Imaging AI evaluation and monitoring just became even hotter topics, following a particularly revealing Twitter thread and a pair of interesting new papers.
Rads Don’t Work for AI – A Mayo Clinic Florida neuroradiologist took his case to Twitter after an FDA-approved AI tool missed 6 of 7 hemorrhages in a single shift and he was asked to make extra clicks to help improve the algorithm. No AI tool is perfect, but many folks commenting on this thread didn’t take kindly to the idea of being asked to do pro-bono work to improve an algorithm that they already paid for.
AI Takes Work – A few radiologists with strong AI backgrounds clarified that this “extra work” is intended to inform the FDA about postmarket performance, while monitoring healthcare tools and providing feedback is indeed physicians’ job. They also argued that radiology practices should ensure that they have the bandwidth to monitor AI before deciding to adopt it.
The ACR DSI Gets It – Understanding that “AI algorithms may not work as expected when used beyond the institutions in which they were trained, and model performance may degrade over time” the ACR Data Science Institute (DSI) released a helpful paper detailing how radiologists can evaluate AI before and during clinical use. In an unplanned nod to the above Twitter thread, the DSA paper also noted that AI evaluation/monitoring is “ultimately up to the end users” although many “practices will not be able to do this on their own.” The good news is the ACR DSI is developing tools to help them.
DLIR Needs Evaluation Too – Because measuring whether DL-reconstructed scans “look good” or allow reduced dosage exams won’t avoid errors (e.g. false tumors or removed tumors), a Washington University in St. Louis-led team is developing a framework for evaluating DLIR tools before they are introduced into clinical practice. The new framework comes from some big-name intuitions (WUSTL, NIH, FDA, Cleveland Clinic, UBC), all of whom also appear to agree that AI evaluation is up to the users.
The Takeaway – At least among AI insiders it’s clear that AI users are responsible for algorithm evaluation and monitoring, even if bandwidth is limited and many evaluation/monitoring tools are still being developed. Meanwhile, many AI users (who are crucial for AI to become mainstream) want their FDA-approved algorithms to perform correctly and aren’t eager to do extra work to help improve them. That’s a pretty solid conflict, but it’s also a silver lining for AI vendors who get good at streamlining evaluations and develop low-labor ways to monitor performance.