AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: Glass Health's AI: Revolutionizing Medical Diagnoses with Precision

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 will 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.

Back in 2021, the healthcare tech landscape saw a very interesting new company and that's

Glass Health that I mentioned in the intro to this.

So founded by Derek Paul, who's a former medical student at UC San Francisco and also

Graham Ramsey, who is an engineer at Woman's Health Company, a modern fertility, the company

initially launched as a notebook for clinicians.

So the notebook aimed to help medical professionals store, organize and share their approaches

for diagnosing and treating various conditions.

So Ramsey articulated that the original goal was to create a quote personal knowledge management

system for doctors serving as an evergreen resource throughout their career.

Now this is really interesting because you see a lot of the companies that are really

succeeding right now in AI are companies with data and it's really interesting because this

is a company, right?

It's from the very, from the beginning, they were collecting data, they're allowing doctors

to store a lot of their, you know, quote unquote, it's like doctors were able to dump

all of the everything that they knew onto a page and this is where they're gathering

a ton of data.

This is what Ramsey said, quote, during the pandemic, we witnessed the overwhelming burdens

of our healthcare system and the worsening crisis of healthcare provider burnout.

That was actually Paul that said that and said, our empathy for frontline providers catalyzed

us to create a company committed to fully leveraging technology to improve the practice

of medicine.

So back in 2001, they, you know, got this whole thing kicked off.

And then in 2023, Glass Health took a pretty big pivot.

So leveraging the upward trajectory of generative AI, the company shifted its focus.

Now the platform is powered by a large language model that can assist in generating diagnoses

and treatment options for clinicians.

So physicians can input descriptions like patient demographics and symptoms and Glass

Health's AI offers a likely prognosis and clinical plan.

This is really interesting.

And also, I think this is important to talk about because this is what we're seeing.

This trend is all over tech right now where essentially you had this company founded back

in 2021, probably raised money at a very high valuation.

Maybe they weren't seeing the product market fit they were wanting or maybe they just saw

some new opportunities in AI and kind of did a big, huge pivot.

And now it's like, oh yeah, we're just like an AI company to help diagnose things for

doctors.

That's just what our company is.

But obviously it's not what it started as.

And I think this is a no-brainer.

Any startup that I was really actively involved in as the beginning of the year came around

and we saw this big, huge wave of AI, yeah, immediately all of them turned into AI companies.

We just integrated AI into everything we're doing and completely changed our product.

So I'm not calling the kettle black and I actually think it's great because there's

a whole bunch of, here's the thing.

For a company to go from zero to here or zero to a hundred, completely fresh and then come

up with a revolutionary idea, takes a lot of time.

It takes energy to get investors or to get the team put together to actually build the

product.

And if a company is already, let's say they have some version of product market fit, they

have users, they have a database, they have some sort of user base and then they're able

to add in AI features.

One of my companies I had when AI started hitting the forefront, it was two weeks before

we had some major AI products.

And that's just because we already had all the infrastructure, the logins, the sign ins,

the client base, we had everything built out and then immediately we're able just to start

building these AI features and adding them on top of what we already had.

And it felt like a very robust system.

Now if we had thought of the idea for what we had right when AI, when CHBT came out,

we'd probably still be under development working on building what is now, we were able to get

it done in two months.

So I think this is awesome.

I think this is something we're going to see in the future.

This is definitely a trend and good on Glass Health for kind of seeing this and grabbing

this traction.

So I think while AI's promise in healthcare is quite, it looks very attractive.

It's not without some of its caveats.

So Babylon Health, which is an AI startup backed by UK's NHS, has faced a lot of criticism

for essentially making inflated claims about its capabilities.

So Glass Health's offering might also bring about some ethical concerns.

Of course, there's people talking about data biases, as the AI is trained on health records

that could be influenced by different racial, gender, socioeconomic biases.

It's like, I guess you kind of have to ask yourself, even geographically, the AI might

be trained on the United States and maybe the diet of the United States is completely different

than the diet in, let's say, a country like Argentina or Ethiopia or France.

There's all sorts of different factors and if an AI is specifically trained just on one

area, maybe it's going to miss some things that are relevant to people in other places.

So there's all sorts of things to look at in that regard, but in any case, addressing

a lot of these challenges, Paul emphasized saying, quote, Glass connects LLMs with clinical

guidelines that are created and peer reviewed by our academic physician team.

Our physician team members are from major academic medical centers around the country

and work part-time for Glass Health.

We ask our clinician users to supervise all of our LLM applications, outputs closely,

treating it like an assistant that offers helpful recommendations.

Here's one other thing I did want to bring up on this whole, people essentially are poking

holes in what these models are not able to do.

And I think, sure, it's totally fair.

Make sure if a model say it can do something and it isn't, like you're holding them accountable

to being truthful, whatever, I'd get that.

But the people that just want to criticize for criticism's sake, I will say to those

people, right now, of course, even my thing of like, oh, you're living in a different

geographic area and maybe the model was just trained on Americans and there's differences

between people that live in other places.

So here's the thing, current medicine and current medical journals, which is where a

lot of doctors get their information, may just very well have the exact same problem.

So it's just all about the data.

And what I think is interesting is it's probably actually AI models that could help solve this

problem in the future.

You can imagine an AI model like this that diagnoses diseases, but it asks you exactly

where you're from.

It looks at all the latest data from that specific area, studies in your specific geographic

location, specifically for your body type, your ethnicity, your background, everything.

I've heard a lot of like talk about, you know, people saying like, oh yeah, like my

ancestors used to eat potatoes and your ancestors used to eat rice and yadda yadda.

So today, like my body can digest this or that better.

And there's all sorts of interesting things like that.

And I mean, I am definitely not a nutrition coach.

I'm definitely not a medical expert with medical advice or anything.

And so, I mean, I can't tell you exactly how accurate that is, but I've heard things

like that in a lot of different areas.

And what I can say is our current approach of medicine and everything else, we definitely

are just treating everyone like the exact same, like, oh, you have X, Y and Z.

Okay, here's the pill for X, Y and Z, where it might not actually work as well on you

for your type of, you know, body or whatever.

So I think the cool thing about a lot of this AI is I think it does have the possibility

of becoming more customized, more personalized to you.

And I know right now, people might be like, yeah, right, like, look at chat, it's got

all sorts of errors and problems and like, give this, give this tech like five years,

10 years.

If we don't figure out any way to make it better other than to make it more personalized,

I think we're going to see some really incredible advancements and it's going to be a lot more

effective at, you know, doing whatever it needs to do, just based off of like, let's

say you train an AI specifically for you, everything about you, it's got your like,

you know, your genealogy in there, like whatever, I don't know, right?

But it can really dial in on what it thinks can help you best.

And so, yeah, I think it may not be there right now, but that's definitely where this

stuff will go in the future.

It's going to seem like, you know, a no brainer in the future, you know, it's going to seem

crazy that at one point the entire world used one giant model called ChatGBT and everyone

had like the same thing with the same data set, whatever.

So I think it'll be interesting to see how that evolves.

I think despite all of these complex complexities, GlassHealth has already secured a significant

market validation.

So they just did a $1.5 million pre seed round led by Brayer Capital back in 2022.

And this was followed by acceptance into Y Combinators winter 2023 batch.

I think this certainly adds weight to their venture.

And also I think with more than 59,000 users and a direct to clinician monthly subscription

offering GlassHealth is positioned for growth.

I think the company plans to launch an electronic health record integration enterprise, which

is essentially offering with HIPAA compliance and already has 15 health systems on the wait

list.

So Paul also commented on this and said, quote, Glass is different from LLM applications

like ChatGBT that rely solely on their pre training to produce outputs and can more easily

produce medical information that is inaccurate or out of date.

And so I think while, you know, GlassHealth navigates the choppy waters of this whole

healthcare innovation and ethical AI use, it also continues to resonate with both investors

and the medical community with a total of $6.5 million in funding and four years of

runway.

I think it's worth keeping an eye on how this kind of ambitious startup is, you know,

going to potentially be redefining the role of technology in healthcare in the future.

So definitely one will continue to follow.

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Machine-generated transcript that may contain inaccuracies.