Medical AI testing is unsafe, but addressing hidden stratification may be a way to prevent harm, without upending the current regulatory environment.
Improving Medical AI Safety by Addressing Hidden Stratification
Author: laurenoakdenrayner
AI competitions don’t produce useful models
Ai competitions are fun, community building, talent scouting, brand promoting, and attention grabbing. But competitions are not intended to develop useful models.
The best medical AI research (that you probably haven’t heard of)
I discuss a piece of medical AI research that has not received much attention, but actually did a proper clinical trial!
Ten controversial opinions about medical AI
Forget about interpretability, don't share your code or data, and remember, AI is magic.
Half a million x-rays! First impressions of the Stanford and MIT chest x-ray datasets
My first impressions of these datasets. How do they measure up, and how useful might they be?
Medical AI Safety: Doing it wrong.
Medical AI has a safety problem; we know for a fact our testing isn't reliable. We've seen how this plays out before.
Medical AI Safety: We have a problem.
For the first time ever AI systems can directly harm patients. Are we doing enough to prevent a medical AI tragedy, the equivalent of a thalidomide event?
Explain yourself, machine. Producing simple text descriptions for AI interpretability.
Humans explain their decisions with words. In our latest work, we suggest AI systems should do the same.
The unreasonable usefulness of deep learning in medical image datasets
Medical data is horrible to work with, but deep learning can quickly and efficiently solve many of these problems.
Post-doc and PhD positions for deep and reinforcement learning in medical images
Our team has post-doc and PhD positions available, so come to unexpectedly great Adelaide!
Lauren Oakden-Rayner 