When patients have questions about their health, they prefer answers from AI over answers from human physicians. Helps explain why people are increasingly turning to chatbots for medical advice!
jamanetwork.com/journals/jam...

There is a lot of literature discussing the problems of "black box" algorithms in medical AI applications. But the world isn't black and white, and this framing ignores important differences between algorithms and user interfaces. Better to view AI interpretability/explainability as shades of grey.

There should be more overlap between coders and surgeons. The OR is the only environment I’ve been in during med school so far where a deep focus flow state is common. Hours fly by working on detail-oriented, tedious tasks. Pursuit of this focus also drives many 10x programmers.

Snails are macro-scale example of the chirality arms race. Mirror mutants have fitness advantage because the snakes that eat them evolved to have more teeth one side of their jaw.

Scary but not surprising that the chirality arms race goes down to the molecular level!

www.nature.com/articles/sre...

Have been reading about Test-Time Training after seeing it in the ARC prize technical report. Very interesting ideas and tied with some things I have been thinking about recently re: AI in medicine. AI models deployed in Clinical trials should continue learning at test-time (i.e. in the real world)!

Papers that have changed how I think about AI in Medicine: 🧵

New study: AI _increases_ burnout in Radiologists

Highlighted difference between screening and clinical settings. AI can actually increase workload in clinical settings when an abnormality is found due to time spent in differential diagnosis.

jamanetwork.com/journals/jam...

One commit at a time.
One flashcard at a time.
One mile at a time.

Consistent incremental progress over time is so powerful. Crush that project/exam/marathon!

Who is the person with the API keys to access the data? That is who you should collaborate with!

Medical knowledge isn’t as special as we might think! In my experimenting, I have never run into problems with GPT-4 lacking domain knowledge

A difficult but important skill for research is being able to appraise a new software/package/model and decide whether it is worth the investment of time and effort to learn how to use it

There are tasks that humans do today, and tasks that we would never expect a human to be able to do. More interesting are the tasks in the middle: what can humans not do today, but could do tomorrow with the right tools/knowledge?

Don’t link to a GitHub repo if it isn’t public. If you want to publish code alongside a paper, then both should be released at the same time. This concludes my TED Talk.

Isaac Asimov was 20 years old when he started writing the Foundation series

One reason for Python’s success is that its syntax is relatively close to English prose. But I imagine this would have ~zero benefit for non-English speakers.

Are there programming languages that are more popular & accessible in other parts of the world, built on languages other than English?

I love this city 🗽

People overestimate how much AI will change medicine in the next year, but underestimate its impact in the next 10 years

Copilot has completely changed how I write code. Even though it is not perfect, the gains from 10x speed-up of iteration cycle far outweigh time spent fixing mistakes. I am very excited for how LLMs will similarly transform clinical note writing!