Diagnostic algorithms can be fooled by cyberattacks, UPMC research finds
Becker's Health IT
Artificial intelligence models designed to expedite cancer diagnoses are vulnerable to cyberattacks that falsify images, according to a study published Dec. 14 in Nature Communications.
Researchers from UPMC in Pittsburgh trained an algorithm to identify cancerous and benign cases among mammogram images with more than 80 percent accuracy. They then simulated a cyberattack by developing a program that produces false images by adding or removing cancerous regions from images.
The algorithm was fooled by 69.1 percent of the falsified images. The breakdown is as follows: Of the 44 positive images made to look negative, the algorithm classified 42 as negative. Of the 319 negative images made to look positive, the algorithm classified 209 as positive.
The researchers also asked...