Emilia Javorsky
speaker
1,653 appearances
2 recordings
1 series
first heard Mar 2026
last heard 20 Mar
Emilia Javorsky’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Mar 2026 with 2.
Appearances
What people don't know is medical records are not actually designed to be data capture systems that reflect your clinical reality and your health.
What they were designed for is to optimize and facilitate billing for medical institutions.
And so the data they care about is the data that is relevant for issuing claims to insurance companies.
And so as a result of that, there is a systematic bias in all of the data to make you look a little bit more sick than you do in order to increase revenue.
And we saw this fail with the original IBM Watson experiment.
So this was the original AI that played Jeopardy.
Everyone loved it.
It was probably the first AI that entered the public zeitgeist.
And they went into a bunch of partnerships with the different medical institutions, Mayo Clinic, X, Y, and Z. And those partnerships ultimately failed for this reason, right?
Training on electronic medical record data actually does not help in terms of predicting performance in the clinic and being generalizable to the clinic.
The lab tests that we have, those are great, but those are very coarse measurements of biology.
You know, you think about going to the doctor, you get your annual exam, you get back like 35 blood tests, right?
Or somewhere between that.
Maybe if you're doing like the full battery and going deep and you're paying for some sort of personalized medicine service, you get to 135.
And that's just a very, very coarse measurement of everything that is happening with inside human physiology.
And we do have new measurement modalities coming online that are much more precise, like proteomics, metabolomics that can measure thousands of things in one's blood and imaging and full body MRIs.
That's not being really integrated into routine care or being captured at scale in any meaningful way.
And so we end up effectively with this kind of data desert, both from the basic biology standpoint, all the way up to clinical biology.
And I think that's a travesty because we do have other examples of what happens when we do have good data with AI.
And the example I lean into here quite heavily is what happened with AlphaFold.
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