Well, looking at the Diagnostic AI Consortium, in your mind, what does success look like a couple of years down the road? What are you hoping to see, say, a couple of years from now?

I'm going to start by saying something that some of the CEOs of the consortium told me when I told them a few years down the road.

They were like, what do you mean a few years down the road? I mean, in a year, this should look completely different.

I mean, we should already have a lot of outcomes in a year, which I agree.

I think that impatience is healthy here.

But I do think if we're successful, there are a few things.

First of all, we have playbooks that help people outside of ADOC, outside of these health systems that help organizations answer questions about how to do these, how to implement it safely, effectively, and truly transform their care.

Just wondered, maybe sort of as a final question, what advice would you be offering to hospitals and health systems that are looking to expand the use of AI, especially in the clinical arena? Because I think most health systems at this point are realizing AI is part of the future, but how do they expand the use of AI successfully in patient care?

Like, have you one gotten any pushback or fight about it from other people saying, hey, you're using your own thing to grade yourself? But like, what is the real value that the bridge framework brings to the market?

I had a realization.

Who's the people who's doing the most benchmarking on Opus or the model? Who's building the model cards and doing the alignment checking? It's entropic.

They're releasing these hundreds of pages model cards.

They're the people that understand the beast they created and how to test it.

So to some extent, that is natural.

And I would love for somebody else to take from me and say, okay, we will track it.

looks like if someone was interested in purchasing AI doc.

Talk a bit about, uh, when a patient gets a CT scan and what Aidoc does.

Clinical AI, the category we're in, is the category, I think where people are l- thinking about healthcare AI, that's what they're imagining.

So Chip, you've mentioned that's a lot of the conversation we're building up to.

To be clear, I think we need to tackle the administrative burden, the back-end offices, the scribing.

All of those are really important problems.

Clinical AI is really touching care delivery, and boy, is that an area where I think healthcare needs the help.

We're stretched so thin.

H- how do you deal with that? What is it, and how do you make sure it doesn't undermine all of the advantages you have from the very specific, accurate readings that, in a sense, your machine is doing?

And as we mentioned, it's a very difficult space, but can you tell us this aha moment that you said, okay, I actually have an idea to build a

company around? I actually remember this.

We sat in our ideologist room of a good friend of mine.

So my background, I don't come from healthcare at all.

I come from the AI space.

So I'm an AI engineer, led AI and AI department, and me and my two co-founders We were really passionate about healthcare and we didn't know anything about healthcare back in the day.

So we had this, you know, big whiteboard of ideas we called the Lean Startup Methodology.

However, over time, looking into the actual operation of the company, has there been a change in go-to-market strategy over time, or does that remain the same since day one?