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: Doing it wrong.
Tag: automation
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.
The End of Human Doctors – The Bleeding Edge of Medical AI Research (Part 3)
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.
Predicting Medical AI in 2017
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
The three phases of medical AI trials
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
Lauren Oakden-Rayner 