Medical AI testing is unsafe, but addressing hidden stratification may be a way to prevent harm, without upending the current regulatory environment.
Forget about interpretability, don't share your code or data, and remember, AI is magic.
My first impressions of these datasets. How do they measure up, and how useful might they be?
Medical data is horrible to work with, but deep learning can quickly and efficiently solve many of these problems.
Today I want to look at two papers which tell us something very useful about medical AI, particularly if we are trying to predict the future of medicine.
Welcome to 2017! What a blast 2016 was. It seemed like every day there was a new, massive breakthrough in deep learning research. It was also the year that the wider world really started to take notice. The media, professional groups, and the general public all climbed aboard the AI hype train in 2016. Governments commissioned … Continue reading Predicting Medical AI in 2017
In a recent blogpost I explored how to critically read medical artificial intelligence research, focusing on the relevance of these experiments to clinical practice. It has since struck me that we don't have a simple, clear way to discuss the idea that some studies are still a still a long way off use in the clinics, and others … Continue reading The three phases of medical AI trials