Dr. Emilia Javorsky

Like we still don't actually know that question because all of our data is when people present to a system that are sick, right?

And so I think that is a great example of like public infrastructure investment in data collection that is clinically relevant to help us bridge that gap of like the mouse to the human, right?

How do we know if something works in a human being?

How can we better predict that?

It's gonna start with measuring and studying people at the end of the day.

I think there's the piece of AI investments in general, and I would argue we should be investing a lot more in AI just in medicine and in tool development.

And there are so many areas that this is really exciting for AI to discover new biomarkers, right?

New things in your blood that you can start to see, well, is something working or not?

Or is a surrogate of a disease that can help accelerate therapeutic development there?

helping to detect things earlier.

We use the AI in mammography example.

And so those are all like AI tools that need to be built that are going to actually like unblock progress in oncology that we're just not investing in because that money is going into building the ASI promise.

Yeah, fundamentally, I'm super bullish on the promise of AI and oncology and medicine in general.

It's just the right kind of AI development that's targeted to actually solving the problems and unblocking the things that are holding up our ability to move science forward.

I would also add to that landscape, Tristan, the AI on the manufacturing side of things.

So like, how do we actually make drugs at scale?

How do we do quality control?

Looking at something like CAR-T therapy, which is a cell-based therapy to help treat cancers in patients that's very individualized.

And because it's individualized, it's very expensive to make.

Right now, it's upwards of $400,000 to access the therapy.