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 can discover shortcuts to “correct” answers that 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. “The community fools [itself] into thinking we’re developing models that work much better than they actually do,” Berisha says. “It furthers the AI hype.”
Berisha
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