Researchers Show How AI Could Stop Cyberattacks Messing With Hospital CT Scanners

If there's one thing a hospital patient doesn't want to think about as they prepare for a medical scan it's the possibility a cyberattacker might have found a way to remotely tamper with the diagnostic images, or even quietly upped the radiation levels used to generate them. The good news is that nobody has ever been confirmed to have done such a thing to a computed tomography (CT) X-ray scanner, which along with MRI (magnetic resonance imaging) and ultrasound systems form the backbone of modern hospital diagnosis. There is a caveat of course – the moment when somebody tries must be growing closer, leaving researchers searching for a reliable way to head off the troubling possibilities. Now a team at Israel's famous Ben-Gurion University of the Negev thinks it has come up with a solution to the problem of defending medical imaging devices (MIDs) using an AI system trained with families of open source algorithms to monitor commands sent to CT scanners for something that doesn't look right. In a proof of concept study due to be published this month, this splits the AI defense into a context-free (CF) layer that filters for obviously suspect commands (an excessive radiation level, say), and a more sophisticated context-sensitive (CS) layer that compares an apparently legitimate command to the medical context in which it is being used (giving a child an adult radiation dose).