Dr. Emilia Javorsky

And I strongly believe that that is not actually the best way to start saving lives today.

Yes, I'm incredibly excited about the potential for AI and this general moment that we're in for progress in oncology.

I remain really hopeful and excited about what the future has ahead.

For me, that's sort of three ingredients, which is one, supporting all of the AI tools that are being developed in specific areas of oncology that are making things go faster, cheaper, better, unlocking new capabilities.

the exciting research that's happening in biology.

So there's really exciting science that's happening that's sort of discovering totally new ways to think about the problem.

And so figuring out how do we support those scientists doing that good work and getting their discoveries out of the lab and into the clinic faster.

And then thinking about how can we actually realign and redesign the system that we have and identify where the parts are in the current system that are either holding up progress or even taking it in the other direction, right?

And so I think that kind of tripartite approach is one that makes us well-suited to make a lot of progress in oncology in the next decade.

But part of the reason I wrote this essay is because I'm worried that the current approach isn't doing those key things that we need to actually move the needle and that our resources are being placed in areas that are not going to deliver the benefits that we hope for.

So we hear a lot about the ways that AI is helping advance progress in medicine in kind of the here and now, which it is, and it is going to be instrumental in doing so.

But it's not chat GPT that is unlocking that progress.

It's scientists building bespoke models off of highly curated data sets to actually solve a specific problem, whether that be drug design or whether that be predicting toxicity, the list goes on.

So I think one piece to start is the AI will cure cancer promise, surfs a little bit on the AI progress that's already being made with tools and smaller models, and kind of bundling that as evidence as to why ASI will help solve the problem, because if the AI could get so much better, imagine how much better results we could be getting, right?

So that could be an image of a mammogram for breast cancer, or it could be blood test results, right?

And then getting sufficient measurement of that phenomenon into a data set.

And so can we generate a data set that captures all of the variability that we see in when we measure that phenomenon that's sufficiently representative?

And then can we apply intelligence to unlock insights that previously humans did not see or were unable to do at scale?

And so in medicine, we're seeing this happen across many domains where we have good data.

So when we talk about early detection of breast cancer, AI is amazing at that because we have lots of great images that are high quality and curated by human radiologists of what is and what isn't breast cancer.