Medical AI Researcher / Radiologist
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.
I discuss a piece of medical AI research that has not received much attention, but actually did a proper clinical trial!
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
Deep learning research in medicine is a bit like the Wild West at the moment; sometimes you find gold, sometimes a giant steampunk spider-bot causes a ruckus. This has derailed my series on whether AI will be replacing doctors soon, as I have felt the need to focus a bit more on how to assess … Continue reading Do machines actually beat doctors? ROC curves and performance metrics
Lauren Oakden-Rayner 