🔥Telegram İfşa

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.

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

Areas that I think will be increasingly important for human doctors as AI continues to get better and better: - Communication / interviewing - Embodied tasks (e.g. physical exam, procedures) - Teamwork - Research (creating new knowledge) - Using AI tools effectively

There are obviously lots of issues with relying on LLMs for med decision making. But they also provide so much value by making medical knowledge more accessible! Balancing these interests to build trust and provide better care will be a big challenge for medicine in coming years

Many people in my med school skip lectures and instead invest their time in churning out clinical research projects. This is a very rational decision given the incentives for applying to residency but a net negative for medicine in my opinion. Need to change incentive structures!

🌶️ Hot take: the value function for the number of papers a person publishes is not monotonically increasing! My own mental model is shaped more like a mesa, with x-axis scaled depending on seniority/circumstances. There is such a thing as having too many papers!

Completely misguided to think that these 3000 questions capture the entire "frontier of human knowledge." Still, very useful to curate challenging benchmarks like this. Models will benefit from training on these difficult questions (cf. hard example mining, boosting).

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

This piece from more than 30 years ago is incredible - replace "computers" with "AI" and it could have been written yesterday. I predict that it will still be relevant 30 years in the future for whatever the next big technology leap will be. academic.oup.com/jamia/article-…

Finally passed 2000 ELO in bullet on lichess.org after more than 13k games played over the last 10 years. I won’t say it was time wasted, but it did make me reflect on these words from Paul Morphy, one of the great American chess masters 😅

When using GPT to help debug code recently, the first thing it suggests is always numerical precision error. It seems to have a poor feel for general magnitudes of numbers. No ability to gauge the difference between a "small" number like 0.006 and a SMALL number like 6e-20

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the

Queensborough Bridge by Edward Hopper (1913). Almost exactly 100 years later this spot would go on to become the home of Cornell Tech. I wonder what the future holds for Roosevelt Island in 2113?

The only technical detail Google releases for this project is that they’re using a python version that’s been obsolete for 5+ years? 🤔

AI + expertise paradox. More technical and specialized medical tasks -> easier for AI to substitute & outperform _average_ human experts. Case in point is echocardiogram interpretation. Yet it is those tasks that are better paid/reimbursed vs general primary care. HT Venk Murthy MD PhD

Huge update to our preprint today on the superhuman performance of reasoning models in medical diagnosis! TL;DR – they don't just surpass humans in meaningful benchmarks, but in actual medical care from unstructured clinical data: A 🧵⬇️: x.com/AdamRodmanMD/s…

Ursula Le Guin and Philip K Dick, two generational literary talents, were high school classmates

Before modern neuroimaging, there was pneumoencephalography: CSF was drained and replaced with air and an X-ray was taken. Patients were spun upside down so that the air bubbles would float to different places in the ventricles to get multiple views. This continued into the 70s!