Medical information is more complex and less available than the web data that many algorithms were trained on, so results can be misleading.
to improve image-recognition algorithms. Accessing health data is harder because of privacy concerns and creaky IT systems. And deploying an algorithm that will shape someone’s medical care carries higher stakes than filtering spam or targeting ads.
“We can’t take paradigms for developing AI tools that have worked in the consumer space and just port them over to the clinical space,” says Visar Berisha, an associate professor at Arizona State University. He recentlya journal article with colleagues from engineering and health departments at Arizona State warning that many health AI studies make algorithms appear more accurate than they really are because they use powerful algorithms on data sets that are too small.
That’s because health data such as medical imaging, vital signs, and data from wearable devices can vary for reasons unrelated to a particular health condition, such as lifestyle or background noise. The machine learning algorithms popularized by the tech industry are so good at finding patterns that they canthat won’t work out in the real world. Smaller data sets make it easier for algorithms to cheat that way and create blind spots that cause poor results in the clinic.
Berisha says that problem has led to a striking and concerning pattern in some areas of AI health care research. In studies using algorithms to detect signs of Alzheimer’s or cognitive impairment in recordings of speech, Berisha and his colleagues found that larger studies reported worse accuracy than smaller ones—the opposite of what big data is supposed to deliver. A
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