AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: AI Tool: Diagnosing Brain Tumors Mid-Surgery

Welcome to the OpenAI podcast, the podcast that opens up the world of AI in a quick and

concise manner.

Tune in daily to hear the latest news and breakthroughs in the rapidly evolving world

of artificial intelligence.

If you've been following the podcast for a while, you'll know that over the last six

months I've been working on a stealth AI startup.

Of the hundreds of projects I've covered, this is the one that I believe has the greatest

potential, so today I'm excited to announce AIBOX.

AIBOX is a no-code AI app building platform paired with the App Store for AI that lets

you monetize your AI tools.

The platform lets you build apps by linking together AI models like chatGPT, mid-journey,

and 11Labs.

Eventually, we'll integrate with software like Gmail, Trello, and Salesforce so you

can use AI to automate every function in your organization.

To get notified when we launch and be one of the first to build on the platform, you

can join the wait list at AIBOX.AI, the link is in the show notes.

We are currently raising a seed round of funding.

If you're an investor that is focused on disruptive tech, I'd love to tell you more

about the platform.

You can reach out to me at jaden at AIBOX.AI, I'll leave that email in the show notes.

Brain surgery is a field where millimeters matter.

Surgeons often grapple with the very daunting challenges of discerning between healthy brain

tissue and malignant tumors during operations.

But thanks to innovative research from the Netherlands, artificial intelligence might

now be offering them a new powerful tool.

So researchers have unveiled a cutting edge, no pun intended, method that enables surgeons

to harness artificial intelligence to identify tumor types as reported in a recent study

published in the journal Nature.

So the technique involves AI analyzing fragments of a tumor's DNA, pinpointing specific chemical

modifications, and then very quickly providing a detailed diagnosis, even discerning like

subtypes of brain tumors, which I think is just incredibly fascinating.

So let's talk about some of the implications of this, obviously very cool technology, but

what does this actually mean?

So surgeons using this can now make more informed decisions during surgeries.

This is essentially they're able to determine how aggressive or the aggression level of

their operation based on the tumor's subtype.

So Drone Derrider, an associate professor of the Center of Molecular Medicine at UMC,

emphasized the importance of this technology saying, quote, it's imperative that the tumor

subtype is known at the time of surgery.

We have now uniquely enabled a very fine grained robust detailed diagnosis to be performed

already during the surgery, end quote.

So the technology named Sturgeon underwent some really rigorous testing and initially

frozen tumor samples from previous brain surgeries served as the test bud, where it correctly

diagnosed 45 out of 50 cases in just 40 minutes.

So this is very, very fast and, you know, has a relatively high accuracy rate for people

that are saying, hey, how come it didn't correctly diagnose 100% I believe in the future, we'll

get there.

This is just kind of the beginning of the technology.

I think when used during 25 line brain surgeries predominantly involving children, it produced

about 18 accurate diagnoses.

The remainder fell short of the confidence threshold, but its turnaround time of less

than 90 minutes holds the potential to revolutionize surgical decisions because typically this

takes much longer and is much more difficult in the current medical landscape through,

you know, genetic sequencing of brain tumor samples, which, you know, essentially offers

a more detailed diagnosis.

And while this isn't universally accessible, this becomes a really good option.

So even when available results, you know, can take weeks on this stuff as Dr. Alan Cohen,

a cancer specialist from John Hopkins puts it quote, we have to start treatment without

knowing what we're treating, which obviously is an incredibly difficult problem that we're,

you know, these people are faced with today.

So Sturgeon's method accelerates the sequencing process, focusing only on select parts of

the cellular genome.

This means results are available even before a surgeon begins working on the tumor's pedigrees.

So Dr. DeRider likened the models diagnostic capabilities to recognizing an image with

just 1% of its pixels visible, which is absolutely insane.

Of course, like all that being said, the technology sounds amazing, you know, but like

what's the catch?

I don't know if there's necessarily a catch, but what I will say is there's still a lot

of challenges around this.

So some tumors provide more difficult to diagnose, especially if samples contain healthy brain

tissue, intra tumor heterogeneity where, you know, parts of a single tumor differ in genetic

makeup can also make things a little bit more complicated.

So additionally, the expertise required for sequencing and classifying tumor cells is,

you know, it remains very high.

So Dr. Sebastian Brander from the University College London recently kind of made that very

clear.

However, I think, you know, with medical centers already implementing this method and seeing

some really promising results, I think it, you know, it seems the technology is gaining

a lot of traction.

So what is the larger mission of this all right, that's kind of the question I had.

And I think really what they're what they're aiming to do here is to bring molecular precision

to tumor diagnosis.

And I think in doing so, they will potentially be crafting more targeted treatments that minimize

damage to the nervous system.

So although bridging the gap between understanding tumors and effective treatment remains a challenge

as Dr. Cohen recently said, I think there's no denying the progress made in the diagnosis

sphere.

So while this technology is not perfect, the thing I always say is, you know, this it starts

out with something like this where, you know, it can diagnose, you know, 20 out of 25 tumors

or 18 out of 25 tumors, whatever.

And then it very quickly progresses where this technology improves.

And it gets to the point where it's, you know, it's 99.9 percent or 100 percent of tumors

are successfully diagnosed.

So I think it's a matter of time before we arrive there, a matter of time before this

technology is incredibly efficient.

And I'm very, very excited to see a lot of the progress that we're going to be making

in the healthcare space, thanks to AI.

If you are looking for an innovative and creative community of people using chat GPT, you need

to join our chat GPT creators community.

I'll drop a link in the description to this podcast.

We'd love to see you there where we share tips and tricks of what is working in chat

GPT.

It's a lot easier than a podcast as you can see screenshots, you can share and comment

on things that are currently working.

So if this sounds interesting to you, check out the link in the comment.

We'd love to have you in the community.

Thanks for joining me on the open AI podcast.

It would mean the world to me if you would rate this podcast wherever you listen to your

podcasts and I'll see you tomorrow.

Machine-generated transcript that may contain inaccuracies.