2.5 million patients analyzed with AI.
When you watch an interview with a well-known actor, you realize that one thing they have in common is that they tend to be quite shy.
This may come as a shock at first, but what happens is that good actors are precisely shy people who became actors to hide their shyness behind a character.
It is as if, while they are acting, they rest from their social anxiety because the mask protects them.
These paradoxes or psychological compensations are interesting and they are everywhere…
and I think that’s what happened to me somehow, when I got into Artificial Intelligence.
I’m so impatient that it kept me from doing research.
And it bothered me because I think research is the most noble and interesting thing a person can do.
So I invented a world in which I could do research by being impatient.
Lots of data, lots of patients, but with a fraction of the effort.
Speed and immediacy, without spending a lot of time entering data.
Associations and correlations without having to spend 20 years in a practice to intuit them.
That’s why I often describe machine learning as “sensing machines”.
If we had not done all this with Savana, it would be impossible to have published the article we have just published called “LiverTAI, a retrospective analysis of EHRs through Natural Language Processing”.
And which has the peculiarity of having studied the patient journey of almost 2.5 million patients in terms of HCV testing.
2.5 million is a lot of patients.