Medical diagnosis AIs can be tricked into telling whose data trained them
AI models used to help diagnose medical conditions have a problem: They’re ready and willing to identify patients whose data was used to train them.
German researchers reported in a Nature paper published Wednesday that discriminative AI models - those used to classify data and make predictions about new inputs based on their training sets - are particularly susceptible to membership inference attacks (MIAs) that query the models in an attempt to figure out whether a particular datapoint is included in their training sets.
What that means for medical AI models is that any patient whose data is used to educate the bot could be exposed, leading to details about their medical history and diagnoses being leaked. In an analysis of seven medical AI datasets consisting of images, ECG records, and general electronic health records, the team determined that individual patients targeted by such attacks can be identified with “near-perfect...
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