📢 New piece published today in @Nature!
It was a pleasure to work with such a stellar team including Oishi Banerjee, @ZahraShakerii, @hmkyale, @jure, @EricTopol, and @pranavrajpurkar!

🧵below.



@Stanford @Harvard @Yale @UofT @scrippsresearch
1/nnature.com/articles/s4158… 💡We envision a new paradigm for medical AI, which we refer to as generalist medical AI (or GMAI).

We define a model to be GMAI if it:

1) has flexible multi-modality 🩻💬💉 (the ability to interpret combinations of modalities like text, images, EHR, lab results, ..)

2/n

Interesting failure mode of #GPT4!

It can't play "Set", a card game that is trivial to solve with a 10-line python program.

--> it can explain the game, can abstract it, write a program to solve it, but can't actually *play* it.

Check the full convo in the chat below: First, I asked about the game (great intro if you don't know the game).

2022 was wild for medical #AI and esp. medical foundation models (FMs).

This 🧵lists some of the standout papers from this year about this topic. Let's go!
(1/9)
#medtwitter #AIinMedicine #medicalAI @NatureMedicine First, some excellent reviews explaining some key preliminaries of FMs:

🔍 self-supervised learning:
a paradigm that allows for the training of AI models w/o explicit and costly labels (huge for medical applications).
nature.com/articles/s4155…
(2/9)

If you want to predict clinical phenotypes using #MachineLearning, check out our systematic review on ML-based #sepsis prediction. A THREAD with take-aways that could be relevant to #AI in #healthcare in general. (--> = hints for practitioner) 1/n
bit.ly/34pIvRs 1) Motivation: why should we even care about #sepsis? For decades clinicians have a) struggled to detect it in its early stages where organ damage is still reversible and b) failed to find a robust and early biomarker for sepsis. 2/n