Next Unicorns: Bringing AI-powered solutions to medical analysis with PathAI CEO Andy Beck | E1780

0:00It's just super exciting the way this whole field will be transformed. as you mentioned, it's kind of like two big platform shifts, essentially at once, because most of it is not yet, like you mentioned, microscopes aren't even connected. So just the basic advances of digitization, creating an inexhaustible digital resource that you continue learning from without destroying any tissue over time, connecting all of these, you know, brains together across the world is just a huge platform advance in itself, the advantages of cloud and digital being connected. And then at the same time, you know, as that's being transformed, we're getting the substrate for training all these new models that can do things at lower cost, more accurate, more reproducible and more predictive.

0:42So like, this is like, you know, where things are going. And I agree with you within five to 10 years, you know, the world of, of pathology and diagnostics for drug development, as well as for clinical care, I think we'll be totally transformed by this. This Week in Startups is brought to you by Eight Sleep. Good sleep is the ultimate game changer. Now you can add the pod cover to any mattress. Go to eightsleep.com slash twist to check out the pod cover and get$150 off at checkout. Carta now lets you launch and administer SPVs for your syndicate. Share your knowledge, capital, and network to launch your syndicate SPVs through Carta.

1:22Get 10 % off your first SPV at carta.com with promo code TWIST. And LinkedIn Jobs, a business is only as strong as its people, and every hire matters. Post your first job for free at linkedin.com slash unicorn. All right, everybody, welcome back to This Week in Startups. I'm really excited for our next presenter here, our next founder. His name is Andy Beck. He's a CEO and co-founder of Path.ai. and path ai is a research platform it's designed to improve the speed and accuracy of cancer diagnosis and treatment every time we have a conversation about ai the past decade the incredible example people like to give is doctors during cancer or identifying cancers early or looking at scans and getting better at it so we thought we'd find that person after a bunch of research we knew somebody was working on it the name of that company is path ai and we're featuring them today on our next unicorn series andy welcome to the program thank you great to be here you heard my little preamble there about somebody has got to be working on this because they always give the example of using ai to help with cancer diagnosis cancer is i don't know if it's the leading source of death here in the united states i know it's up there with heart failure and obesity right those are the top three i'm not sure where it stands today but how are you using ai to help with diagnosis and treatment yeah no it's a great question and definitely part of the reason we sort of formed this company about uh seven years ago was you know really thinking about what are the killer applications of AI and the big advance back then was deep learning as it still is, you know, that could really make a difference for people.

3:16And really the first big area of deep learning where it made a huge impact was the interpretation of images, kind of imagery, deep convolutional neural nets really created this incredible advance in the field. And then images play a big role in medicine in a couple different areas. So if you picture your patient, you go into the doctor you might have a cough and have some risk factors so they might say you know i don't know what's causing this cough it's either something totally uh that's going to get better on its own or it could be a malignancy that you know you want to deal very aggressively with to cure the patient so the next step would be go to radiology and they'll take images and in the bottom of the radiologist report they'll say you know impression i'm concerned this is something bad like cancer or impression this looks totally benign let's just track it but if they really want to get the definitive diagnosis to guide definitive therapeutic decisions after what's going to kind of be considered the ground truth for what the diagnosis of that patient is they actually stick in a needle to take a piece of tissue and they send that tissue to pathology and then the way pathologists examine the tissue is a process where it's embedded in a wax block a thin five micron section of tissue is cut off the block, it's put on a glass slide, and that's put under the microscope.

4:34And the definitive diagnosis of cancer really is defined by bad malignant cancer cells invading into normal cells. And that really is an image based diagnosis. So no matter what's happening at the gene, you know, mutation level, or the RN expression level, or even the protein expression level, it's kind of like cancer is defined by bad cells invading into good cells. And you need to actually take a picture of that or look at an image of that through a microscope. So those are the types of images that we analyze at PathAI. So the input to our system are what are called whole site images, very, very large pictures of tissue biopsies that have been stained, stained in such a way that we can see the cancer cells, see the normal cells.

5:13And then the AI's job is to look through these very large images with hundreds of thousands to millions of cells, identify what every cell is, identify all the normal cells, all malignant cells to really accurately and reproducibly diagnose diseases like cancer and in certain cases if patients have cancer to try to make predictions on how aggressive the disease is and which treatments it may respond best to so cancer is a big majority of what we work on although we also work in other diseases that involve tissue biopsies like liver diseases as well as inflammatory bowel disease so when you get these images um they're giant images They're created by what type of machines are best for doing cancer scanning.

5:57Yeah. So for these images, so I would just say, just so you know, kind of where the field is largely in the diagnostic side, many labs are still using microscopes to generate images and they're not captured digitally. So someone is just sitting, they've got on one side of them, a big stack of glass slides, and then they're just sitting at their desk and looking at each slide into the microscope. And then, you know, interpreting this is what I think I see in the image, putting that in the report, and that's it. but increasingly labs are becoming digital and to answer your question there's whole site imaging systems that essentially are like microscopes in an imaging solution where they can create these microscopic images capture them and then you know the digital file can then be sent to the cloud and analyzed um got it with image processing this is not we're talking about the biopsies where they actually take a little piece of uh material from your let's say lungs or liver i guess uh for cancer and then they analyze it put it on that swab or whatever those typically have all been 100 manual and those aren't even online microscopes wow that's crazy to think in 2023 that microscopes are not digital and hooked up to a cloud server somewhere what percentage of this is done offline versus online today um it depends by country but i think with the u.s yeah yeah so i think overall about 90 today is done offline and about 10 oh my god this is not even with algorithms this is just digital plain old digital wow that is extraordinary when you think about it's almost like we're gonna look at this in five years and think we were in the stone ages just like having to go send your when you get an x-ray like going to the dentist remember in the old days they had to send them out you know like when's the last time they send out an x-ray they're all done right on the spot with digital imaging now so how many samples are in the cloud in and is there like a national repository of lung cancer and you could look at it and say i have a smoker who's 50 years old reports smoking since the age of 15 so the 35 year smoking history two packs a day and their norwegian and irish descent and their bmi is 30 here they are do we have that kind of fidelity yet in any kind of database like that on a national or international basis so we can actually start to do some analysis of cancer across some reasonable number of people not much i mean i would say there are different initiatives to try to do that but it's such a massive problem i think we're only at the very very beginning of putting those sorts of resources together, something we very much invested in building ourselves.

8:32So I think in terms of another sort of the AI theme, like, what are some of these areas that are not yet on the internet, and not yet captured digitally, and not aren't being incorporated into some of these massive models that are being built off public data, because this is an area where there's very little digital data to begin with, relatively to the scale of the problem. And then, you know, a very small part of that that's in the public domain. So there is a need for companies to do a lot of this work themselves and to work with great partners who are making investments in digital and you know 10 of a very large healthcare system is still a pretty big percentage so you know we're able to partner and and generate data ourselves to create these very very large sets of data the good thing about pathology though is each image is so data rich these are massive images that contain on the order of hundreds of thousands to millions of cells per image so even with you know, data sets that aren't super massive, we can use deep learning and obtain annotations from expert pathologists to train models that work extremely well.

9:36So we've invested heavily in not just the whole site images, but also creating a community of almost 500 pathologists across the country who are tied into our platform for helping to train these models, which are now trained on the order of tens of millions of annotations provided by both the images, but also by the expert pathologists. If you want to get ahead in your career, you need to sleep well. And if you want to be a stallion, if you want to be a workhorse, you need to get a great night's sleep. It's that easy. So do what I do. And that's getting an eight sleep, right? Especially over the summer, right?

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11:12Stay cool this summer with Eight Sleep, please. And they're now shipping, not only in the US, but Canada and the UK and some other countries in the EU and Australia. So eightsleep.com slash twist for$150 off the pod cover. Can't go wrong. I would suppose one of the solutions here would be to give your 500 people participating. i'm curious if you would give 10 of them the ability to mark up the same patients or if you do this the same patients slides as it were and then see if you can find some sort of trend so 10 doctors because this is what we all want as patients if i had some loved one god forbid with lung cancer or my dad has prostate cancer my mom had um breast cancer both survivors which is extraordinary when you think about it um 30 years and 15 years for each of them uh 45 years combined wild um but imagine i could have had 10 folks look at my dad's samples and then give their opinions and i could say wow five are saying this and three are saying this and two are saying this is the when you come as a technologist and listen you're i believe a phd yourself on this and you talk to medical professionals do they have a god complex or do not want to be one of technology company second guessing it in their minds obviously you're not trying to second guess it you're trying to save people's lives whereas the spirit and culture of this hey we should get better at our jobs what's the resistance like in this field you know it's a good question and um and i kind of had a bit of both i trained first as a pathologist and then did uh did the machine learning after so at least i've worked very closely with pathologists uh for a long time now And I think pathologists truly come to work every day wanting what's best for patients.

13:07And the one thing that would keep a pathologist up at night is giving the wrong diagnosis and not having the best diagnosis for, you know, their own parents or your parents. I mean, that is what why everyone comes to work every day. So I don't think that's the big barrier. I think it's more technology hasn't existed for that long to really move all these images around to have this network of pathologists connected through technology through a platform is very, very new. of course the deep learning and image analysis is very very new and in and um just the overall incentives for making that transformation happen in the healthcare system is also a challenge so i don't think it's individual pathologists who are our barriers to this yeah i think it's you know really you know getting the technology out getting in the hands of users and and i think uh you know i think the future you outline we could do that today even without algorithms um and we do do this in clinical trials in terms of having consensus of experts who support a diagnosis.

14:11And that really has been unlocked by this technology platform, because literally before people were doing this, and even still today, people are sending around glass slides. So, imagine how long it would take to send around glass slides to 10 people, you know, versus distributing the images. Yeah, I mean, it basically kills it immediately, because it's just the cost and getting them to town. And then also, your 10 experts might be in 10 different cities in three different countries so there's a real opportunity here i think i believe that if people could you could upsell people on this as a like there are a lot of affluent people uh uber started with black cars not uber x like i mean there are rich people would be like i can get three more opinions for ten thousand dollars each i'll do it right so there is a there could i mean as perverse as it is when we talk about health care we can talk about the profit incentive here there is a profit incentive here people go for second opinions but the people go for second opinions correct me if i'm wrong tend to be rich people um who are paying for their own health care and don't care as opposed to poor people who have to stay in network etc so maybe you could explain uh or maybe we should do the demo first but then i want you to explain economics to me of how the industry works let's do a demo sure because i think that's the most interesting part of this and just looking at my notes here 2000 2021 heart disease 700 000 deaths cancer 600 000 covid 415 000 and who knows how many of those were with covid or from COVID, obviously.

15:35Disclaimer. That's the United States data. All right. So, and here's the demo from, you know, you just mentioned the cancer statistics, the cancer with the highest responsible, I believe, for the highest number of deaths each year is still non-small cell lung cancer. Despite all the really major advances that have been made, particularly in the last five years, one area of huge advance has been these new therapies called immuno-oncology therapies that target either the PD-1 or the PD-L1 protein that have really had massive advances in increasing five-year survival for patients with lung cancer.

16:11So, one of the most important factors for deciding which immunotherapy regimen a patient should get is their protein expression of PD-L1. So, this first algorithm that I'm going to show in this demo is AIM PD-L1, AI-based measurement of PD-L1 in non-small cell lung cancer. And this is actually a demo of our AI site platform. So here you can see sort of the opening page with a case list in the dashboard. So if we pick one of these cases that's awaiting review, we start out with shown on the right here in this display is a whole slide image in the center here of a a resection of a portion of a patient's lung tumor here in the middle as well as we have a sort of guide for where you're looking at on the top right and for people who are watching i look like i'm in my gmail inbox to a certain extent standard navigation on the left and the top and then the main body in the uh you know top right uh and the main body of where your email would be and we're looking at a sample of somebody's lung i guess lung tissue yep lung tissue is including lung cancer in it and this would be for a patient who's already been diagnosed with lung cancer and the pathologist's job now is to look through this sample which if you just look on the top right where you can actually see where i'm looking like your heads up display in a video game that's like the map view yeah the map view so you can see we're already looking at dozens of cells and And this little red dot here indicates how much of the sample we're looking at.

17:49Wow. So we've zoomed in. This is equivalent. It looks like Australia, the sample. If you imagine if you're listening, like Australia is a map and then a little dot that would be Sydney. It looks like there's a little city. And then when you look at it, you're seeing a bunch of little blue dots on an orange and white background. And then there's clusters of darkness. I'm assuming that's not good, the darkness. But I'm taking a guess. So the pathologist's job here, yeah, is to estimate what proportion, so you outline these little blue dots. So there's a few different types of little blue dots. Most of these are cancer cells.

18:23Some of these are immune cells in the middle. And the pathologist's job is to estimate what proportion of the cancer cells are brown, are expressing this brown protein. And literally, there's a few clinically important cutoffs, but they include 1 % and 50%. so just so you know how difficult this manual task is oh my god is like really the job is to enumerate how many positive cells there are how many negative cells there are and then what's the overall percentage eyeballing and counting with a pen on their screen or something yeah you're kidding me this is the state of medicine today with computer visualization where it is this is something that ai could just immediately give you a heat map of this would be like um using the topography view to plan a drive in google maps or something and then having to like look at each house to find the address on the front of it using google street view it's barbaric it's extremely difficult um whereas our system has been trained now on on millions of annotations from hundreds of thousands of slides now can be forced to exhaustively classify every single cell based on is it a cancer cell is it an immune cell is it positive is it negative and does it come from the cancer tissue here outlined in red or from the supporting stroma here outlined in this kind of yellowish color and then we can get an exact count of what's going on so you can just see how this would be an impossible task to do manually like here we're still just looking at this tiny box versus you know we can actually have the machine learning system do this for the whole image do it behind the scenes when the pathologist sits down at their desk unbelievable they basically uh can say is this slide acceptable sure um you know what do i think the results are you know this is okay 1.5 cancer cells are positive which is really important because it's more than one percent but it's less than 50 percent um and then you can actually see the supporting results in terms of you know looking inside the black box of well how did it come to that number there's almost 12 positive immune cells um there's about 80 almost 82 000 cancer cells almost uh you know 380 000 immune cells and then you get this exact proportion of positive cancer cells positive immune cells you know you can spot check a few of the areas and then um you know accept the score so this is you know our goal is for all of our algorithms to make pathologists more accurate more reproducible as well as more efficient so there's this um quote if you measure it you can manage it you know like if you actually know your weight and you weigh yourself every day you know you might be able to manage it a little better or if you're steph curry hitting threes you can look at the arcs now there's a three-point system that basketball players use the knicks famously used it three or four years ago and they everybody on the team got better at shooting threes because they were looking at their arc and their stance and yada yada all the best practices there of measuring stuff so cancer diagnosis and then other treatments that come after it we are in the measure it so you can manage it phase of this solution and and that is in and of itself extraordinary to me as a neophyte that we're getting off of essentially people doing this manually on pen and paper and writing down on a legal pad and and typing this stuff out it's just crazy uh but if we can measure it and then you put a um if you have this level of fidelity when you are measuring stuff well then if you do another uh is it called a biopsy when you take the sample it's like the proper term we do another biopsy and you do biopsies every year i don't know how invasive they are but if you did multiple biopsies over multiple years for somebody with lung cancer or multiple months would you not be able to then measure the efficacy much better treatments absolutely 100 and even if you just do a pre-treatment and post-treatment so for many serious cancers as well as another i can talk about liver disease there's a biopsy that makes the diagnosis and then you can actually give treatment like if you're diagnosed with early stage lung cancer a real option now is to get a biopsy to get a treatment and then to analyze the resection specimen.

22:45So, in a single patient, you can analyze what's changed from the biopsy to the resection and what does that mean about how effective this treatment is going to be for curing this patient of their disease. And should we stick with the treatment we used in the so-called neoadjuvant setting, the prior to surgery setting, or should we switch to a new treatment? And you can't really inform that analysis with manual scoring in the way that you can with AI Because now we can measure hundreds of important quantitative features of the image before treatment and after treatment in a highly reproducible, scalable way that would just be impossible to do manually.

23:18So absolutely seeing the effect of treatment. The other disease is NASH, where actually the primary endpoint, non-alcoholic steatohepatitis. It's a very fatty liver disease. It's increasing in prevalence significantly. and the way that patients are either enrolled in clinical trials or we assess whether treatments have worked is a pathologist manually scoring things like inflammation amount of fat amount of ballooning hepatocytes amount of fibrosis and there's a lot of noise in those manual measurements and then you're trying to say did it get better or worse after treatment well if there's a lot of noise in the measurement you don't know if it changed due to the treatment or due to the noise that increases noise in the placebo group as well and it makes it harder to assess whether new drugs are effective whereas applying a system like this you know that you know at least the interpretation was highly reproducible between prior and post-treatment so we think this will be very important for approving new medicines for this so increasingly common disease these cancer cells these these are pdl1 you call them this example i showed yeah these were this was a case of lung cancer and the way they the way we subtype lung cancer is based on how much is it expressing this protein called pdl1 yes a lung cancer patient could be pdl1 negative which would have certain implications for what treatment they should get they could be sort of positive at the one to fifty percent level or they could have greater than fifty percent of their cells expressing pdl1 and there's different treatment recommendations for patients based on their level of expression of this protein got it and the pdl1 protein kind of obscurifies the cancers there it kind of hides it is that the yeah it does yeah so pdl1 um it's it's in the pathway that's targeted by major drugs like keytruda um or optivo which are two of the the major relatively new cancer drugs from the past few years uncloak it i guess they kind of make it appear i know just enough about this to be dangerous yeah exactly so uh But PD-L1 sort of inhibits the immune response to the tumor, which is kind of the body's way of trying to defeat the cancer.

25:25And if you give these drugs, which are antibodies against PD-1 or PD-L1, they inhibit the activity of either PD-1 or PD-L1. And that enables the immune system of the patient to really be unleashed and then to really work hard in anti-cancer immunity and has led to significant improvements in survival for patients and cure rates for patients with serious cancers like lung cancer. It's like a shock grenade or something. You shock the cancer cells, they stop growing, and then the immune system can do its thing and kill the cancer cells or get them out of there. Yeah. Listen, if you're in the tech industry, you've heard of CARTA, right?

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27:22that's carta.com and use the code twist this is extraordinary in your estimation now um working on this a lot of us like to say oh yeah cancer is going to be solved in our lifetime there's a lot of reasons treatments measurements early uh detection do you think it's 20 30 years before most cancer because most cancers are treatable now that was something crazy to think of in 1980 in 1980 or before 1980 you got cancer you basically started your goodbyes and did your trust your will whatever now here we are in 2020 you get cancer they say okay here's here's your options here's the plans and it's very rare that somebody gets a cancer diagnosis and they say hey you know now it's starting to start your will and say goodbyes it's that advanced so amazing what progress we made in what 50 years i would say 40 years maybe 40 um you tell me um what is the next 10 years having stories is it possible the idea of dying from lung cancer and brain cancer will become very obscure like dying in a you know plane crash kind of situation um i think it will become more and more manageable uh over time there's going to be more early detection there's going to be fewer cancer cases as uh as we treat more risk factors effectively for developing cancer smoking being an example for lung cancer that's quite important i don't think it's going to be as rare as a plane crash i mean i think there's something about how fundamental making errors during cellular replication are and how important cellular replication is as you keep living life and dealing with all of these insults we're constantly getting on a daily basis from the environment including sunlight and things we're ingesting down our gi eye track and things we're breathing in and hitting our lungs so cells get injured cells need to proliferate and recover errors are made because you're trying to copy this enormous genome and you're going to make mistakes the machinery is going to start aging for finding those mistakes and cancers will develop and the question is going to be so so it's like it's way more fundamental than plane flight which you don't do that often and i don't know there's probably technical ways to get around plane flights i think cancer is sort of fundamentally tied to being alive for a long time however i think we can you know make the rates of it go down start to prevent it start to identify it early and manage it we're already making headway there and and there's some really bad cancers that we have made less headway on that we need to to do even more so i think we're still at the very beginning and i do think it will become much more manageable over time with these advances it's pretty amazing i literally just um was at my doctor's and she said oh you know there's this you like all kinds of new stuff there's a new test you can take a blood draw and i will tell you if you have cancer and i'm like do we expect i have cancer like no it's just it's a blood test that tells you if you have cancer at this moment not predicting the future of it and um so i did it uh and i get my results back at some point i mean i don't i'm 52 two i i don't know if i just and i just did the pre novo body scan nothing came up on that this is the stuff that could really i think maybe you could speak to body scans and i don't know the impact of these general tests like a blood test like i mean at every age if everybody did a blood test yearly and a body scan yearly what would cancer diagnoses look like in that world yeah so i mean i think there's a few things one is just the the things we already know about that aren't going to change that are almost like universally good for many things including reducing cancer like as much as we can preventing obesity encouraging you know exercise even i feel like the exercise cancer link is potentially not huge but i think there's just general like healthfulness stuff that doesn't cost money and has no adverse side effects and isn't going to result in a wild goose chase that you found something you don't know what it is and you get unnecessary surgery unnecessary biopsies so to me it's like there's all this really basic essentially not technology stuff that uh can even vegetables and yeah exercise vegetables taking a huge huge impact so i'd say that's one just whole big area that we should not forget about because i think it could just have a massive impact in terms of screening screening historically has always been tougher to prove the benefits and patient outcomes than we would hope so i just think there's a lot of excitement about screening, but we're still kind of waiting to see a lot of the data about improvements in overall survival from a screening alone.

32:04And we do see it in some areas, but even that the impact size is relatively small. And the reason to think about it is you need to be finding things that would have otherwise, you know, killed you, but you're finding them in an early enough stage where they're curable. And those things do exist, but they're not as large a proportion as we might like. So the positive and why I am in favor and think we're not doing enough of this is you do find those things early. The downside is, let's say you, you're only 52, you had a healthy scan. So your pre-test odds of having a cancer that's likely to kill you might be extremely low and something shows up in your blood.

32:41And then you are in this kind of wild goose chase where you're getting more and more imaging, more and more biopsies, a lot of anxiety. and if nothing showed up on imaging yeah i don't i don't really know what the next step would be for you so i don't know i think it's complicated i love it i gotta say uh i know that i'm going to die i'm convinced it's going to happen so since i've already accepted that i'm going to die if i could get even if even if they told me there's something here we're not sure what it was i would rather because i guess i'm not like a prone to anxiety or like catastrophizing i would rather go on the goose chase um than not i'm okay with a little goose chasing you know um but i think you're thinking holistically about the entirety of the population and like this impact it could have yeah yeah i think i took gallery g-a-l-l-e-r-i was a test i took um and prenova was the body scan i did and then i don't did you see that daniel ek the spotify founder um has invested in a scanning company that only costs 400 bucks or something.

33:43Or he thinks he can get it to 400 bucks, which pretty interesting in and of itself. All right, listen, you're listening to the next unicorns, right? That's table stakes. Well, if you want to start on the path to becoming a unicorn yourself, you're gonna need to find and hire great candidates. And how do you do that? LinkedIn, of course, you're on LinkedIn, I'm on LinkedIn, over 900 million users. Now the march to a billion continues for the amazing team over at LinkedIn, then you can attract both the passive and the active job seekers. And all you have to do is put that purple hiring ring on your profile.

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34:44So we found somebody amazing on LinkedIn, of course. And so here's your call to action. LinkedIn jobs helps you find qualified candidates you want to talk to fast, post your job for free at linkedin.com slash unicorn. That's right. LinkedIn.com slash unicorn to post your first job for free terms of conditions apply, because LinkedIn is so generous. maybe you could talk to me about what you think all of this data is going to do and if there's anything with general ai that's starting to happen that inspires you because pools of data are starting to emerge um i don't know if you have an apple watch on but uh i used fitbit for 10 years apple watch for five i would happily give every bit and i have eight sleep for my bed i would give every piece of data i have to the cloud including every blood test i've ever taken and i would love to be part of a national project to get all this data together and i think there are some going on but i don't think you need that many people either i don't know what the statistical number would be 500 people a thousand people doing this in order for y 'all to have incredible data why is there not some national project this why is it also piecemeal i guess is what i'm getting at i talk to smart people like you you have all the solutions you need only implement them why isn't there like a national effort like we did with the manhattan project to just understand the human body in relation to the new tools that are available i.e ai computer visualization technology and make the proper data set for y 'all to to to really have a go at this why is that not being done or is it well i think different groups i think gallery is a great example that was largely funded by industry they've done massive trials incredible data i think and i'm very bullish in general on this technology that like this is the future and it's better than uh certainly what we're currently doing which is much more reactive and and less likely i was just saying every patient like you instead of the anxiety may not be a concern but you have to like very much think about what's the potential risk of the biopsy i might have to get to confirm something versus the risk of having cancer and just compare those risks just that you know every everything is is uh everything has some risk associated so one just has to be uh really cognizant of what those trade-offs are but um so i think the i think industry when they see an opportunity you know is going after this i mean pharma is extremely interested in uh non-invasive tests there's large companies um like illumina with grail who funded these major trials around gallery that i think are going to provide very good evidence um around that test um and i think it's been yeah largely you know many of these efforts large-scale efforts have been private there have been some great uh federally funded efforts like tcga the cancer genome atlas um so i think it's a whole slew of different problems and you know many of them are being funded through industry or through the government.

37:51And it's not just one problem. One that actually sounds a lot like what you said is another privately funded the project baseline by Verily, which is very much, I think, similar to what you're describing of just capturing lots of data from essentially healthy people and following them over time to be able to use machine learning over time. So I think there is a ton of activity in this area. It's and there's many different problems. And for some problems, it makes sense to put it all together. But for many, it sort of makes sense to try to solve that problem uh whether it's through industry or through uh government funded efforts it's really fascinating when you think about ecosystems you know or just systems theory in general which system will provide the most uh innovation and solutions the capitalistic system system where people are like you know what so many people die of cancer and or these are the top three reasons people die obesity cancer obesity cancer related obviously uh to some extent we just got to get a zempic and we'll go be going in the country and the free-for-all of market-based capitalism is going to cure obesity right there's enough people who want to lose weight that 800 bucks a month for we govi or osampic people are happy to pay 10 000 a year for it out of their pocket to not be fat right i did it and it worked um so it's fascinating that system versus a top-down system where maybe europe is doing that brain project i guess they're trying to study the brain or some of these larger projects um and then which system ultimately um or maybe it's just many different systems running yeah i mean and the irony too is often these top down systems sort of break into these fragmented projects anyway so that's why i think um you know where there's three billion dollars but it's being distributed among i don't know thousands of labs um so yeah so i what do i think works um i think Like, luckily for us, we've got a diversity and a really pluralistic set of people attacking this problem from, you know, government funded initiatives, big pharma, biotech, and startups.

39:54So I think, you know, we're all going after it. Certainly, though, in terms of the data piece, you know, single payer systems or, you know, top down approaches for organizing patient data at scale where there's no obvious short term economic incentive for doing it, it makes sense for that to be top down. And I do think we could learn from some other countries that have done an incredible job of that, of having for every patient, they know their ID, they know what treatments they've gotten throughout their life, they know what diseases they've had, and they know their outcome. I 100 % agree that that should not be so fragmented, that should be top down, and it could have a huge impact.

40:29And you mentioned sort of scale and like, what are we seeing? I mean, we're absolutely seeing it on the pathology images. Like once we crossed a certain scale of data, which we've been building now for about seven years, like these models have gotten so good and so generalizable across the full domain, that we started in a very fragmented way, we need this model for this antibody for this tissue indication, whereas now we've built such a big data that we can build these foundation models that generalize extremely well across broad diversity of tasks. And that was only accomplished through really this sort of very centralized vertical effort within pathology where we generated enough data at scale to build that.

41:08So there's definitely like a scale effect where you really do need to invest in this large, harmonized, high quality data. And it's probably in the low thousands of samples, I'm taking a guess, to get to statistical, you know, accuracy? It really depends on the application. Got it. Hard to say. Well, I was just saying, you think about vertical. ai you know programming languages and and you know uh github copilot those kind of things they were the first out of the gate to prove that you know hey these language models are very powerful so yours is a similar situation where it's a a narrow data set with a you know if you get a certain amount of it i mean at some point you'll have more than you need and it's kind of like self-driving i was talking to elon about this and he feels they're really close um he's felt and he's felt that way for a long time um but there is a tipping point where when you got you know a million cars on the road collecting data he told me they have too much data now and that the issue is managing how much data they have and then they only need the data he's talked publicly about this so i'm not speaking out of school here but they only need the data when the autopilot disengages or particularly challenging intersections or edge cases they don't need the data on going up and down the 280 or the five freeway to la san francisco like We got it.

42:27It's a straight line. The lines are painted really well. Stay in your lane, go a certain speed. It's when you're at some weird back road or some funky, you know, going to the tender line, there's people in the middle of the street, right? No, the edge cases. The edge cases become that, but. Totally. Yeah. So we think a couple of things on that. It's interesting. So we probably have in the order of tens of millions of annotations, hundreds of thousands of slides. And a lot of it's the scale and the point in time of the prediction thing you're predicting. so for cells and tissues yes we have a ton of data i think it's similar to the the self-driving car example we can cover the vast majority of things every now and then you know there's an outlier where something's for a clinical use case that's really where you want the human judgment to make sure got it there aren't some catastrophic mistakes but like the vast majority of it's done but what if you're trying to predict things that aren't in the image but you're using the image to predict the molecular under underpinnings of the tumor or even the outcome of the patient so while there's, you know, on the order of hundreds of thousands to millions of cells per slide, there's one treatment and one outcome safe per patient.

43:28So the instances of patients are much fewer. So you actually need a lot more data that we're certainly not there yet to predict what's going to happen at the patient level. And then as you go further and further out in time, you need even more data because you're trying to make predictions further into the future. I imagine self driving is probably similar, like, well, can I predict the risk that this person will get into an accident, not in this minute, but five years from now, you know, and then yes, you're going to data to predict over time so you can look at all miles driven under autopilot and how many disengagements happen per thousand miles you know and i think that number and i don't know what number they use internally but i know like the number of disengagement wow if i'm going from the bay area to tahoe as an example it does not disengage on the highway i disengage it when i get off the highway and i don't use it on surface streets all that often although i've been testing it and it does work really well and i guess in your model it's like the surface streets or like your driveway when you arrive at your location there's like a private driveway or you're you know on some back road and trucking which i am sometimes and it's like yeah a lot of people haven't been down this road that's the example of oh this is like a 25 year old who got god forbid lung cancer it's like and they don't smoke okay we haven't been down this road before how many 25 year olds with lung cancer are we going to find it's not it's not an average condition it's really fascinating um i think we're gonna wrap all this up this decade i know it sounds crazy but i feel like at the advances that are happening especially on the hardware level and then optimizing the hardware and the amount of data that's being poured into those two things which are being relentlessly innovated on by open source people at hugging face and nvidia you know whatever if everybody is doing their best in this crazy capitalistic society we talked about in different systems nvidia has got a profit incentive and you know everybody competing with nvidia is now in an existential crisis for their lives intel for example etc they're all now just solving that hardware level and then all these open source people are trying to build software and products uh to keep their businesses going on hugging face and get hub and everybody's doing their part to be rabid innovative capitalists and then it comes together in this crazy orchestra that you benefit from what's the hardware platform that you use are you hardware constrained like the large language models are or do you just need like three h100s and you're done or just use cloud to you know i'm assuming you just use the cloud we use cloud for deployment and we use we have our own compute center that we also use for training um and i i think we're less hardware constrained than than many of these companies trying to train massive llms and uh yeah and i think to your point i mean we are absolutely benefiting from what's going on in the overall ecosystem um all of these things that are sort of pushing in only one direction um and it's just super exciting the way this whole field will be transformed as you mentioned it's kind of like two big platform shifts essentially at once because most of it is not yet like you mentioned microscopes aren't even connected.

46:35So just the basic advances of digitization, creating an inexhaustible digital resource that you can continue learning from without destroying any tissue over time, connecting all of these, you know, brains together across the world is just a huge platform advance in itself, the advantages of cloud and digital being connected. And then at the same time, you know, as that's being transformed, we're getting the substrate for training all these new models that can do things at lower cost, more accurate, more reproducible, and more predictive so like this is like you know where things are going and i agree with you within five to ten years you know the world of of pathology and diagnostics for drug development as well as for clinical care i think will be totally transformed by this yeah i mean it's mind-blowing really it's like there's something about compounding innovation occurring like you said is very insightful these things are happening in parallel there's some group working on microscopes somewhere i don't know where that is is it germany with carl zeiss lenses or is it china somewhere people are working on the lenses inside of those microscopes somebody's working on the electronics in those microscopes and then you're working on your platform and all this comes together as people buy increasingly powerful smartphones and laptops and play more video games that's the precursor to your company existing is video game addiction and the smartphone i mean if those things hadn't become mass-produced products we would not have you know nvidia or you know the the stack that's been that's been growing wild to think about why investment in technology and iteration is compounding compounding iteration is so worth it and just the impact like even in the u.s we have far too few pathologists but it's significantly worse around the world in terms of access to experts you know diagnosis yeah like somebody in africa or the front let's say frontier markets yeah what did they have they don't even get biopsies i bet yeah well increasingly they they do so in certain cases not i would say the the number of biopsies are going up the number of people with chronic diseases as we make advances against acute infectious diseases increases over time.

48:56But what that comes with is needing to be able to diagnose and manage serious chronic diseases like cancer even better. So many of these very, very large areas have, you know, very, very few pathologists. And, you know, where we are today, where there's like a very small number of experts who aren't at all scalable is not going to address that global problem. Whereas once we've built these very high quality models that are deployable through the cloud, it should really democratize access to this expertise and enable, you know, the best diagnoses to be distributed everywhere. And of course, that needs to be placed within infrastructure for also using that to guide the best therapies.

49:37But we think this is a really key part of that process that will be, you know, unlocked by both digital as well as AI on top becoming more ubiquitous. Listen, I could talk to you for hours and I did. we talked for an hour andy back uh he's andy back on twitter and the website pathai.com they're hiring so go ahead and go to pathai.com if you want to save lives and push the human species to even greater life extension which i think is noble i think people living longer adds more wisdom and people having longer health span i guess as uh peter attila keeps saying is this is all good stuff and so if you want to do something meaningful with your life quit facebook and optimizing to get people to click on ads and invade their privacy and go work on something noble like what andy is working on at pathai.com life is short if you're an elite developer don't go for the quick ad money at some facebook go work on something meaningful it's much more important and you'll see better in that andy thank you for doing this work on behalf of the human race i I think it's awesome that smart people are choosing to do really important work like you're doing into your team.

50:48I thank you on behalf of the human species. Thank you, Jason. Thanks so much for your time. Awesome to get to meet. And I love the podcast. I'm a big fan. So, I'm so honored to be on here. You know, after doing 1 ,800 of these and having people, like, tell me they listened in high school and college and now they're on their second startup, it is the great joy and legacy of my life that so many people have told me they heard this interview or that interview and it inspired them to start a company and then some of those companies go on and change the world it's really important that founders because there's so few of us out there and uh you know it's like less than one percent of the population who are willing to do what we do and it's uh it's filled with suffering and failure and sleepless nights but it moves the human species forward and founders are a unique breed.

51:35No, and I love the connection you make across these different areas, like how consumers playing video games and buying cell phones, you know, helps NVIDIA. And then NVIDIA becomes the core of a lot of what we do in terms of building and deploying models at scale and how it all works together. And definitely your both this and all in have been very inspiring, just thinking about what's possible with building companies today. Like it just gets me really excited about, you know, what we can do more with less going forward. Um, never been a more exciting time to be in a company. And I think probably to start companies because yeah, the world is just so different now.