“There’s been a lot of concern about how machine learning will actually work within the medical field,” said Allison Park, a graduate student in statistics and co-lead author of the paper. “This research is an example of how humans stay involved in the diagnostic process, aided by an artificial intelligence tool.”
This tool, which is built around an algorithm called HeadXNet, improved clinicians’ ability to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians. While the success of HeadXNet in these experiments is promising, the team of researchers — who have expertise in machine learning, radiology and neurosurgery — cautions that further investigation is needed to evaluate the generalizability of the AI tool prior to clinical deployment, given differences in scanner hardware and imaging protocols across different hospital centers. The researchers plan to address such problems through multicenter collaboration.