Dr. David Fajgenbaum

Our medical team uses these same machine learning algorithms.

You can use them too, but it's important to remind them that when our medical team uses those machine learning algorithms

and they come up with something like lidocaine for breast cancer, we still then go on to do a bunch of laboratory work of lidocaine in breast cancer.

And then we think about doing the right clinical trial of lidocaine in breast cancer.

So it's not like we use the algorithm to immediately move forward into action.

We use it to then plan out what to do next.

So our feeling is that we as a nonprofit at EveryCure, we're only going to be able to go through like dozens.

I mean, if we can get to the hundreds, I would be over the moon about it.

But like there are still thousands of diseases that like could potentially benefit from our scores that we'll just never be able to get to unless it's like because the list that Matrix is spitting out is just so big.

It's so big and it's so powerful.

The thing is, when we look at the top, we are blown away by the number of promising drugs.

We're like, wow.

And actually, some of the cases, there's actually been clinical trials that have shown the drug works.

But someone stopped after the small trial because there was no way to commercialize it.

So one part is, let's make it available to the world so that other people can pursue these things that we're not able to go after.

And the other is sort of probably a little bit inspired by

Maybe we shouldn't be so paternalistic in medicine, and maybe we should allow this information to be out there.

Of course, when I say that, I do cringe just a little bit because I don't want us to create problems by putting this out there.

But it feels like the responsible thing is to share the scores.

but to appropriately caveat them and disclaim them to say, like, these are for research purposes.