solve, that has the biggest payback versus its competitors. So you'll have things like diabetes, weight loss, cardiovascular cancer will be over index in terms of probably investment.

Saurabh Jain [00:18:24]:
And some of the rare genetic disorders will probably just get get less, get less attention.

Jordan Wilson [00:18:30]:
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, you know, 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 models can actually take out some of that guesswork, I see how it's a good thing. But then how can you still, you 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?

Saurabh Jain [00:19:26]:
Yeah. So what you'll probably find is that clinical trials with tech like us will iterate a lot quicker. So as you said, it takes right now a decade, right, to go from start to finish. What you'll find is it might still take that long, much a few years off, but you'll be doing less trials. Right now, Humanae does about 35,000 trials a year. We've got a couple of job to 5 or 10000 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 or some way that sends people to go solve some of the young problems, that will probably still happen. Because luckily not everyone does this for pure pure financial motive, people have, personal experience and those kind of things.

Saurabh Jain [00:20:04]:
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 in an AI simulator. They'll be like, awesome. I think we're gonna solve this problem with 72% chance of success. Baseline is 10%.

Jordan Wilson [00:20:20]:
You know, one thing, you you know, 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, you know, when looking at all of that data? You know, because I'm sure that there's kinda 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, in your process of creating, this model for others to use?

Saurabh Jain [00:21:06]:
Let me let me ask you a question, and it's a trick question. What do you think is more important in a phase 3 trial? The actual drug or the way the trial is succeeded? So the the way the trial was designed, what do you think is more important on whether whether a drug works and gets their approval or not?

Jordan Wilson [00:21:22]:
I'm gonna well, trick question. Love to Trick question. Yeah. So I I'm gonna guess the second. I'm gonna say how it's used because regardless of the drug, if it's not used correctly, it's maybe not relevant. That's that's my guess. What's the That

Saurabh Jain [00:21:35]:
is that is totally it. Right? Look, it makes no sense. Like, 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. Alright. So how the trial was designed.

Saurabh Jain [00:21:51]:
The drug only accounts to about 20% of the likelihood of success. Because keep in mind, in phase 2, you, you kind of crave some level of efficacy. That's how you kinda get to phase 3. It's it's it's not the main dish. And that's totally crazy. It's only 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 awesome drugs. Drugs that actually were really good, were gonna be super ethical, have a lot of efficacy, but they've not never got taken to market because they had a crappy trial design.

Saurabh Jain [00:22:24]:
Is the principal investigator, the doctor, and chief medical officer didn't design a great great trial or what they're not enough funding or couldn't do what they wanted to do, and they never got some amazing drug to market.

Jordan Wilson [00:22:38]:
You know, so one thing one thing I'm I'm curious about is, you know, I I kind of think right now, people who are doing knowledge work. Right? So they're they're reading, you know, PDFs and they're creating outlines and pitches and presentations. To me, it seems kinda 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, you know, digital twin simulations, like, how do you kind of see the future of this playing out?

Saurabh Jain [00:23:18]:
Yeah. Totally. Look, as I said, there's about 35,000 trials that happened in the world. I reckon that's gonna be dropped to, like, 5,000, and there'll be millions and millions of trials that will happen in a in an AI simulator. And at some point in time, we're gonna 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. I learned to drive about 20, 25 years ago. It was super dangerous.

Saurabh Jain [00:23:45]:
I'm so lucky that I never killed anybody. But everybody who is 17 who learns to drive, like, are 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 a simulator, but I have to pass the test in a 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 will happen for clinical trials at some point. It would be like, that's crazy. Right? In in the dark ages, we actually tested drugs in people before in the AI world.

Jordan Wilson [00:24:15]:
Yeah. It's it's it's kind of 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's 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 complete 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 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?

Saurabh Jain [00:25:06]:
Totally. Look. I mean, I think so you always still do a trial in a person because, like, you can't simulate everything in the human body in AI. Humans are so different, so unique. But I think what will happen is before it's done in a person, it will be done in AI. And that will call out a lot of trials that don't should 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 actually shouldn't shouldn't take or or drugs that are probably not gonna succeed.

Saurabh Jain [00:25:41]:
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 acne, you can actually run that through an AR stimulator, 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.

Jordan Wilson [00:26:15]:
That's that's that's, you know, super super interesting. And 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 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 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.

Saurabh Jain [00:26:58]:
Yeah. Totally. Look. Unfortunately, it still takes a decade, like, 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 then 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, a few months here, a few months there.

Saurabh Jain [00:27:35]:
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 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 running a 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 have started to accept some real world evidence, trials. They they don't have the same kind of bar as a clinical trial, but it's a great way to start for, you know, for, some alternatives, vitamins, and some of those kind of things.

Jordan Wilson [00:28:23]:
So that's yeah. It's it's interesting. Right? And also a little disheartening maybe. Right? That that, you know, all of these, you know, technologies are are helping this field advance, but, you know, still, the the slow turning wheel of, you know, a a big organization like an FDA can still slow down, things things for everyone else. So, you know, we've we've 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 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 will hope that people, can keep in mind as they look at the future of how AI is used in clinical trials?

Saurabh Jain [00:29:10]:
Yeah. So, if I take a step back, not just in clinical trials, but, like, you know, any kind of similar problems set. So what we've actually done, and this is what the cofounder's genius was. He's he's found a world clinical trials in this example where there's a 1,000 variables or 7 100 variables in our case. You have a known outcome, and you wanna figure out what variable leads that, known outcome. So we just happen to apply that kind of to clinical trial. And, basically, anywhere where you've got a whole bunch of variables that lead to an outcome, you can now, for the first time ever using AI, actually, about what variables lead to that outcome. And I think that generic solution set will now solve a lot a lot more problems around the world, I think.

Jordan Wilson [00:29:52]:
I love love to see it. You know, I another great example today on how generative AI is really shaping our future, in a good way. So, this this was a great conversation. So, thank you so much, Shirob, for joining the Everyday AI Show. We really appreciate your time. Alright. 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.

Jordan Wilson [00:30:22]:
We'll be recapping 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.

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