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!

The first AI disruption in medicine might not be radiology

The first doctors to feel the effects of automation might not be radiologists at all. It might be surgeons.

CheXNet: an in-depth review

Since the CheXNet paper came out in November 2017 I have been communicating with the author team. I'm finally ready to review the paper. Some of the things I found out surprised me.

The philosophical argument for using ROC curves

I just wanted to do a quick follow up to my recent blog post, which discussed the performance metrics I think might be appropriate for use in medical AI studies. One thing I didn't cover was the reason we might want to use multiple metrics, or the philosophy behind choosing the ones I did. So today, …

2017 in review: progress, problems, and predictions

2017 was cool. Medical AI progressed apace, the AI community grew up some and got a bit creative, and I made some predictions that mostly held up to vague scrutiny.