MD, MPH. Peds Hem/Onc. Data nerd using technology to improve healthcare for kids around the world. Love languages: graphs, references, Bayesian statistics
Joined November 2012
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I'm a doctor who does research.
I've annoyed DOZENS of statisticians.
Here are 5 proven ways to exasperate them.
#medtwitter #epitwitter #statstwitter
Mark Zobeck retweeted
OpenAI was hacked via the exact library mentioned in the xkcd comic hahahaha
The prophecy is fulfilled
Mark Zobeck retweeted
Elon buying Twitter and restoring free speech was unironically one of the most pivotal moments in the 21st century
Its effects will be studied for generations
Mark Zobeck retweeted
There is a super shady data control setting on everyone's ChatGPT which makes it seem like your data can be used for training even if you explicitly say to *NOT* improve the model for everyone (1/9)
Readers added context they thought people might want to know
This claim is false. OpenAI has clarified that the in-app toggle and the privacy portal are independent ways to opt out of data training. You only need to use one method, and OpenAI respects the opt-out choice regardless of where it is set.
x.com/thsottiaux/sta…
help.openai.com/articles/77308…
Mark Zobeck retweeted
👀Un estudio pone a prueba ChatGPT en el trabajo de un bioestadístico y estos son los resultados más interesantes. Hilo👇
onlinelibrary.wiley.com/doi/…
Desplaza la discusión desde la productividad y la automatización hacia la supervisión y la validación.
El tema de mi próximo curso.
Mark Zobeck retweeted
We've added to the website a free PDF of the Bayesian Workflow book (for non-commercial purposes, like research and exorcism). We put so much work into this book, hope you all find value in it. avehtari.github.io/Bayesian-…
1/ The bitter lesson is true right now for clinical AI. And that is ok.
ALT Minimal illustration of a clinical expert and an AI system fist bumping as collaborators.
That is why the bitter lesson being true right now is ok. It shifts the durable work toward the harness: governance, retrieval, citation standards, audit trails, escalation rules, input/output controls, and workflow design.
The practical lesson is to build for model churn. Assume better general models will keep arriving, then build infrastructure that can use them safely and keep responsibility where it belongs.
thedoctorsdialectic.com/docs…
A lot of clinicians are stuck at the same point with AI:
“I see the potential, but what do I actually do with it?”
My answer is small: pick one workflow where verification is faster than generation.
ALT A gold verification checkmark on a pile of documents.
The hard requirement is that you have to know what correct looks like.
If you ask for “something good,” you will get something plausible.
If you know the standards, you can verify.
That is where the leverage starts.
