Super-resolution promises to be one of the most impactful medical imaging AI technologies, but only if it is safe. This week we saw the FDA approve the first MRI super-resolution product, from the same company that received approval for a similar PET product last year. This news seems as good a reason as any to talk about the safety concerns myself and many other people have with these systems.
Medical AI testing is unsafe, but addressing hidden stratification may be a way to prevent harm, without upending the current regulatory environment.
Ai competitions are fun, community building, talent scouting, brand promoting, and attention grabbing. But competitions are not intended to develop useful models.
I discuss a piece of medical AI research that has not received much attention, but actually did a proper clinical trial!
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 AI has a safety problem; we know for a fact our testing isn't reliable. We've seen how this plays out before.
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?
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.
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, … Continue reading The philosophical argument for using ROC curves