This Will Change Medicine Forever | Ep 010 Lemonade Stand 🍋

Good news, guys.

You have some good news?

You're invincible now.

AI has fixed all your health.

That's name, name your problems, all of them.

I guess I care too much.

Is that cured?

You'll be a sociopath now.

No, I work too hard.

I just worked too hard.

Cured.

You guys will be uncaring assholes

with optimist robots going around doing all of that.

That's awesome.

That is that's brilliant.

I've been looking forward to this.

Uh, look, I have another question for you guys.

And let's not be some podcast that just fucking just regurgitates the popular thing.

Do you think cancer is good or bad?

I think it's debatable.

I would like us to at least steal mana because today I'm going to make an argument for how AI can really help us in cancer treatment and prevention, but I don't want it to be one-sided and we're just going to have one wing of the spectrum.

Okay.

I think I'm kind of on that like raw milk, let the cancer grow sort of way.

Yeah.

It's like, if it, you know, it's natural.

Have we thought about that?

If the good Lord wanted me to fight my cancer, he wouldn't wouldn't have given it to me.

Okay.

So that's true.

He does give his greatest.

He gives his hardest working, most caring soldiers.

His hardest

biggest cancer.

His most cancerous.

Yeah.

So, okay.

There's a, there's cure.

Well, that's a big, bold claim.

I'm actually going to make the argument that in our lifetimes we'll cure cancer because of AI.

Oh, damn.

Oh,

it's that big, dude.

I'm that deep in the cool.

Okay.

For a week straight, I've just been joking

tech crunch articles, just like catch AI.

Jamming into the Super Bowl over and over again.

I just have Optimus robots in a revolving line dumping me with Gatorade.

Well, before we hop into this, I think first things first, I think we're going to try something a little new this week.

We wanted to try having one larger topic that we dive into a little deeper and see how that goes and then fit in a few smaller things on the tail end of our hard topic.

Talk for a bit.

Just letting us dwell on things we've heard or seen throughout the week.

We're going to start.

I have been really excited about this topic because, of all of the scrutinizing and negative things to be said about AI, a lot of which we've already talked about on the show, one of the things that has always excited me is the prospect of it improving just health for the average person and life expectancy.

Like the things that I imagined were AI helping you dig through research and data in a way that couldn't be dug through before and that brings you to some sort of solution for a disease that we can never cure before.

So those type of things, I think, should be.

We can give people new diseases.

Or and

mix it up.

Like a patch notes.

Here, stick, you're going to pop this Neuralink in.

It's going to give you malaria.

But if you don't like that one, you can just goggle it to the social.

There's like your ability to experience all disease with none of the consequences.

It's like a Minecraft world.

You can do a random seed or you get a predetermined, like the most popular trendy disease right now.

You get that inserted into the brain.

This is going to be the this, maybe this is like the rich, the weird rich people trend, like 100 years from now.

Do you know, like, the old, like, older stories of in medieval times, like all rich people would like gorge on food and then, like, make themselves throw up so they could keep eating?

This is going to be like that a hundred years from now.

My first thing to do is to wake up and get malaria for six hours of the day.

My morning routine,

10 minutes of malaria 15 minutes of alzheimer's then i go get three treatments of chemotherapy

god so it'll be uh yeah i'm excited about it it'd be like a cool train yeah damn it's so sick all right well that's the world i want to move to so the way i'm going to break this down so uh we're going to talk about ai and healthcare

i'm going to talk about what ai is currently doing in healthcare right now that's chapter one then we're going to talk about what ai is probably going to be doing in the next couple years the exciting kind of ventures that are currently being developed that look really really promising and then chapter three is going to be ai is going to fix all of our problems forever and we're going to have no more disease so is this and by the way that's in 10 years in 10 years that's a guarantee that's a dumb that's a dumb broadcast

in the comments yeah if you are sick in 2035 you are come to me get a refund for this for this podcast

So, okay, my first question is this, when you say AI and healthcare solving all our problems, my understanding is that doctors are going to go to chat GPT.

They're going to type in how fix cancer.

Yeah, yeah.

Okay.

And it's good, this is a good thing.

All right.

So there's actually a lot to cover here.

And hopefully I can make a compelling argument, both setting up the groundwork for like why this stuff matters and how healthcare works and then how it can help.

So it's very important to recognize right now.

Everybody uses the term AI to mean everything in the same way that you're like, oh, the internet is good or bad.

The internet has a million different things, right?

So AI can mean many different things.

We're not talking about chat GBT in this podcast, really at all.

Chat GPT is one product with AI where you talk to a chat bot and you can get information and that's cool and great.

I imagine that there will be an increasing amount of like hospitals who have a chat GPT agent that like screens you for certain things like primary care.

Maybe that would help primary care physicians, you know, alleviate some amount of workload, for example.

But what we're talking about here is machine learning.

That's fundamentally what AI is.

So chat GPT is a subset of AI.

More broadly, we're talking about machine learning, which is basically the ability to get a network, a software product to analyze a whole bunch of data and learn from it and then solve any problem.

So the idea is very much like, can we create systems that can learn to solve anything really, really quickly?

Not can we talk to ChatGPT and ask it for questions.

Okay.

Because that is not a good.

So yeah.

And if you hear, like right now, there's a lot of dumb things going on with AI.

And in fact, I want to pull up.

If you could pull this up on the thing, Perry, we are in the hype cycle.

Okay.

Oh, I love this chart.

This chart, right?

So, the innovation is triggered, right?

And so, the expectations are really inflated right now.

And everybody's going, Oh my God, AI, incredible, everything, and it's amazing.

And so, we're kind of at the peak of inflated expectations right now, where everybody's jamming AI into absolutely everything, and we're rapidly heading towards the trough of disillusionment, which a lot of you guys are already at.

Probably, I was going to say, the investors are at the peak of inflated expectations.

Investors, everybody else is like the commenters are in the trough as well.

Yeah, yeah, yeah.

This week, I'll say in my car, I asked Apple Intelligence, like the new replacement for Siri, how to say a certain word in Swedish, and it couldn't handle that request, which didn't, you know, didn't leave me.

You were deep in the trough.

Because, like, 15 minutes earlier, you were up on that peak, and the expectations were inflated.

Um, this is, I actually, I chatted with my sister about this as well.

She is a registered nurse, so works in healthcare and primary care.

I was talking about how some company that they work with was like, we're so happy to announce that we've introduced like a chat bot that can summarize your patient information.

Or it was like, this can like reply, give automated replies to people who, to patients who reach out with you.

And it's just the most like unemotional, awful thing.

Like somebody's opening up about this trauma and their dog died and all this stuff.

And it's like, wow, so glad to hear that.

You should take this medicine.

So as I'm sure everybody has seen, there's a lot of people right now.

The most hype cycle thing is that every CEO in America is like, oh, AI gets stock to go up.

And so they They say AI 10 times.

They say AI 10 times and they literally, and you just got to grant AI.

Yes.

And so they're going to keep saying it.

So I want to say that up front.

I'm not claiming and nobody's claiming that ChatGPT is going to go solve cancer or anything like that.

But what is really valuable is deep learning, which is, again, the broader concept of AI, which is where you train a system to basically solve any problem, particularly specialized problems.

So.

Let's start with cancer.

Let's talk about what cancer is.

Always a good opener.

That's what I do at dinner parties or in lights.

You You guys hear about cancer?

You guys talk about cancer?

Let's open with a little cancer discussion.

So cancer, the reason cancer is so hard, and I had a family member who the past couple years who's been who is dramatically affected by this.

So I've been, so I'm partially focused on that for that reason.

But also cancer, obviously, extremely big deal.

The reason cancer is hard to cure is because cancer cells are your body's own cells and the DNA is mutated to where the cell now thinks it should just be growing and expanding constantly and not dying.

So that's where a tumor comes from.

It is the cells expanding repeatedly and they're programmed to think that's what they're supposed to do and they just keep expanding.

They try to not die and this goes on and on and on.

And then that causes problems, not only because you have a physical thing in your body somewhere, but also because it's growing so rapidly, it's consuming all this energy, potentially releasing enzymes that are causing all these different problems.

So cancer in particular is not like other, let's say, you know, a bacteria where it's like this thing comes into your body.

Cancer is your own cells that are just mutated and and they're doing the wrong thing.

And that makes it very, very hard for your body to correctly identify.

This is a bad cell that is behaving badly.

Because on the surface, it kind of looks, well, this looks legit.

This is a totally legit.

This one's ours.

This is ours.

Yeah.

This one's ours.

Yo, let it go.

Yo, we're chill.

There's more complexity to this.

Oh, yeah, by the way, disclaimer: I am not a biochemist.

So as much as I typically claim to know everything about everything, there might be slight broad generalizations here.

You're telling us before the pod that, like, biochem or whatever was the class you wanted to

sailed out of biology

in high school.

I got that was the only grade I ever got where I got like a D-min on a test, and that was right when World of Warcraft released uh Blackwing Lair

in 2006 or seven, and that's a raid, it's a 40-man raid, you got to show up a couple hours every night, and I was not learning biology.

Um, so this is like the core problem with cancer, and so we basically have three ways to treat it right now.

One is chemotherapy.

Chemotherapy is where you put a drug in your body that is specifically meant to attack fast-growing cells.

Because one of the hallmarks of cancer, like its whole thing, is it just keeps growing and it doesn't die.

Right.

So

if you get a drug that is able to identify cells that grow really fast, then that drug can go in and be like, aha, you're growing way too fast.

You're a tumor cell.

Caught you.

Kill it.

Yeah.

The problem is there's other parts of our body that grow really fast.

For example, bone marrow.

Ooh, and you do want that.

Like bone marrow is actually helpful.

Also, your your hair.

I mean, that's like chemo has had worked a lot on you.

And then chemo is radio radiation.

Did you say rich?

No, no, no, it has worked on your hair.

Radiation is different than chemo.

Yes, we'll get to that.

Right, right.

I was attempting a bald joke, and I don't know if it really landed, but I think the audience is so desperate and hungry for bald jokes, they'll be like, nice one, dog.

Particularly bald today.

You really, you really got him.

That was clever.

Way to work it in.

So chemotherapy is like one of the main three ways we treat cancer.

This is you basically are giving yourself poison that attacks fast-growing cells and you do a bunch of collateral damage in the process because you're killing off a bunch of your own fast-growing cells.

Yeah, my understanding is chemo is basically it's attacking your

like hedging that your body can tolerate the damage done to the rest of its systems while the chemo destroys the cancer.

Yes.

It's like you're pointing, it's like the end of fight club.

You're like pointing a gun at yourself and you'll be like, I'll pull the trigger.

I'll fucking do it, man.

It's badass.

And so, yeah, no, it's, it's bad.

And so for people who have had, you know, friends or family who have gone through chemotherapy, the reason you feel like fucking terrible the whole time, why it's brutal, is like your stomach lining, for example, fast-growing cells.

So, if you're killing your stomach lining, you're going to get nauseous and inflammation and indigestion and all these horrible things.

Your bone marrow, which is helping, you know, run your immune system, that's getting shot to all hell.

So, there's all these negatives.

And you can't even, every person is kind of different with how tumors show up, which we'll get to later.

So, you don't even know the effect that a chemo is going to have unless you start it.

So, chemo, it's like one of our best things right now that we have.

The second is radiation.

Radiation is where you are applying basically high energy at cells.

So, most often what this means is you take like a gun and you shoot electromagnetic radiation at cells.

And this is bad for cells.

I can tell this is an American podcast because all of our metaphors

about how different treatments are like different types of guns.

So, a chemotherapy is like a lot of little guns in your body.

Like a restaurant, like Burger.

Your body can only take so much burger.

Okay, okay.

So radiation is a little different.

What you're doing is, I mean, you can literally think of it as a gun, kind of.

You are shooting a high-energy beam of electromagnetic waves at a target.

And radiation.

is not good for cells.

It kills cells.

Basically, it fucks up the DNA in a cell, and that makes it either kill itself or it will over time die.

So radiation in the body is super bad.

It is in the same way shooting yourself in the chest is bad.

I've heard.

It is not good for you.

But in theory, let's say you could point a gun just at the cancer tumor and just shoot that.

Well, that actually could be great.

So radiation, which is a thing you do not want in your body, if we kind of like have a narrow beam that we target directly at the tumor can hit just the tumor, that actually can help kill off a bunch of those cells and prevent it from growing to the same extent.

So that's radiation.

As you can imagine, also not ideal.

We are shooting radiation beams into the body and just trying to avoid shooting all the other stuff.

And then the third is surgery, which is if you are able to, in some cases, you just cut the tumor out, but that is basically like,

you know, you have to get to a point where the tumor hasn't spread.

If it's spread around the body called metastasizing, you're kind of fucked at that point because it's all over the place and it's already spreading.

And it's more about like a broad treatment like chemo that's going to have the best chance of attacking it versus surgery, which is a very specific thing.

So there's a lot of people who get, you know, cancer, for example, let's say pancreatic cancer, and maybe they're able to get a surgery and cut that out of the pancreas, causes a whole lot of collateral damage.

And if you're lucky, you got it before the cancer spreads.

I see.

Yep.

Yep.

So if cancer spreads, it's really bad.

And there's, okay, quick more terminology, and then we'll get to the kind of like.

treatment of it.

So you have all these scans to determine if somebody has cancer.

So you've probably heard of things like x-rays, CT scans, MRIs, biopsies, ultrasounds.

Basically, there's there's different categories of how we scan the human body, see what's in there, and then doctors are able to go, okay, this or this or this thing is happening.

I watch House MD.

I'm kind of an expert.

The thing is, with cancer, it could be lupus, but that doesn't explain the headaches.

So

something like 99% of the time, it is lupus.

That's what I've learned.

Yeah.

Cancer specifically.

Lupus is the key.

What we're going to keep coming back to is radiation.

Radiation is, again, it's like really, really high energy waves, like x-rays are literally shooting protons, and that kills cells.

So you don't want the body to get a lot of radiation, but we can use it to learn what's going on in the body or to kill cells if we want to.

So an x-ray, if you go get an x-ray, you're shooting radiation into your body.

And basically, the energy as it goes through, you see that it is being absorbed by certain parts of the body, like a bone or whatever.

And that allows you to see if a bone is broken.

This ties,

my closest friend from college, he's studying to become a medical physicist right now.

And I didn't know this was a job a year ago.

I didn't know this existed.

And they create their position is to create like the radioactive isotopes that these sorts of machines and treatments use.

And there's a really limited of these like residencies and positions for this role in the country.

And he's studying that right now

at university.

And it's wild.

I never thought about like they're, they're not like the x-ray technician.

They're not the person like performing the actual treatment.

They're creating the

radioactive isotope that the treatments or the machines use to do these things.

Keep it grounded.

He's making a gun.

He's basically making a gun.

He's making a gun.

So he's trying to find safer ones, I assume?

My understanding is that, you know, an x-ray is a lower dose of radiation, but like if you do a bunch of x-rays, that's also

very damaging to your body.

So this is the whole thing.

It gets back to.

So there's all these different ways of scanning your body.

An x-ray, we've all done that, right?

It shows you bones.

But if you want to get more detail, you do a CT scan.

A CT scan is where you go in and your body...

to put it simply, you get a bunch of x-rays.

So it's doing x-rays on many, many different planes of your body.

So you're getting a shitload and from that you can kind of make a map of the body you get more detail but the problem is you just got a shitload of x-rays that's a bunch of radiation in the body which again is not good that's going to damage healthy cells radiation also ironically can create cancer cells because if you're messing up the dna in cells that can cause them to become cancerous so the one of the most powerful things we have radiation to learn what's going on in the body and get rid of cancer also can create cancer if you're not careful.

Is there a counterpoint where

this is how most superheroes are made?

So, is there a chance that you could get

so

kind of ignored?

Yeah, what's chat GPT got to say like that?

About like how we so we ran a test to basically see if that would work on anybody.

It was called Chernobyl, and we're still waiting, still waiting for a Russian superhero.

Right, it really is.

And so far, we haven't really given that enough time to stew.

Oh, Vetchkin, I think.

And what's the who's the karate guy?

He came out of Russia.

Stevenson, Stephen Seagal.

Steven Seagal, also

from your theory is that he had a Chernobyl radiation.

Right, he was one of the test subjects.

This is good.

This is good.

I didn't know that.

That's a fact, though.

Yeah, but most of the test subjects, again, do die because radiation is bad for you and it's not good.

All right.

So, yeah.

So

the kind of main things that are relevant here for this conversation is CT scans.

These are extremely common.

It's basically a ton of x-rays.

You are exposing your body to a bunch of radiation.

So you're trying to do this as little as you can while getting a lot of information.

And a CT scan can actually actually show, okay, there's most likely a cancerous tumor there.

Then there's MRIs, magnetic rib resonance imaging.

Anyway, magnets.

You get into an MRI machine and fucking pulls all your atoms with magnets like to your, to the edge of the cells, and then it whips them all backwards and it uses that to figure out your body.

It's crazy.

So an MRI is actually even more accurate.

So you can see in more detail what the body looks like.

In fact, let me show a CT scan because we're going to get into this.

If you can pull up this, Perry.

So a CT scan looks something like this, right?

So this is like a a slice of a CT where it's basically one, you know, one image of your body that's like, okay, this is what's there.

Okay.

So, and you might have seen this with like an x-ray or something like that.

It kind of, it kind of looks like that.

An MRI is even better at particularly soft tissue, meaning a tumor or something like that, rather than bones, because a tumor is just, again, a mass of these cells that keep growing, or at least a malignant tumor is.

And so an MRI, what's really cool about an MRI is there's no radiation.

You get a person into an MRI machine and it's crazy and it's kind of scary and claustrophobic and it's expensive and it's slower and it's harder to do, but there isn't radiation.

So MRIs are like great.

I did not know that.

That is cool.

Yeah.

So radiation, so radiologists, including the one I spoke to for this, is like MRIs are kind of ideal.

CT scans actually have less detail and put the body through a bunch of radiation, but it's easier to get people through CT scan.

It's cheaper and easier.

Cheaper, easier.

So then if you are, for example, like a hospital or the NIH in Britain, it does actually matter, right?

If you can get through twice as many patients with a CT scan versus everybody get its MRIs, then that's better.

Or often what happens, you get a CT scan and then you get an MRI for additional information.

There's other supplementary things like a PET scan.

So that allows you to see.

You do a CT slash PET scan, which is where you give the body radioactive sugar.

And then that gets eaten by the cancer because, again, the cancer is growing so rapidly that it's draining your body of resources.

And so you can kind of use it to find where the cancer is.

But then the ultra, the ultimate thing is you do a biopsy.

This is where you take a physical sample of the tumor and scientists look at it under a microscope and go, yes, this is cancer.

So in general, the simple way to think about this, there's kind of three levels to this.

If somebody thinks you might have cancer, you generally get a CT scan.

So this is a whole bunch of radiation, but it's going to show, does it look like there's tumors in your body somewhere?

You might additionally get an MRI, which is going to be more detailed, but again, it's slower, more expensive, but it gives you more information.

And then the ultimate you do or do not have cancer is a biopsy that's where they grab the actual physical cells and look at them they have to find it first like they'd have to you have to do a different scan okay yeah so you don't start with a biopsy because that would mean they're just randomly what are these

sucking stuff what if i just feel it and i tell the doctor like biopsy right here or whatever yeah so you could

uh and that's what an ai will do it will go just touch you every morning and look for lumps and just be like ah biopsy your robot assistant will give you a pat down and be like this is like

and you're like oh your boobs are feeling a bit big.

And you're like, no, I've just been gaining weight.

That's nope, biopsy time.

And it just starts with a turkey baster grabbing cells.

I would not want Elon Fusk optimists to decide whether my boobs are too big.

I need to biopsy.

Wow, you've won.

Oh, wow.

So you're supporting cancer today.

Pro-cancer.

She'll be in the village here.

Guys, if you're going to leave a comment, who do you think won the debate?

Pro-cancer or anti-cancer today?

All right.

So this is the groundwork for all the stuff we're going to talk about.

Cancer, fast-growing cells.

We have three.

We have these different ways of scanning it.

And then we have a couple, not great, but they sort of work and help people ways of treating it which is chemotherapy radiation surgery all with this weird dynamic where radiation is super bad for you it kills cells and it can cause more cancer but we can use it to kill bad stuff and to learn about it right so understood that is the whole dynamic so let's focus in on imagine you are crafted robot from our community who is a trainee clinical science at the na nhs in the uk nhs i said nis and whatever n a national healthcare service in the us in the uk for people who who are not aware, UK has a national healthcare system.

So basically, this is somebody who is involved with the treatment of cancer patients with radiation.

So this is a person who is actually, you have a cancer patient who comes in.

They're like, I have prostate cancer, and he is part of the team that's going to make sure the machinery works, kind of like what you're saying.

The machinery works.

They're going to use the information from the scans to determine like, what does this person look like?

They're going to figure out a plan of where to shoot the radiation gun, which again is literally a machine that's going to shoot a beam of like deadly laser into your body.

Exactly.

And then and then, you know, basically follow through with that treatment plan.

So the process is basically what I just described.

You have a patient come in, you have some degree of scanning, and the first thing you need to do, you have some degree of like information from the previous scans.

that determined that the person even has cancer.

And the first thing you need to do is create an outline or a contour of their body because each person is different.

And you need to know if you're going to be shooting radiation into their body, you got to know exactly where the tumor is and like where their organs are, right?

They're a unique combination of people.

That sounds important.

Yeah.

So what that means in practice for somebody like Kraft is that he looks at hundreds of images like these, because again, a CT scan is usually just like many, many x-rays, essentially.

And he has to combine these together to make a map of the person's body.

that then they will go, okay, we can shoot radiation from this angle and with this intensity in order to get the maximum impact.

Okay.

But the first step is this auto-contouring or contouring where you are taking these, these x-rays, basically of the human body and turning them into something like this.

So I'm going to zoom in on the oh, okay, there we go.

So you can see something like this is

you're actually mapping specifically how this person's organs are arranged in very precise detail.

And this matters, again, a lot if you're going to shoot radiation into their body.

This looks like in a video game, if your lungs were like more damaged than your audio.

Yeah.

No, it looks like

mid man when you go into like the stealth mode and you can see the enemies.

So this is a, let's say, average three-hour process to take all of these x-ray images from the CT scan, the CT images, and turn them into this outline of the body.

And this is like one of the main steps that's needed in order to get a treatment plan for this patient.

This is really hard and it takes a lot of time.

If you think about treating thousands of cancer patients, that is a huge bottleneck of needing a trained,

you know, radiotherapy oncologist, technician who is able to make one of these plans using these images.

Not only is it hard, it's actually kind of subjective.

You have to make educated guesses about what goes where.

It turns out a lot of parts of the body are near each other.

If you're trying to target the prostate, for example, there's all, you know, there's intestines all over the place.

It's not always clear what is where, right?

And so you making this map

of the human body.

I think one of the things I wanted to express here is that I think there's an expectation or a kind of feeling that doctors are all great and we need to make sure we match that.

But doctors are actually subjective in a lot of ways.

You're, you're often, I talk to doctors for this.

They're human.

One, they're human.

And two, so much of what's going on in medicine is like, you're like a detective, right?

You get all these clues and you're trying to figure it out, right?

It's not like you, you don't just get to like check a box and be like, yes, it is that.

There's maybe like 50% of stuff where you're like, okay, I'm using my best guess to figure this out.

And anybody with a chronic disease or anything can attest to this, where, you know, different doctors will say different things.

So this is an example of one of many elements of medicine where there's a subjective element to this, where you're trying your best, but the technician comes up with this outline over the course of three hours.

Then the doctor will come in, confirm it's all good.

And then they say, okay, now let's come up with the treatment plan to shoot this person with radiation.

Okay.

So this whole process, right, if a person feels like they maybe have cancer, they go into the hospital, they get scanned with a CT scan or an MRI.

They get this imaging and a doctor will say, okay, it looks like you have cancer.

They then get a biopsy to confirm that it is 100% cancer.

At this point, maybe they're recommended for radiation.

They go into the radiation team, which is where like crafted robot would be participating in this.

They use the scans to build an image of their body.

From there, they build a plan to shoot radiation into the body.

And then, over, let's say, 20 sessions, it varies.

This person is coming in, laying on a table, and being shot with radiation in the exact way that they're doing.

Recording how they're and they're checking it on it and seeing if they're okay.

So, this is an example right now, where this company, this is a sperm bank in Germany called Siemens, is using GI

right now

to speed up this process.

He's joking, to be clear.

Well, I don't speak German.

So

if you imagine that there are thousands and thousands of cancer patients coming in every, you know, every week, every month, whatever it is, if you are the entire country of the UK or any part, any healthcare, wherever you want, you have all these cancer patients coming in.

There's two reasons why the speed of this matters.

One, if you cut out a three-hour process that a technician has to do by hand, that is a crazy speed up.

And right now, this program, Siemens, is being used in the UK by the NHS and is taking that three-hour process and making it 10 minutes.

That is a crazy reduction.

Not only does that mean a technician is now has way more resources to spend on additional patients or focusing on reviewing the, let's say, you know, 95% of this that's done and they make sure that it all looks good and check it off and everything.

Not only that, as a reminder, It's really fucking important to get cancer patients into the hospital.

As time goes on, the cancer is growing and potentially spreading.

So if you get somebody into, like it's a very common story that you get diagnosed with cancer and you can't start treatment for weeks.

Sometimes you can't get a scan for weeks, right?

And this is terrifying if you're like, there is maybe a malignant tumor that might spread and make me ineligible for surgery and kill me.

Every day matters.

And so if you imagine that this kind of process gets us from we can see, you know, let's say 100 people a week to 150 or 200.

or you get patients in a week earlier than you would have otherwise, this is potentially life-saving stuff.

This is a big

question about this process.

As someone who also played World of Warcraft in high school, so okay, let me just, simple question.

What you're describing is it is taking the images from the CT scan or the MRI.

Yes.

And instead of a human putting them all together into a composite, it is using machine learning on a million composites to determine what the likely best combination is.

Yes.

Yeah.

And now

the doctor has access to this much quicker.

Yes.

Okay.

So basically that step, and it is exactly that.

So when we talk about AI, so right now there's an AI system by Siemens that is doing this.

Generally, the way you make an AI system like this is you need a whole lot of data from what humans did in the past, where they figured out here's what a person's contour looks like.

And they then feed that data into one of these systems.

And by doing that tens of thousands of times, the system can now extremely quickly look at a new patient and go, okay, I get 99% of what this is.

So that is fundamentally what's going on, something we'll come back to.

You need a lot of data to start this off for most of these services.

In this case, it requires the data that we've had from a long time of doctors doing this by hand, but now this machine can be.

Basically, the same way a doctor would learn, right?

A doctor would

look at

one of these composites or whatever and see what a previous successful doctor already did and learn from that.

It's just a sped up advanced technological solution.

But it's the same kind of

naively,

when I think about medical solutions because of machine learning, I imagine

somebody in a lab like poring over data and a bunch of different scientists around the world like collecting data that the AI then analyzed and then reduces into some sort of like vaccine or super serum.

And then that's the cure for cancer.

Like in my head, that's the, that's the

10 years.

Within 10 years, I do promise.

But in this case, it's so so cool to see.

It's like, no, this is just an automation of an existing process that takes a really long time that humans have to do themselves right now that we can just streamline.

And it's not creating some sort of miracle treatment that doesn't exist.

It's just making the existing treatment way more effective and faster to get to, which is.

And reducing mistakes, I assume.

You know, one study I saw a while back that is super stuck with me.

is the idea that, you know, judges have spent their whole life studying the law.

They're probably more knowledgeable about the law than any of us.

Judges impose harsher penalties before their lunch break than immediately after because they're hangry.

Right.

That's a real, it's a proven longitudinal study that when they're hangry, they, they're more, so like humans just have these kind of, especially the things they do every day.

We're human.

We're just human.

They're going to make these errors.

Or even talking to friends that are in, that are nurses or are in residency or are doctors, you know, being at the tail end of your 20-hour shift as a resident, dude, it's insane.

Because I've asked my friends before, I'm like, what are you bringing to the table at hour 19?

I don't want you to see me at hour 19.

That's what I want.

Yeah.

I want a fresh.

But that is the phase that you're interacting with with your medical staff a lot of the time.

And

that's not their fault.

I mean, the system, the medical system, is constructed a way that forces that to be the case.

But

yeah, not, I wouldn't want to have my exhausted MD trying to piece together all the scans at the end of a long day when something could potentially

life or death for you.

And life for him is, you know, it's one of the things that you feel terrible.

You wake up, you're exhausted, you got a bad night of sleep, whatever happened, maybe you're a little sick or something, but you still go into work and you kind of power through it.

Imagine you're a brain surgeon, right?

Like that person is, their entire life is in your hands that morning.

And it's like, it's crazy.

how much depends on the performance of a person and how variable it is.

One of the things with this family member of mine who, who passed from pancreatic cancer over the past like two years, they got to a point where chemotherapy had gotten them to where they're eligible for surgery.

So eventually they did surgery, but we talked to three different surgeons who gave three completely different answers about whether or not there should be this surgery for pancreatic, for the pancreatic cancer, right?

Yeah.

So again, it's like, even when you, these are like three of the top doctors in the country, even when you talk to them, it's like people have different perspectives of what's going on.

So one of the things that things that Kraft did mention, who again, does this radiation oncology work right now, is that like different, as you can imagine, different people have different specialties in the medical field.

So, some people in this process of treating cancer patients with radiation might be a little more skilled at one area or better at detecting certain types of things, right?

And one of the benefits of AI, and we'll get to downsides, but one of the benefits of AI is that you're kind of pooling all that information together, right?

If you can have the scans from the guy who's insanely good at, you know, at finding pancreatic cancer or the angle that other doctors can't, and you combine that with the doctor who's way better at at contract or whatever it is, right?

And you combine those skill sets together rather than you, like you have a source of knowledge that pools all of it rather than what people right now have to do, which is you basically reach out to doctors and you try to get the best one.

Hoping you get the best one.

Right.

Yeah.

I think that it, oh,

do you know

what sort of feedback has there been on this so far?

Because

I imagine this is similar to self-driving in that the obvious benefit is that by automating this, you could potentially make it way more effective, safer, cheaper, et cetera, right?

Yes.

But much like self-driving, any accident that is highlighted is even if it's like by the stats, maybe more effective than the human it's replacing, it becomes a gigantic story or fear that any accident or failure is happening at all, right?

So it's not seen as an adequate replacement for the human equivalent until it's totally perfect.

Yes.

So has there been, you know, what is the, for example, the one we're looking at right now, what is the effectiveness of this so far?

What's the pushback?

Yeah.

So, and we're going to keep like right now, we're just talking about what AI is doing currently in 2025.

Yeah.

And then we're going to get way crazier over the next like 30 minutes.

But yeah, right now, so there's a lot of hesitation.

For Crafted, who is doing this radiation work and talked about this,

his experience, it's faster and there's less errors actually than humans doing this.

So it seems to largely be extremely positive.

There are, of course, long-term questions, though, of like, how do you train somebody to be good at this in the first place and be able to come in and review that end process if you weren't grinding and doing that first thing?

There's ways around this, but every industry is asking this right now.

For example, coding, same question.

You're like, should a person learn the fundamentals of computer science?

Or what if they just use an AI and never learn them?

Right.

So it's this very interesting.

Like, how do you teach new people in the field to be able to oversee AIs?

So there's a long

concern around that.

It's very useful when you have an expert paired with AI who can make things quicker and easy, but they have the expertise to check it.

But I do understand the issue with new people in the industry.

It's like, well, they don't get that expertise and they're still.

Why can't I just type it in into my TIA form?

That is an interesting thing.

Chat GPT, I'm asking you to do it.

Yeah, I mean, so with software, it's like I have a computer science degree.

I went through learning the foundation.

When I use an AI, I can check off everything it's doing.

And then I have friends who are learning from scratch with ChatGPT and they barely know any fundamentals at all.

Right.

And so there's a very interesting broad question about learning.

So that's part of it.

I think there's also a lot of resistance talking with my sister, who's again a nurse practitioner or

nurse, resident nurse.

Oh my God, R-N, resident nurse.

Registered nurse.

That's like right below being a doctor, basically.

Cool.

I'm sorry.

So go ahead.

It doesn't really matter.

Point being, she works in primary care.

Okay.

And so she described a lot of her, and also my mom is a physician.

So a lot of doctor people.

Doctors in the family.

a lot of doctors in the family.

You went into tech, though, yeah.

My okay, you're kind of a black sheep.

My parents are doctors, my brother and brother-in-law are doctors, or you know, in the healthcare field.

My cousins are doctors, my aunt and uncle are doctors, and me and my brother play video games.

Well, you said, I don't want to help sick people.

No, well, so I actually was like thinking about it, and then World of Warcraft came out.

I was like, I want to make video games.

And now, hey, who's having the bigger impact on the world now?

We're podcasters, we're solving people.

The most important people

are solving it right now, Saving lives.

And so she talked about how there's

a lot of hesitation for multiple reasons.

One is people in the medical industry are having the same experience, like we talked about earlier, that everybody else is having, where some executive barges in and is like,

you're going to use this AI chat bot.

And it's just not.

It's going to save us so much money.

Right, right.

You know, this, use this to reply to your patients.

They're just like, oh my God, no.

So there's a lot of resistance that is justified.

There's a lot of resistance in the medical industry generally to change things.

There's a lot of old, crotchety doctors who just don't.

And I, and my dad experienced this when he was trying to get doctors to switch to telemedicine.

So much resistance because these doctors who have been in the field for 30 years didn't want to do it.

It's like, no, don't want to.

I don't want to see them.

They need to come into the office.

And it's like, dude, this is clearly going to be helpful for many patients.

So there's, it's partially just old guard kind of vibe that people being averse to change.

And it's partially it's not there yet, right?

It's it for most of them, it's not actually adding value yet.

I'll just say a third thing is with all things, um, somebody financially benefits from the current way of doing things.

Like somebody is an expert that can get paid absolute top dollar

to be compiling these things.

And if it becomes fast and cheap and easier, that person loses an economic advantage.

And so that a lot of resistance comes back to that in most fields.

I don't know about medical field, but in a lot of fields, some people are resistant because they have a monopoly on some skill that allows them to extract extra wealth that is attacked.

And the last piece is the healthcare industry in a lot of ways is not you're not a robot that's just doing this stuff maybe you know maybe a little more if you're a technician behind the scenes but like if you are for example a primary care physician a large portion of what you're doing is also kind of being a psychiatrist right you are also determining like what's going on you know the example i've been given of like if you're going in you're going to treat somebody who has trauma and you walk in the door and they start sobbing versus a person who's like drunk versus a person who's the most laissez-faire person who has no issue they just immediately it's old woman who takes her shirt off and she's like all right go ahead and you know like those are all very different situations.

Yeah.

And so,

yeah, I won't give all the details, but I heard some interesting examples.

But, you know, a large portion of the job is about being, you know, being tuned into the human element of what's going on.

It's not an algorithm to fix everything.

You got to be conscious in there.

And that's both.

That's both a, you know, a blessing and a curse to the industry.

The blessing is people can really be human and

carefully cater what they're doing to what a person needs.

And on the other side, not everybody can do that.

You know, and it's, it's almost unfair for us to treat the healthcare system as like, you should listen to all my woes and be, you know, responsible for everything, which is just is one of the reasons that so many people in primary care feel so overwhelmed.

And everybody I've talked to feels like it's overwhelming right now.

Yeah, I've heard that as well.

So this is some of what's going on right now.

So now we're going to start to accelerate.

All right.

This is one example we've talked about.

And now let's talk about a lot more stuff.

Turns out this example with radiation of helping speed up the process, cutting that three-hour thing down is being replicated replicated in a lot of different areas.

This next one, if you pull this up, Perry, Page AI.

So, this is a company right now, a healthcare medical company that is helping speed up cancer diagnosis.

So, again, we talked about how

biopsies is where you actually get cells from the tumor and then you analyze it to go, okay, what type of cancer is this?

Confirm, is it cancer or not?

This software does that same thing.

It is looking at an image that it's been trained on many, many, many biopsy images.

It now knows how to recognize cancer in biopsy images extremely fast.

So doctors can get the sample, feed the images of it to this AI, and now it's able to diagnose exactly what's going on much quicker.

This is FDA approved for prostate cancer detection.

They're expanding to other cancers.

Again, this might sound like, eh, why is that a big deal?

But if this speeds up how quickly you can start cancer treatment, if you're a patient who like, if you're given, you know, for example, pancreatic cancer, which you very well could have a couple weeks to live if you don't start treatment, this is a big fucking deal if this cleans off an hour or two hours.

Does this mean when I start logging into places and I have to do a CAPTCHA, it's going to be four cancer cell images and I have to pick which one?

Pancreatic.

Are they going to be usable time?

It's crazy.

Like tumor cells in the body all look like letters that are a little distinctive.

I just need to get in my yacht.

Because I'm always picking the one that has the bus in it.

And I know they're using me for training, Doug.

And I just, I picked the wrong one or it's half a bus and it's not right.

And then I get logged in.

I hate it.

I hate it.

But, okay, I see this.

The cool thing with this is I imagine outside of cancer, anything that is image diagnoses based, right?

Like, I just sprained my ankle pretty badly four weeks ago.

Yes.

Any, anything that is a like a break, a sprain,

like your fracture,

a torn ligament, anything that could be identified through imaging, you could compile a database of different like

breaks or issues and then have the tool reference the database and then pull an immediate diagnosis.

That is amazing.

That's pretty cool.

I mean, I imagine there's some sort of maybe

downside here with protection of your medical data, something like that.

Like, we have this,

you know, we have a counterpoint.

I'm a tech bro, don't care about your data.

Okay.

Now, we will talk about data.

Yeah, that's a key part of this.

Yeah, and I wonder how though

you can.

Am I crazy?

That's a good question.

We'll talk about that.

It's interesting to see that this would clash with some level of modern, like HIPAA regulation, right?

This would clash with that.

And the data or the pool of data to reference might not be possible to build with the current regulation and who gets to own that data.

Is it private companies?

Is it the government?

Who gets to take advantage of that?

But I'm just thinking about my experience walking into an urgent care four weeks ago, trying to figure out how bad my ankle sprain was and how long this was going to take to heal, what I should do.

It's like, if I, and I got an x-ray.

And so having that be verified quickly through a giant system of all the people who have been imaged before me

millions of other people, that sounds amazing.

Yeah, and that's a mild thing, right?

You know, so I'll rapidly go.

So you've picked up on the pattern, which we were working towards.

This is all image-based stuff, right?

So let me hit with a couple more.

This is Viz AI.

They basically, once a stroke patient comes in, so stroke is where there's blood flow blockage in your brain in some way.

This is like every minute that goes by, your brain is dying faster and more.

Like it is, it is super time sensitive to treat somebody with stroke.

You get them into the hospital, you do a scan, and then you need to.

you need to look at the scanned images of their brain and go, what type of stroke is this?

What's a reasonable treatment?

And if that is typically 30 to 60 minutes, Viz AI can knock that down to a couple minutes, right?

And that is a huge deal.

If you're talking about a five-hour process, knocking out 45 minutes and allowing the treatment to start faster might literally be the difference between you have your legs functioning or not, right?

This is is a big deal.

Again,

this is serving 45,000 healthcare providers.

Right now, it's been acknowledged by the government.

Some Discord folks suggested one from Super Levis in the Discord.

Singapore, I think I have it up here.

No, no, that's another thing.

Okay, well, I'll just list out.

Singapore has been like their national healthcare innovation center has been funding AI projects that are like doing eye scan really, really quickly.

And so now they're able to do mammograms and eye scans way faster than they were before with higher accuracy and get more treatments through.

This is currently happening in Singapore and a bunch of areas.

Denmark released a three-year study that shows that with breast cancer radiology or the breast cancer analysis,

if they use AI systems to, again, basically analyze the image that comes from these scans that people are doing to see if you have breast cancer, that it is dramatically speeding up

basically, you know, how they're able to detect these things and how quickly.

It's always Denmark and Singapore, bro.

Yeah.

I'm sick of them.

I'm sick of them.

I am sick of this.

the sickness of the Nordics, dude.

It's like, oh, what is this cool breakthrough?

Where is this happening?

Oh, interesting.

It's the same three.

Yo, we added a burger where you can have the donuts instead of the buns, and I would never see that coming out of fucking Denmark.

That's actually next week's episode.

That second.

I'm going to do 45 on that.

All the foods at the KFC double chicken.

Next week is live at the state fair.

We're frying everything.

We're eating everything.

And we're giving a middle finger to Denmark.

all right so we can quickly chat about data it's exactly what you said right so to summarize everything that's been going on right now when you're treating diseases conditions some of which are super time sensitive you have to do analysis of imagery that you're scanning from people and if you can get it if you can get an AI to you know, if you give them a couple hundred thousand examples, it can be incredibly good at doing this.

You have a doctor come in and oversee the final result, check it off, say, yes, that does look good.

It's not like we're removing doctors from the situation, but you're speeding it up dramatically.

The question, of course, is like, where's that data come from?

So that's not clear right now.

Talking to crafted with the

NIH, NHS, NHS, with the NHS in the UK, it's like, you got to get companies that have gathered all this stuff to pull it together.

And that can be really challenging.

In the United States, we have a hippo that you also can't, you can't just like share

this big hippo.

But he's got all the data.

That would be surprisingly effective.

We have to get past this hippo to get to the file.

That's the hippo.

We've installed him as the guard.

And that way, only like a really good doctor can get in there and grab files he has to trick him with fire yeah so that's a question not only is there patient privacy of like okay are you down let's say you went and got cancer you know scans for your body are you down for it to just be go fed into some ai for some other company yes i would be but a lot of people why wouldn't you i can see like if i write war and peace and mark zuckerberg steals that scans it and puts it into his fucking meta thing so he can try not to write i get being upset with that but i don't understand why if a doctor doctor correctly identifies my cancer and then we anonymize it so nobody knows that it's me with cancer and it feds into a million other things so they can find other cancer.

Like, what?

I think there's just this strong sense of you know, bodily autonomy.

It's like this idea of anything that is yours, especially your body specifically being used to be in a way that could be monetized, especially maybe losing the autonomy itself, but especially losing it at the behest of monetization is something that people don't like.

I think when you think about the tangible benefits, it like to me, one thing I hate when I move and then I try to go get like a checkup or like a new primary care physician is that we, I feel like we have to walk through all of this basic background information again.

We have to start with all these new questions.

And as somebody who's moved a lot and had to do that over and over and over again, it's like it would be so helpful if you, as my new doctor, could just pull up a page of all of the information about my health from

all the previous times I had come in.

So you have a more specific picture of what's going on with me.

And also, if it's notes from previous doctors, probably better than the picture that I can present to you as the doctor right now.

And in the same way, it's like, well, I would want to volunteer my information to be a part of a pool of data like this.

I think maybe similar to something like signing up to be an organ donor on your driver's license, right?

That you basically agree I'm okay with this image being used for a trainer.

Yeah, there probably has to be an opt-in aspect to that.

My pushback to that would be like, well, what securities securities do I have over that information being abused?

But I, to pretend like I'm really concerned like that, about that personally right now, it's like, well, I think I'm already like.

you know, scrolling through terms of service when it comes to medical apps in my life and signing them over already in different ways.

So to pretend like I'm suddenly worried about this now when I haven't really been before

would be kind of making that up.

I would just hope that, you know, the regulation that comes alongside this is like preventing my, I don't know, the pictures of my ball cancer from being spread around publicly.

What if you got it?

I don't know.

If you check a box or something that says, if this is used by an AI, they have to Photoshop my dick to look bigger.

Make it a little bit bigger.

And that way, if the data does

look good.

It's like you've really touched on the main issue.

I was kind of scared.

Yeah, you weren't even sure you were.

We read in between lines.

That's the heart of it.

Wait a minute.

I do see, actually, I just saw an issue.

Okay, what if, what if somebody, what if an insurance company buys this data and then can

de-anonymize it or whatever, and now you have higher rates because they know you have a preexisting condition or

big bucks.

I just realized how, how I would, I was like, how would I abuse that?

Okay, that's okay.

I see.

Yeah.

No, there, and it's so, and another question is like, okay, let's say, say you're, you're C-Man, right?

Which makes these auto-contouring things.

Yeah.

Again, this, this is being used right now to help with radiology and help it go faster.

This is great.

Yeah.

I don't know,

I should have asked and maybe I forgot, but crafted, I don't remember asking, where does that data come from, right?

And so, really, to build tools like this, you want companies pooling all their stuff together.

But if you are a company like Siemens, you're incentivized to keep the data for yourself, right?

Yeah.

You don't necessarily, if you're the product better, right?

It's a, you know, if you are the company that has been pooling all this data, let's say for stroke analysis, and then you have the ability to create your own AI and another company is like, hey, let's all share so we can all make products.

You're like, fuck you.

Yeah, you might be like, fuck you.

It's also, it's interesting with healthcare.

I think this is by far the easiest and most likable way to apply AI in our world.

So many other areas that are a lot messier.

With this one, I think most people are like, you know what?

If less people can get jobs in healthcare, but cancer is 10 times easier to treat.

We're okay with that, right?

I think most people, same with the imaging.

And cheaper, right?

That's what it is.

And cheaper, right?

People really want it to be cheaper.

And again, we'll keep going forward.

This is going to keep advancing of like how AI is going to do more and more and more.

But I think with healthcare specifically, unlike some of the other areas like writing or creativity or all of this stuff,

it's a little easier to be like, okay, for the public good, let's pool this data.

Let's make an AI able to diagnose this stuff because preventing somebody from dying of cancer, getting that rate down even 10%, saving 10,000 more lives a year, that's worth it.

And maybe that means the government steps up and is like, you have to share this data.

Okay, you know how when you like go to a website that you haven't been to before and you can log in with Google or you can log in with Apple?

What if there was like a government thing with your data and I could go to any hospital or somewhere and I could log in?

I could give them permission and it goes to a database that has my previous things.

And what if it's like the login of the data?

I know it might, I know it might sound like a joke.

I know it might sound like a joke, but I think this is actually how it works in Estonia.

I think

this might actually be.

Back to our roots.

This might be a bit of an Estonia

podcast room.

This reminds me of the wire episode four, actually.

The wire Estonia.

Those are pretty much the the only tools I have

in my mind.

No, I think they have

a system where

your digitized medical data is

more widely available.

Like I can own it, but I can log in and they could give them permission.

I don't know if it's literally like a lock.

I don't know if it's like an ability where I can give them the unlock key.

Yeah.

I mean,

I think what we're asking for here and what you are too is what is the way that this, like

these

private companies have a motivation to develop solutions for healthcare while not abusing the fact that we're contributing very personal data in order to build these databases.

Very big data.

And

also the biggest data.

Well, it's not the biggest of the three of us.

I don't know about that, but I talking megabytes, dude.

People say medium data.

Okay.

In Microsoft Word, it's going like 15 pages down.

It just keeps going.

You need like half a data scientist.

Yeah.

I might need a full team.

It's not a one-man job.

Well, if we take my dick and every other porn star, we can make a penis analysis AI that can do that quickly.

That way I'm not adding too much burden to bring it up.

Well, I'm bringing up the average.

But

if you want to thread the needle of having those things not also allow the average person to be charged more for health care or get made it more inaccessible.

The goal of these things is to increase efficiency, reduce cost.

I mean, if I were,

uh, we, there, there's a section of, I'm in, I'm going back to it.

There's a section of abundance at the end when they're talking more about medical advancements and innovation, and it's talking about the government

using using push and pull incentives, specifically in

the medicine field, the medical field, the medicine, medicine,

to get private companies to like explore and take on more risk to things that would benefit, you know, benefit society.

And I think one of the main examples it uses is the COVID vaccine, mRNA, and it talks about the government resources that were used to initially fund and allow the scientists who basically discovered

mRNA as like a treatment for things or a potential treatment for things.

And then later on, when we made you know covet vaccines which were made uh by private companies the government even the u.s government right we didn't get charged for covet vaccines they were distributed and they were given out for free well that was because fauci wanted to put a microchip that was because of the fauci micro treatments yes to track me and i was willing to accept that i said

i'll take the microchip in order to be less damaged by COVID-19.

I see.

And I knew the trade-off I was making.

And a lot of people don't know that.

But in this case, it's similar, right?

You need the underlying technology to be developed and the solutions for these healthcare problems to be created.

But at some point, you want some sort of like government ownership or regulation over these things so that they aren't abused.

That's what I would be looking for, right?

Yeah.

I mean, that's the end.

If the NHS is working with a company like that, that's a national healthcare system that is cooperating.

with a tool like this to make things better.

I'm concerned it will be harder with the U.S.

healthcare system that everything is

is so driven by profit where the nhs can as the country be like we are going to do this it's still challenging from what crafted was saying of like some of the forward-facing ai stuff another thing uh somebody uh rumble badger in discord gave some great thoughts on this has also worked in this field on like you know developing ai tools for this type of stuff he he listed an article that was mentioned that basically shows

To put it quickly, you have these ECG systems that check a person's heart rate for arrhythmias.

So again, one of these specialty

fields where it's like, can we help a person with this certain type of

issue?

And a person, a human has to go through this vast amount of data of basically how their heart was beating over like multiple days.

Now they have an AI trained on all that stuff.

It was

the humans were 14 times more likely to miss a diagnosis compared to an AI, right?

But the AI would say people had a heart arrhythmia a little more often than a human was.

So it's like, it was better in some ways, worse than others.

And that's, it seems like a lot of the AIs are like that.

It's not a silver bullet that solves everything, but it does, it gets you a lot of the way there, but it still needs some human oversight.

But what he was saying is like, it's like he knows the company that contributed the data to that AI.

And he's like, that is not even what the data was really meant to be for.

Like technically, that's kind of correct.

Also, the company that does that has been dubious in its quality from his perspective.

So it brings up this whole thing around the quality of data actually matters a lot.

And there's different devices.

So if, you know, not everybody is formatting things exactly exactly the same way.

So the way in which you bring all this data together to train an AI, where it comes from, the quality of the people who is generating that data, what if you had a shitty doctor who kept misdiagnosing things?

And then that goes into the pool, right?

Yeah.

And this was from the, this was one of the big notes I had from discussion in the Discord when the topic was teased was how difficult data is to come by in the medical field.

Good quality data is not as widely available as us just scraping the internet for images.

And it's more important important to get it right.

It matters if the consequences are higher.

If ChatGPT makes some shit up, you know, okay, whatever.

If this thing says you don't have a heart arrhythmia, you know,

if the auto-contouring system is like, oh, yeah, this guy can take a shitload of radiation and then you get blasted in your pancreas with too much.

It's like, dude, this is, this is a big deal.

And it gets to then government oversight where, again,

I will just reiterate for people who seem to not realize this.

I am in favor of regulating AI.

I'm not, I don't advocate for AI to just be this like Wild West craziness.

There has to be be regulation around AI algorithms broadly, but particularly in the medical field, right?

Because holy shit, right?

Yeah.

If Siemens comes in or this other company comes in and says, you're a child, it misdiagnoses my stroke and says I have a...

Sorry, don't let the adults talk on this one because this

baby over here can't be.

Dude, I've said, that's like the eighth time I've said Siemens.

You got funny?

He's giggled every time.

Dude, it just comes back.

It just hits every time.

I'm so sorry.

But I was listening and then you derailed.

I did derail.

i derail it one more time because i can't move on the character you created of guy who believes every fauci conspiracy theory but got the vaccine anyway is so funny in my mind he believed all of them but he's like that's a fair trade

dr fauci's evil bro but the diamond microchip is working trade-off

he's tracking me right now because it's cracking me up in my brain okay we're back doug i'm sorry we're back

so there's a lot of challenges and complexities around the data of these things so to summarize everything we've talked about so far uh ai can help with image analysis that's being done right now in a bunch of different fields by a bunch of countries.

I've talked to people for whom they're like, this is helping.

This is better.

This is potentially saving a lot of lives.

It's a big fucking deal if somebody that you love gets diagnosed with cancer and this gets them starting treatment a week earlier or a month earlier.

Makes total sense.

This is a big, big deal.

It's easy to be like, AI is going to be great.

This is saving lives right now, and we'll continue to do so as it gets better.

So, I think this is a really big deal.

There's a lot of challenges, though, with regulation, data, all this other stuff.

So, now let's get into the crazier stuff, which is forward-facing.

Okay.

So this is all happening right now.

But the real holy grail is like, okay, well, you know, it's great that we, you know, are going to get better at treating cancer with radiation, but why do we let it get to the point that a person has cancer spreading everywhere before we even start treating it, right?

Our whole healthcare system, and there's like this, this article I read from the

Doug's about to go full minority report on cancer.

Yeah.

He's the American Hospital Association.

And really the gist of this, and this is what my mom specialized in for a long time in the healthcare industry: preventative medicine.

We have, there's right now, we have like a sick system, which is basically if you get sick, you show up.

But it's way, way, way easier to treat people if you do it in advance.

There's lots of examples of this.

Like, you know, before your heart fails, if you start exercising and taking care of it earlier, that's going to prevent all these issues down the road.

So, not only for things like, you know, like smoking is an obvious one, but less obvious one, Alzheimer's fucks up your brain, right?

Like it's your stops brain functioning correctly and Alzheimer's is brutal in the world we basically by the time you recognize that Alzheimer's is happening it's too late like it's already going and it's already done damage right if you could detect it earlier which we don't really have the capability to do right now you could you could stop it even our even our medicine for alzheimer's right now is mostly about stopping the progression but if it's already done a lot of damage you can't do very much the person in my family was diagnosed with pancreatic cancer it's one of the most deadly basically everybody's dead within a couple years and the biggest reason, on top of it just being brutal, is because you don't catch it until it's really developed.

So, unlike some other cancers like breast cancer, which it's easier to

find early on, and maybe there's a lump or whatever else, you don't notice a pancreatic tumor until it's really causing problems.

And by that point, not only is it in an area that's hard to operate on and it's near all these other organs, it also likely has spread.

So, very often with pancreatic cancer, it's like when somebody realizes they have it, they have a few weeks.

Like, it's often that serious.

And then you're lucky if you make it like another two years or whatever.

So preventative health care is hugely, is a huge opportunity to not only massively shift how we treat people in general, but has a huge impact on the number of people whose lives could be saved, the number of people, like the amount of money and resources it takes to be able to do it.

This is a horrendously scary graph that you're showing here, by the way.

Is it visible on screen?

Yeah.

So number of chronic health conditions.

Basically, this whole article is about how the amount of chronic health conditions has dramatically increased in America.

Can you go back every year?

That number is millions of Americans.

So 171,

2025, 164 million Americans with chronic medical conditions.

Did you scroll down?

What were the most common conditions?

That's that's

half the country.

Yes.

And then

one of the one of the things that noted here

is arthritis.

In the last between 1987 and 2002, two-thirds of the growth in Medicare spending was accounted for by 10 chronic conditions.

Like the majority of the cost, I don't want to say majority, but a huge percentage of the amount of money that we spend as a country every year, at least in America, and but this is relevant for every country on earth, goes towards treating people who are already sick with chronic conditions.

And if you could stop those things from happening in the first place, this is maybe what allows Medicare to even continue to exist in 10 years because it's going to go bankrupt at this point, right?

This is, this is a huge deal.

But preventative healthcare is really hard.

Some of it's easy, right?

And for example, example, you know, women go in and get mammograms, right?

And you have preventative screening for things like cancers.

But imagine that right now, all three of us are like, this is scary.

I don't want cancer.

I want to go in and get a scan, scan my whole body, see if there's cancer anywhere.

Well, that's not feasible.

When you go and you get a CT scan or an MRI, like we talked about earlier, you don't want to CT scan your entire body.

That's radiation.

You don't want to fill yourself with radiation just maybe in case you have a problem.

MRI machines, expensive, difficult, slow.

You don't want to MRI your entire body.

That's not protocol.

Even if you convinced a hospital to do it, right?

There's fields of somebody who looks for tumors in the brain specifically, tumors in the pancreas specifically, tumor in the lungs, tumor in the, in the abnormal, whatever, right?

So you would have to then have a variety of specialists all individually look over this giant amount of data that you produced about your own body to be able to tell you whether you maybe have cancer right now and you probably don't.

Doing that one time is completely infeasible right now.

Doing that every six months, basically impossible.

But I'm sure you could pick up from that.

Well, if it's about processing data, that is exactly what AIs can do.

There is no reason why long-term down the road, you couldn't get a full body scan or some kind of imaging or information about your body every six months.

And an AI has been trained on all the different potential diseases, problems, things that could come up, early onset Alzheimer's, or early tumor markers, or early hypertension, or whatever it is, right?

All these things that could be scanned that is completely infeasible right now, which suddenly you can imagine, wait a minute, if we had an AI trained on this and we expanded the resources for scanning people regularly, this could massively solve what we're dealing with and reduce the number of people who are just going to the hospital because they're fucked, you know, and get that down to most people are preventing things before they happen.

So there's two examples of this happening right now.

One is Gallery.

So this is a blood test.

company.

So their mission is basically to do that.

They want to do a blood test that can scan for cancers and let you know if you're at risk for cancer and if they're they're seeing early onset cancer.

This is in clinical trials right now, so it's not like out in the world.

But there's like 100,000 people in this using this right now in the UK specifically to get data to show and refine this.

And this is an AI analyzing data about the bloodstream.

Specifically, it's looking at fragments of DNA that come from cancer cells and using that to identify what we never would have been able to before and say, hey, you actually have this, this, and this cancer that is starting to happen.

Go into chemo now.

So it's hundreds of thousands of people taking regular blood tests.

The AI is trained on all their data and knows that if your blood looks like this, it's very similar to someone else's blood who had cancer that looks like this.

And we've made this connection and that you can.

And a kind of thing to note here is it's, it's, in this case, it's not even just, oh, it's doing the work that a human could have done.

Analyzing CF DNA is not, from my understanding, not possible right now.

That's too this is new.

Right.

And this is basically like, this is a data source that we didn't even have the ability to really use in a meaningful way before, certainly not at scale.

But given that AIs can, you know, basically learn to be nearly as good as a human and then apply that knowledge at 10,000 times the speed of a human, well, suddenly this is a data point we could actually use that could save literally tens of millions of lives or get people started in cancer treatment early.

Maybe that person who's about to die of prostate cancer, they learned about it five years earlier and they were able to apply treatment earlier, right?

You know, I can't say that

the exact, you know, because I'm not familiar with all the specifics, but my

mom passed away when I was like one years old from cancer and she was like 29.

And I wonder, you know,

I have a great life.

I have a great mom.

I'm not asking to change the past or anything.

I'm just curious, like, there's, and, and I have two other family members who have died of cancer.

Uh, and I wonder, you know, is this like the difference maker for for a lot of people?

Like, just them catching it earlier.

Because she, I, you know, her and then her dad, my grandfather, who I never met, they both died before they were like 40.

And that's, uh,

and, and, from like something that was just rare and like hard to find and late stage.

And that's, that was it.

So at that point, again, you got limited options.

And so I experienced the same thing very recently.

And for the last couple of years, I've been going through the logistics of that.

And it's fucking brutal.

I mean, it is so emotionally draining and just.

And obviously for the person going through cancer treatment, it's just absolutely fucking brutal.

And the idea that you could prevent that from happening, or at the very least, you start somebody treating, you know, a pancreatic tumor when it's just starting, right?

And maybe chemo, a round of chemo every six months, is able to keep it abated before it really grows, or you're able to hit it with radiation with a smaller amount of radiation.

So, another thing I wanted to talk about, there's not really time.

Another area that we're looking at that, so this is from again, Crafted Robot who works in radiation in the NHS, is right now we shoot a beam of radiation at the tumor, right?

You have to make it a really wide beam because if a person has has like if a person's body has shifted from the previous time they came in and they did the scan let's say that day their bladder is more full or they've been working out or whatever something has changed in the body those millimeters matter and so they have to like make the beam of radiation wider in order to ensure that the changes in the body

They're still going to hit the tumor, right?

And there's multiple factors that basically make them keep widening the thing to account for possible changes that have happened.

And if an AI, and this is what they're starting to look into now and could easily happen in the next five or or 10 years, if an AI is able to provide the information to, for example,

the day a patient is screened in an MRI machine, they get out of the MRI machine or maybe stay there, and the AI does a full analysis and recommends a treatment plan that can then be reviewed by doctors within two hours.

And the treatment happens that day, the beam of radiation, again, imagine you're shooting a gun into your body at the tumor, like you can make the bullet a lot smaller, right?

Now they have to use giant ass bullets and you're filling the body with a ton of radiation that, again, can literally cause cancer and cause all this damage it is super bad and the more refined you can be and more targeted this is a huge huge difference yeah um and so this is like very feasible in the next few years and then these other areas we're talking about a preventative medicine light gallery there's also a startup that i was looking at called superpower their whole thing is they want to have this six month every six months you get a lab test and it is analyzing a hundred different biomarkers and gives you this comprehensive look of here are all these risks.

And again, it's AI processing this data and being able to do this.

And again, this isn't feasible for every person on earth.

Even if you 10x to the amount of doctors in the world, it's still not feasible to do this at scale.

But if suddenly the time that's taken and the sheer amount of data analysis gets reduced to like almost instantaneous, this is opening up all these new incredible things.

There's a real world where in five to 10 years, many of the debilitating diseases that have killed so many people we know in our lifetimes, you now know five or 10 years ahead of time and can start treatment way earlier.

And in some cases, it never grows to a deadly point.

That sounds incredible.

I guess here's my feedback.

We haven't even gotten to AI curing all disease, by the way.

That's chapter three.

We're almost there.

All right, I want to give my feedback is that in a public health care system like the NHS, I can totally see how this works.

I'm already seeing, I guess I have fears.

Like if I'm taking a blood test every six months in an environment where my employer is paying for my health insurance and they find out that I now have early stage pancreatic cancer or something, their incentive is to drop me from their coverage.

You know, or there's some, I guess that's my,

this feels like this works really well if we've all agreed to subsidize healthcare.

Now I'm,

it is, this is all concern.

It's more concerning in America's.

It's just more, yeah, I understand America.

I mean, this is America's fault.

It's nothing to do with AI.

It's just like there is, there's some dream.

So, you know, if, and, so again, I think there's going to be need to be a lot of government regulation with all this stuff.

And I personally am somebody who would rather rather have a national healthcare system, not stoked about the free market for medicine.

But just a counterpoint there is like, ultimately, health insurance providers would rather have healthy people, right?

And so if broadly the government says, hey, you have to apply these preventative screenings to all these people.

And that means, yes, 10% of your insured clients, turns out they're high risk for these things.

You are not allowed to drop them based on this.

But the other 90% are now far less likely to need medical, expensive medical support.

That might be a net positive for an insurance company, right?

So in theory, the overall net gain of people being healthier and overall cost dropping would in theory allow a health insurance company to actually spend so much less on the average patient.

Just because preventative is so much cheaper.

Right.

Because preventative medicine is so ridiculously impactful, but just not feasible in many ways right now in our healthcare system.

If you could introduce that into the system, bring the average cost necessary for a person way down because you've prevented all of these different issues.

Um, in theory, that works out.

I'm not going to pretend like the health insurance industry is some.

No, I mean, I hate the health insurance, right?

Yeah, but I, I am, I'm thinking at the end of the day, you hate them so much, you might even take action.

It's, I mean, who knows?

I,

well, I, it's interesting because when we're looking at, uh, I think when we're talking about preventative

measures to combat something like cancer, right?

That is very different than the graphs we were looking at before.

If you look at the chronic diseases that were listed out, I mean, it was showing like chronic

disease in the United States, right?

We can we could be honest, obesity, smoking, most of these are from being

very preventable stuff, yeah.

And and but those are not like genetically, oftentimes, not genetically predispositions or have strong genetic

dispositions towards those things, those are like uh

environment environment and like

behavior conditions, right?

Like the one of the reasons that Japan's healthcare system

spends so little money compared to like the US's, right?

There are a lot of factors at play.

Bushido spirit.

That's what it is.

It's mostly the Bushido spirit.

But even compared to maybe other

like nationalized or like public healthcare systems, right?

Japan spends

not that much money.

And one of the reasons is that they have a really healthy population to begin with.

They don't eat burger.

And they eat less burger.

And

I want.

Is that really living though?

I'm posing on the bottom.

Are they all strapped?

I'd rather fight on my knees.

Yeah.

What is it?

I'd rather fight with my mouth full of burger.

I'd rather die on my feet than live on my knees.

I just see like the application in an American context, I feel like, from what you're saying, is so much of the

care after the fact is what can be helped.

Like dealing with the fallout of these conditions and helping speed up the process of like going into the doctor, that feels like the American proposition.

Also applies everywhere else as well, right?

But there are,

if it's like, if I just choose to like, you know, not exercise and eat a bunch all the time, and

I, you know, the AI isn't helping helping me there.

And that is such a strong amount of this disease.

Or if I'm addicted to cigarettes, I'm dealing with nicotine addiction.

I just like,

I do feel like so many of the conditions that people are dealing with and the health problems we're dealing with and the scale at which we are dealing with them have preventative measures as far as AI catching them.

I feel like this is a smaller piece of the puzzle than the part you were saying earlier.

It's like, to me, the appeal is like

curing the incurable, approaching things like cancer that have,

or helping call out things that are like genetically

more likely to be dealt with like later in my life that might be popping up.

Like, those are the huge things to me.

But AI isn't going to solve like, I don't really feel like AI is going to solve the amount of people being diagnosed with heart disease because that's tied to just other things.

It will not.

And I'm not saying that.

I mean, it could tell you have an increased risk for it or something and maybe scare you.

Yeah, they can tell you there are genetic factors in whether in those things still.

I'm not, I'm not ignoring that.

So, something from this article that's relevant to what you're saying, it was a quote I thought was interesting.

Research suggests that clinical prevention services reduce disease, disability, and death.

For example, counseling all smokers, just counseling, counseling all smokers on a regular basis could save roughly 70,000 lives a year.

Screening all persons age 50 and over with a fecal occult blood test and a sigmoidoscopy could prevent 18,000 deaths per year.

Sort of a sigmoid myself.

Yeah.

And so I think the, you know, part of this is just, again, preventative medicine is so helpful of counseling people.

And I'm like, so even if like, if I were to give an example, even if I were addicted to cigarettes, right?

You, by like talking to me or interview, intervening so much earlier using machine learning in some capacity might still provide a benefit down the line.

For me personally, I'm making the argument, one, preventative medicine is extremely important and impactful.

If we can get to problems earlier, that's a huge deal.

I am mostly focusing in this conversation on like serious disease, like Alzheimer's, cancer, things like this.

There's then a second part of this, which is, okay, America objectively has horrific obesity problems that are causing enormous problems.

And a lot of other places, too.

I can absolutely imagine a world where, you know, you have an AI health person that checks in with you once a month that the doctor oversees, but that way people who are more receptive to the stuff or need encouragement or need more frequent guidance or whatever else can be really helped by that.

In the same way that somebody who gets a personal trainer is more likely to go to the gym every week, right?

If they have some obligation.

So I imagine there's some world like that.

I'm certainly not claiming, though, that AI is going to fix

the obesity or whatever.

Yeah, that's obviously that gets to, you're totally right.

That gets to much bigger questions around.

what's the quality of our food in America and our diet and the way all these things are incentivized.

Cities, like there's so many things that need to be done.

A lot of stuff there.

Absolutely.

So not making claims in that specific stuff.

This is more like the hospital medical system, how do they treat serious disease?

And that preventative is going to be really impactful for certain one of the deadly diseases, basically.

One thing you

think maybe that fits into the more of the first chapter of what you were talking about, too, I saw was

similar to law firms.

This is something that just helps with like bureaucracy and paperwork.

Like you can use things that just make that part of the process more efficient.

And a lot of what you might spend doing as a healthcare professional is like writing up, you know, or going through

the meeting with the patient you just had, summarizing everything,

needing to communicate information to other doctors, like all of those things that you spend time in between actually dealing with patients.

That's like half your time.

That's so much of the time spent.

Yeah.

So can you tell us what about the cures for all diseases?

The miracles

go.

That is currently 10 years away.

You know all this shit we've talked about for an hour, 15 minutes?

None of it matters.

We're going to fix it all.

Okay.

The real holy grail is we just don't even need to treat cancer because we cure it all, right?

That's the holy grail.

Okay.

Finally, we get back to my naive vision of what this is.

I'm listening.

And I'm giving the Doug Doug stamp: no diseases, and everybody is a perfect life in 10 years.

And it's all thanks to AI with no regulation and no asterisks.

Right.

And no asterisks, and there's no nuances.

2035.

Okay.

All right.

So, um, why is disease hard to figure out?

Why is it hard to cure disease?

That's sort of the question.

Yeah, yeah, yeah.

Doctors are allergic.

Yep.

All doctors are allergic, by the way.

You stole my first answer and my second.

My third is that presumably just because

disease changes in so many ways over time, like there are so many

things mutate, like whether they be like bacteria or viruses or your body and cancer cells change.

Like things are just constantly changing.

Things are not necessarily the same thing in every person all the time.

You're not finding one

all-star.

A plus, my next student, what is a protein?

Powerhouse of the cell.

That's not correct.

That's not correct.

Okay, so I'm going to super simplify things.

You know, disease, some kind of problem is there's some kind of,

let's say, reaction process happening in the body that you don't want.

Proteins are like the building blocks of biological life, basically.

They're the workforce.

And you can think of a protein as a literal 3D structure.

Imagine like making origami.

And you, you know what?

I actually, oh, I brought up this.

This fidget toy.

I forgot about this.

Like, imagine that a protein is a whole bunch of amino acids, okay?

And you fold it into some weird shape.

And now that's the shape of the protein.

And based on its shape, that actually, that dictates.

largely what it's going to do because it'll go around the cell or the body or whatever.

And if it finds other molecules or DNA or proteins or whatever else, it can click into that and they they lock in like a lock in a in a in a door and a key

and then and then they start making magic they start porking and they do all this cool stuff okay um when i drink a protein shake that's what's that's what's going on that's what's going on in my body okay so a lot of uh of biological function disease comes down to proteins that it is a major part of of the biological process and so if you are trying to do to fix a disease let's talk about malaria malaria is i didn't even know this before right now.

It's a parasite.

It's like a little bug that gets in your body.

I mean, not a bug because it's a single cell, but it's a parasite that gets into your body.

So malaria kills something like 100,000 people a year, 200,000.

It's an insane thing.

There's some insane stat.

It's like everyone,

there are more people that have died of malaria in history than like the amount of people alive right now.

Oh, I'm sorry.

I'm going to add something to that.

Sorry, 627,000 people a year.

My bad.

I was a little low.

That was in 2020.

So

it is, malaria is wildly deadly.

This kills literally hundreds of thousands of people.

Mostly children under five.

Mostly children and mostly in Africa.

This shit is fucking brutal.

And we don't really hear about it anymore because for us, I know it's almost like smallpox or something.

We don't really deal with it much in the West, but this is a big, big, big deal.

Also, there's been like pretty substantial.

My understanding is that number is actually a lot lower than it was even 10, 20 years ago.

Makes sense.

Because we currently have like okay vaccines that are like 30% helpful.

So let's start with malaria as our grounding point of like, how do we cure all disease?

So, malaria is this parasite.

It's, it's transported by mosquitoes.

The mosquito, you know, bites you, the malaria gets in, and it starts basically growing and developing in your bloodstream and your cells, but it's, it goes through this whole life cycle in your body and it changes rapidly.

So, let's say you want to design a drug.

And just to simplify it, imagine, you know, you know what the parasite roughly looks like.

And you're like, we need to design a drug, a molecule, something that's going to go into the body and it stops the thing.

It recognizes that this is a parasite and it stops it.

But right now, the parasite has so many different forms that it goes through.

It goes so rapidly.

It hides in blood cells.

It's extremely hard to understand the physical nature of the parasite to the point that you could design something that attacks it.

But these guys who are developing a malaria vaccine realized that it could make a tiny bus and it would have a bunch of, it'd have a class of school children and then they'd go in and explore your body and find them.

Kind of like osmosis children.

Oh, I thought you were sending, we would send a bus bus to Africa.

And I was like, that won't help.

I was talking about the magic school bus, but there is a surprising amount.

There is a surprising amount of children's media where tiny ships are.

Oh, guys, go ahead.

That's interesting.

Okay, so let's say, you know, we are humans and we want to understand what the hell is going on with malaria.

So you start looking at this malaria parasite.

It's tiny.

And you realize that there's all these proteins around the body of the parasite, right?

There's a shitload of them.

And you can eventually look at and understand that on the gametes of the parasite, which is basically the dick and balls,

that there's a certain protein.

Socrates in the rat.

Let me actually, let me get the number

so it sounds like it's not.

Dude, the dick and balls.

What's the number?

Is this one also made by Siemens?

Oh,

well, they're making Siemens on the PFS4845 protein.

So basically, imagine you're looking at the parasite and you're looking at his dick and balls, but it's blurry.

And you're like, okay, we know that there's a protein there, right, that's on the body of the parasite.

This is the protein it uses to reproduce, right?

If you could, in theory, put a condom on the dick and balls,

it would no longer be able to reproduce.

Right now, it's incredibly hard because this thing has all these different life cycles in the body that it's going through.

But if you could stop that, it just can't reproduce.

Rather than saying, hey, maybe we can stop it at this section of the liver, you say, okay, what if we can identify that, stop that specifically?

And again, if you think about a protein like a physical shape and you need to find, you need to design a key that goes in, binds to it and blocks it, you can stop them from getting laid.

They've done this for actual mosquitoes, right?

Don't they?

Like they irradiate a batch of only males so they're infertile and they send them out into the world and then they fuck, but then they, there's no babies produced.

So I want to give

some, my thoughts on this, but somebody in the comments is going to reply and be like, as a mosquito penis biologist, what Doug said was wrong, and I'm not going to speculate.

I would love mosquito penis biologists.

If there's any mosquito dick biologists, our viewers are mosquito penis biologists.

They certainly have mosquito penis.

It's the same guys who have student loans in Australia and live in Vienna.

They're studying mosquito dicks.

We're looking for just a miracle, Lyserius.

No, no, no.

It's one guy, and we're asking that guy to go expand your horizons a little bit.

Stop being selfish.

So this is like fundamentally one of the core things that goes on with drug discovery is you are trying to figure out like, what is the protein?

What is the molecule?

What is the thing that is causing a function that is bad?

Here's another example.

In a tumor in the body, right?

This tumor, a cell, again, is a cell that just keeps dividing rapidly.

And the problem is that it looks like one of our own cells.

So it's hard to determine that it's bad in the first place.

But technically, it's got like...

It'll overexpress certain proteins.

It'll overexpress certain antigens, meaning that literally on the surface of the cell, it has more of these proteins like embedded into it.

Like it's like one of those, you know, candies with like nerds on the outside.

A nerd's gummy cluster?

It's like a nerd's gummy cluster.

So think of it, think of the cells as those dangerous to eat?

Is that a disease now?

Well, they're proteins.

Just don't put them near your gametes.

Well, okay.

In your case, you're small gametes.

I wasn't thinking about it.

My gametes are so big they wouldn't even,

you wouldn't even notice a nerd's gut.

I want to hear about gametes.

Bro, can we move on from your gametes?

You bought it out right now.

You bought my gametes.

Okay, so this is like fundamentally what goes on in a lot of drug discovery is you understand a function is happening or like this is what a thing looks like that you know is bad, but you need to teach the body how to recognize it.

But this is very hard.

To determine the structure of a protein, you have to do something called x-ray crystallography or something like that.

I don't exactly remember.

There's some old

some bullshit.

I don't know.

Some science-y shit.

But essentially, up until the past few years, the only way that we could figure out how to figure out the structure of a protein is by this extremely meticulous kind of guess and check process where you're crystallizing the protein into a certain structure in a lab, and then you can be like, okay, this is pretty much correct.

But that is extraordinarily hard to do.

And so I actually want to go to the

cell

back in the day, you could submit your PS3

to help folding at home, where they would do this.

You could have your PS3 running all day to help.

test and check these crystallized

but yeah yeah i mean this sounds like alpha fold which is like some new

way better way of doing it, but used to be able to.

Yeah, this is this is an image right here.

If you pull this up, like this is the potential structure of a protein, right?

This shit looks, this is crazy, right?

These are these crazy ass, these, these structures, again, that all kind of like fold together in this crazy way.

And so the way we had of figuring out structures like this is this extremely tough, laborious process that you're talking six, 12, maybe 24 months to figure out the structure of a protein.

Introduce AlphaFold.

AlphaFold came out, Alpha Fold 2 is in 2020, I believe.

The newest version, AlphaFold 3, came out last year.

This was so impactful that they won the Nobel Prize in Chemistry for this.

AlphaFold is a product of DeepMind, which is a subsidiary of Google.

So this is basically coming from Google.

And this is kind of like one of their labs, almost like Bell Labs, where they're just like cranking out crazy AI shit.

And their goal is literally to.

cure all disease.

And so what they have been doing over the past few years is building a deep machine learning system that can learn to figure out just from the ingredients that go into a protein, which is like a list of amino acids, understand how it would fold into crazy ass shapes like this.

So it just gets the bare, the kind of like, this is what it's made out of and can actually determine this shape, which are crazy.

These are, you know, these crazy things.

The way it was able to do that is because there's like the protein structure data bank, which was a giant pool of data all these companies have put together.

But they were able to make the system actually just from some basic information about the protein, like the amino acids, determine what this thing actually looks like in detail.

So you can imagine now, going back to the malaria example, if you've been trying to make this malaria vaccine and you know that Dick and Balls has the protein that if you stop that, you cure the whole thing, but you don't know what it looks like, there's all these challenges that arise with trying to figure out the exact shape of the protein on those gametes.

AlphaFold comes in.

You can just feed it the amino acids.

This did literally happen.

This is going on with this malaria vaccine.

And basically a years long problem of trying to figure out what exactly this protein is, so how they could design a fix for it so that something can come in and stop it, is suddenly done in like weeks or days.

It's something ridiculously short.

I don't know if they said the exact timeline here.

I don't remember.

So, this is like a massive breakthrough.

Suddenly, one of the key pieces of the puzzle is now visible.

We have the structure for it, and it was all made in a few minutes by a now open sourced system.

It's shared with the for non-commercial use by Google that can dictate and has now uh it's something like hundreds of thousands of protein structures.

Oh, no, no, hold on.

Uh,

it predicted 200 million new protein structures in one year.

So it's not like, oh, this is cool.

It's kind of helping with malaria.

It's like the type of things that were holding us back from understanding processes and understanding how to attack certain systems went from a year-long process to now basically instantaneous.

The ability to now start pursuing drug discovery and drug cures and figure out what's going on in a system just went from like an extremely slow, specific thing to like, anybody can do this anywhere.

Not only that, AlphaFold 3, which is what came out last year and won the Nobel Prize, it doesn't just predict the protein structure anymore.

It also then simulates how that protein structure will interact with other proteins or with molecules or with DNA.

So if you're a drug designer, if you are, for example, trying to come up with a drug that's going to, let's say, attack cancer cells or Alzheimer protein or something like that, before you had a blurry image of what the protein looked like,

you came up with an idea of like, okay, here's my image.

Here's my idea for what a structure would look like that's going to come in and attack that thing.

And you kind of guess and check.

You then spend six months crystallizing your idea of what might work in a lab, then put it together with the, you know, with the target and see what happens.

If it doesn't work, back to the lab.

You're back to another like one year iterative loop.

Now, AlphaFold 3, you do that digitally in minutes.

So a drug designer going from years for every single iteration of a test to see what might help with malaria or what might help with cancer or it might help with any of these other things is now in minutes able to put it through alpha fold and say here's my idea for what might work in on attacking the malaria parasite it simulates it goes that didn't really work and you adjust from there and you're doing cycles at literally i don't know what the math on that is i mean that's the magnitude is insane that's you're saving so much time in the time scales you're talking about unbelievable amounts of time and not only that you're making it now so many more people can participate in trying to come up with cures for things okay you don't have to be a lab that has x-ray crystallography i see how you're arriving at your 10-year conclusion because this is such an escalation at which you could you can search for these answers like it's so

dramatic of how we approach these things that it is like almost unbelievable yeah that is that is so sick i i i mean do you

come on you might need to steal this villain chair for a second i don't sure yeah i will yeah

well the parasite the malaria is like a living being, and maybe it should have a right to kill

me.

Ask the malaria what it thinks about this.

And there's more I'll kind of go on, but yeah, initial thoughts from you guys.

Here's my, I mean, I actually think this is incredible.

I really have no negative thoughts, but I'm going to steal man or whatever.

I'm going to try to get you to.

Yeah, yeah.

Okay, so this is from DeepMind Google website.

Yes.

They have an incentive to make it seem as cool and dope as possible.

Yes.

I've seen many technological advances on company websites that sound fucking incredible.

And then we flash forward a few years and nothing's fundamentally changed.

Yes.

Yes.

And we feel like, okay, where did that go?

Where was that thing that was supposed to do?

X?

Yes.

That.

I think that skepticism is

super fair, right?

The incentive structure to present these things as world-changing solutions as soon as possible is something I totally understand.

You know,

I have read,

you know, when a blog post about some new blockchain thing and this is how it's going to change the way we interact with each other.

And now, I get that part of it, but to me, the part that is more believable about this is like maybe AlphaFold isn't quite as effective, or there isn't all the negative aspects of what they're explaining on this post.

Maybe that's true.

But the basic idea of using machine learning to shave down

the time it takes to analyze data to cut down on something like revising your

approach to curing or attacking

like the malaria parasite.

However, whatever the type of disease, whether it be a parasite, a virus,

or something else, that

just holds, that core idea holds very true to me.

Like that is, my understanding of machine learning, even before.

uh ai was like a big craze is like this is the type of functionality it would be particularly good for it can just cram all of this time it takes a human to do something into something so much shorter.

And we can more efficiently trial and error our way through solving diseases.

And that part of it seems incredibly believable to me.

And that is the part that I have faith in.

It's like, maybe it's not alpha fold

at the forefront of who ends up being.

the critical piece of what it takes to attack these problems, but this is an example or a first mover in this.

And dude, this is this is kind of what I was hoping for.

It's like, like, this is the type of stuff that I think is so, so sick.

It's so much more.

It's like, I would give up being able to ask my phone passing questions about

language or research projects or how

I feel today for this.

This is, to me, like the most important thing.

Yes, I by far agree.

And actually, Dario Amadai, who I'll talk about briefly later, because we've been going on for a while, that's also really what he says.

It's like, look, when you get into medicine and biology, this is the most clear cut of like AI is going to dramatically improve our lives because we're not talking about, oh my God, lawyers are going to have this fucking AI chat bot.

We're not talking about ChatGPT.

We're not talking about AI arts, all this stuff that has a lot more complexity.

This is like,

we're probably going to cure so many diseases.

Demis, the CEO of DeepMind, who's running this and again, won the Nobel Prize for Chemistry last year for this.

Like there's broad scientific acknowledgement of how incredibly impactful this is.

On average, it takes 10 years and billions of dollars to design one drug.

We could reduce that down from years to maybe months or maybe even weeks, which sounds incredible today, but that's what people used to think about protein structures.

And there's all these examples.

Like he's the one who said, it'll, you know, in the next five to 10 years, we'll have systems that are capable of not only solving important problems or conjectures in science, but coming up with them in the first place.

It'll revolutionize human health.

I think one day within, we'll cure all disease with the help of AI.

And he said, I think that's within the reach.

Within the next decade or so, I don't see why not.

So some of the smartest people on the planet who are winning Nobel Prizes for their work are like, this is so dramatic of a leap forward in drug discovery and testing and improving that we might literally cure 95% of the things that have killed billions of humans, right?

And if, like, and again, if you think of your whatever, close family member, and if they come down with some disease and then this allows us to cure Alzheimer's or cancer or whatever else, like, holy shit, that's a big, big deal.

I would, I would give, I would also give up everything else with AI if I could have this.

To address what you were saying briefly, yeah, there's absolutely, absolutely, this isn't some holy grail.

First off, acknowledge, want to acknowledge AlphaFold is not perfect.

It's just really good.

So even in this

post, right, they talk about how it helped the humans, but it was this major leap forward that probably would not have been possible before, right?

So it'll continue to get better though, over time.

Yeah.

And then the other thing is, again, you know, I think the government needs to get involved with this stuff.

As much as I generally do actually have faith in Google, they've shown to be well-intentioned with a lot of things like this.

I wouldn't trust them.

I don't think they should be the ones, you know, hallmarking this stuff.

And so it gets back to the abundance thing of I think the government should be incentivizing centralized development of this stuff.

But also, it's incredible that private companies, this is a private company that did this and has been working on this.

And they're continuing to, like, I listened to a podcast by the CTO of one of the kind of twin subsidiaries that work on this stuff.

And he's like, our goal is to have like five to six more of breakthroughs of this scale soon.

And we think that will basically, you know, 1,000x drug discovery.

And there's a lot of just thinking around that.

Like another from Dario, who's the CEO of Anthropic, it's my guess that powerful AI could at least 10x the rate of discoveries, giving us the next 50 to 100 years of biological progress in five to 10 years.

Like the rate is just going to skyrocket.

But also, it's challenging.

Yeah.

And

I mean,

I'm so,

I also want to give just really quick, people who are like, Doug, you're way too optimistic about AI.

This is why, by the way, this is the stuff that gets me excited.

It's not ChatGPT.

ChatGPT is really cool, I think, but this is the shit where I'm like, oh my God, we might eliminate cancer.

Let me actually quickly go through a cancer scenario.

So right now, a, you know, early stage treatment for cancer is called T cell therapy.

It's actually CAR T cell therapy.

So again, the core problem with cancer is it's from your body.

It's your own cells that have mutated and they're, they're dividing, they're creating too much of themselves.

So the body isn't good at recognizing this is a bad thing that needs to be taken out.

So, what you, in theory, could do, though, is you could take T cells, like you know, immune system cells out of the body that are normally supposed to attack cancer cells,

and you train it to look for specifically what the cancer cells look like.

You put it back into the body, and then they go in.

It's like you're, you like pull out soldiers from the body, and you like send them through like seal camp, like boot camp, and you train them specifically to go fuck up

tumor cells, and then you put it back in.

That is working right now.

Um, I don't know.

Oh, this is CAR T-cell therapy.

So it's called chimeric antigen receptor therapy.

It's named after Hunter Hunter.

And it's literally what they do is they grab a T cell.

You can see here on the left, if you pull it up, grab a T cell from your body.

They reprogram it, basically add a little thing to the outside.

They like jam a little key on there.

Then you send it back into the body, and that key fits into the tumor lock.

And fucking the body goes, oh shit.

All right, we got to cure this.

And it goes and like expands and works.

And this is working right now with leukemia and lymphoma.

lymphoma and it's full on like curing somebody

you want to hear something bad about this

not bad that the therapy is the therapy is very cool that like the functionally but this is something i learned about car t cell therapy about a year ago because i was talking to my doctor friend about what are some fucked up things in the medicine industry and the five so there's five pharma companies uh that have the basically that do car t cell like treatment and the five there's like five different types of the treatment that you can do, depending on, I think, the type of like cancer you're fighting and the type of treatment that you need.

Okay.

And these five pharma companies agreed that each one of them would only do one of the therapies.

So they have a full monopoly on each of the therapies and they can charge as much as they want.

I learned that.

And that's how I learned about CAR T cell therapy.

Yeah, that's some bullshit.

Yeah, I mean, this is, this is, well, so it's awesome.

Yeah, so let's talk about this.

This is right.

It's cool, right?

It's the idea of you take immune cells out of your body, you program them to attack the tumor, and then you put them back in.

But this has the same problem as any other cancer treatment, which is you're the cancer cells look like healthy cells.

And so this same treatment will go attack, it goes into your bloodstream and it helps blood cancer, but then it also attacks healthy parts of your bloodstream.

And my understanding is like you can survive this, but then you need blood transfusions and whatever else because it's killing a bunch of healthy stuff.

So even with this, this crazy ass therapy where we're pulling out, you know, the soldiers of the immune system, we're training them to look for a specific thing and putting it back in, conceptually, that could solve almost anything, right?

If you teach the immune system how to go look for this certain type of thing, that's what a vaccine is, right?

That's you're teaching it, okay, this is what to look for.

And we're manually fucking grafting it like Elden Ring onto the T cell's body and then putting it back in.

But cancer is insanely hard.

And this might work for blood cancer.

But, you know, if you have, again, like pancreatic or

or prostate cancer or whatever it is, every cancer not only is it going to be different they're like morphing they have all these different ways of expressing themselves they mutate and so they have different looks one of the things you can do is they overexpress meaning on the surface of it they have way more proteins or antigens than they should but that's hard to detect of like oh it's more than another cell right all you can kind of do is say like you do or do not have this thing but it's hard to tell oh you have too much of this thing and so the same type of treatment is not really feasible right now because we don't have a way to to realistically design the super, super precise type of soldier in the immune system that can do all this stuff.

But again,

that's not impossible.

So imagine a world in 10, 20 years where you get cancer and they pull a biopsy of the cancer cells.

They look at the tumor cells.

And while a human couldn't do this, an AI analyzes the tens of thousands of different proteins on the surface of the tumor and goes, we've actually figured out a pattern.

Then, once determining that concrete pattern that makes the tumor cell distinct from any other type of cells in the body, you then have the AI design a molecule using tools like AlphaFold to say, we're going to design a type of T cell receptor that is specifically going to fit on this lock, even though that's insanely complex, but we have the data and ability to do it because it's an AI.

We put that onto the T cells, put that back into the body, and it fully kills the cancer.

That is that's feasible within our lifetimes.

Absolutely.

And the challenge here is like the immense amount of data complexity complexity and processing and the amount of time.

But if all of these tools are able to be incorporated into an AI system that

can, for example, actually crystallize these car receptors and put them on the thing.

So the timeline is also reduced.

Like this is, we could cure cancer straight up.

And you can imagine this happening for everything else, all the complexities of diseases that we currently deal with, like Alzheimer's, where we kind of don't really know what's going on.

If we can crunch the numbers on what's going on with a sample of neurons, an AI could identify a specific way to actually attack it.

And this, because of AlphaFold last year,

this isn't fantasy stuff.

This is like, yeah, we see a path to this right now in our lifetimes in 10, 20, 30 years.

I mean, okay,

this is the update I was looking for.

Something that, you know, what is the concrete progress that's being made now, even at the tail end and what we could call chapter three here,

where there are companies that are sort of edging in this direction or introducing technology that is is chasing what you're talking about is so significant to me.

Because even

in my version of what I was hoping for coming into this, I thought a lot of these things were further away.

I didn't know any of the companies that were already working on a lot of these solutions going into this.

And yeah, this is to me the holy grail of like what machine learning or AI is supposed to be about.

This is what you're, this is what it's the end game of it.

It's not to like replace humanity.

It's not to create funny pictures of Spider-Man.

It's to help protect people from disease and suffering.

And then put a funny image of Spider-Man onto the T cells and it goes into your body.

The protein's actually got a tattoo of Spider-Man.

So this is, I mean, this was really wild and it makes me really hopeful.

I do think a lot of the, you know, brief downsides that we pointed out along the way,

I'm curious what other people have to point out here, especially people that are involved in like the medical or research industry around these topics.

If you have anything to contribute more to this discussion, I'm curious what you have to say.

I think to me, most of the downsides that we're talking about doesn't really have to do with the functionality of the technology.

It doesn't have to do with like, oh, what are the, like, am I worried about it taking over the human soul or something?

Like, I'm not worried about that.

No, no, no.

I'm worried about basically just monetization and people's access to it.

And

if we were to treat this in the best light and

imagine a scenario where these technologies are developed, distributed through systems that are at least better than the American healthcare system.

But even we talked about the NHS a lot in this episode.

The NHS has a ton of problems right now.

They're like one of

the

most struggling healthcare systems in like Western Europe.

So out

if we can under, if we can view this through the lens of these systems are going to be improved, we're going to make those things better.

And this is what is going to be deployed through it.

That's awesome.

The idea that I, I mean, one of my biggest fears is that I really, really don't want to develop Alzheimer's.

I think, and

I got way more scared about cancer the past couple of years.

I'm like, oh, fuck, I would really not like that.

And everybody in my cancer die, everybody in my family seems to die of cancer or Alzheimer's, which both seem brutal.

And it's like, yeah, and very real world that in 10 to 15 years, that's gone.

That being on the horizon for like you, your loved ones, like the kids you have, and the idea that that's something that those people don't have to worry about anymore, that's something that worth chasing.

That's like the great, the great human achievement.

Or even aging, starting to understand like, why does, like, you know, there's like tortoises that live like 200 years.

We don't, there isn't, we don't have to die at 100 years.

So like, as this stuff starts to expand, there's a very real world where we understand better like why do cells age and can we program that out of them is there a dna change that we can make and then south korea won't collapse that's true we keep every south korean alive and preserved they look like k-pop stars for 200 years in like a small booth with a star craft and a bts album and we just preserve them like yeah like the dire wolves in the uh in the compound

so i uh i think that's about time for the episode this week uh this is we tried something new we wanted to delve into like a much larger topic for the duration of the episode.

Also, this was way longer than I thought.

I thought this would be 45 minutes.

So I thought we would have time for other stuff.

And so I think in the future, like we'll, we'll try to make sure that the main topic, if we keep doing this, leaves a little more room to talk about like other smaller things.

Like me and Brandon brought like very small time things this week to kind of fill the gap of what we thought would be 90 minutes.

But this was really compelling and you broke it out in like a really interesting way.

And I think it's really important that, and kind of how you clarified at the beginning, I feel like because AI is such a powerful buzzword right now, people like because they feel negative about that and then the idea of attaching it to something sensitive like healthcare, there's this immediate like, ooh.

Yeah.

And again, none of what we talked about is fucking chat bots that your doctor is, you know, like that's, there's going to be a lot of obnoxious stuff in healthcare that may end up being really great, but in the short term, it's going to be really obnoxious.

But the good stuff is actually happening as well.

And this is really cool.

Really, really quick, just some resources if people are interested in more.

Dario Amadai, I think I've talked about this before, CEO of Anthropic, has a blog called Machines of Loving Grace, where he talks about his theory on why biology, health, and neuroscience will be impacted by AI in a positive way, which also talks about how AI will basically create researchers that help generate new discoveries like CRISPR or mRNA vaccines.

And that's why they'll continue to be really impactful beyond just image analysis.

And then the other one, if you want to freak out, AI 2027, I read this the other day.

It's basically a map of how the next couple of years of of AI development could go.

And this single-handedly made me much, much more scared of AI.

So if you want to be kind of freaked out and undo all of the positive good feelings you maybe developed in this episode,

you gotta, yeah, this, this article, Dario's article, will make you feel like, holy shit, we might actually achieve utopia in our lives.

And this one will make you think, I have two years left to live.

I need to go eat all my favorite foods tonight.

I want to tack something on the end here, kind of the last villain chair I could add to the episode.

I didn't bring this up earlier in the episode because as far as I understand, before AI or machine learning had hit more of a mainstream, this is already something that was happening, but you reminded me because you mentioned CRISPR.

I think there is something to be said here about when we're catching like diseases in advance where you're doing like analytics on like embryos and you start making choices about like the babies that can be born and can't be born.

And then those decisions

like kind of stumbling away from diseases and more into things that are like cosmetic or choices of how you want your baby to look.

I think that's like a really interesting topic.

To me, the reason why I didn't bring that up in this episode is I actually don't think that's tied specifically to AI because it was something that was already kind of happening prior to these things being mainstream.

And I do think that's a potential big downside or thing to look out for when we're talking about these sorts of technology.

I just want to bring that up at the end and maybe it's something we get to talk about on the Patreon.

Patreon.com slash EliminateStand.

If you want to join us for an extra episode every week, it comes out on Mondays.

Or if you want to to join our second tier, we have a book club that our first episode is going to come out pretty soon.

And the second book is already announced.

And we also have been doing a kind of a third bonus show where we respond like more directly to comments and things we feel passionate about.

So if you want to check those things out, join the Discord, talk about things there.

Atriarch, you got anything to?

That's it.

That was great.

Hey, drop a comment below.

Who do you think won the debate?

Cancer or humanity?

I think I'm locking it in for cancer.

See you guys later.

Bye, everybody.

Thanks.