Some patient groups are far more vulnerable to near-perfect privacy attacks from medical AI

AI Summary

Medical AI systems aiding diagnosis through imaging face privacy challenges due to membership inference attacks that risk leaking personal information of vulnerable patient groups. The issue highlights a growing cybersecurity threat in healthcare AI applications.

From detecting pneumonia on a chest X-ray to assessing whether a dark spot on the skin is benign or malignant, medical AI systems are playing an increasingly important role in clinical diagnosis. Unfortunately, the models used to train these AI systems are often victims of cyberattacks, specifically membership inference attacks (MIAs), which can lead to people's personal information being stolen or revealed.