This is uncharted territory for AI in medicine and could be a recipe for disaster.
It’s a pernicious issue plaguing the world of neural networks and machine learning that not only strengthens existing biases and racist thinking, but also worsens the effects of racist behavior towards communities of color everywhere.
.That’s what’s so concerning about alast week by a team of researchers from MIT and Harvard Medical School, which created an AI that could accurately identify a patient’s self-reported race based on medical images like X-rays alone. As if that wasn’t creepy enough, the researchers behind the modelThe team discovered that the model was able to correctly identify race with roughly 90 percent accuracy—a feat that’s virtually impossible for a human doctor to perform when looking at the same images.Marzyeh Ghassemi, assistant professor at the MIT Department of Electrical Engineering and Computer Science and co-author of the paper, told The Daily Beast in an email that the project was created initially out of an effort to find out why an AI model was more likely to. “We wanted to establish how much of this bias could be removed from the models, which led to us asking how much information about the patient's self-reported race could be detected from these images,” she said. To do that, they created a deep learning model trained to view X-rays, CT scans, and mammograms from patients who self-reported their race as Asian, Black, or white. While the images contained no mentions of patient race, the team discovered that the model was able to correctly identify race with roughly 90 percent accuracy—a feat that’s virtually impossible for a human doctor to perform when looking at the same images. Of course, this poses a number of big, hairy ethical issues with some terrifying implications. For one, research like this could give ammunition to so-called race realists and other conspiracy theorists who peddle in pseudoscience that purports that there’s an inherent, medical difference in different racial groups even though that is, of course,There’s also the fact that a model like this can be incredibly harmful if rolled out at scale to hospitals and other practices. The medical industry continues to grapple with an incredibly grim history ofand resulting malpractice. This has irrevocably shaped the way communities of color interact with the healthcare system. If an AI were to be introduced that can somehow detect a person’s race based off of a simple X-ray, this could further deteriorate that already strained relationship. To their credit, though, this is not the goal of the study’s authors. In fact, they’re looking to strengthen guardrails to help protect the communities disproportionately impacted by practices like medical racism—particularly when it comes to hospitals and medical providers using neural networks. “The reason we decided to release this paper is to draw attention to the importance of evaluating, auditing, and regulating medical AI,” Leo Anthony Celi, a principal research scientist at MIT and co-author of the paper, told The Daily Beast. “The FDA doesn't require that model performance in non-medic settings are reported by subgroups, and commercial AI often doesn't report subgroup performance either.” However, there’s still the massive, deep-learning elephant in the room: The researchers still have no idea how the AI is ascertaining patient race from an X-ray. The opaque nature of the model is disconcerting—but not uncommon when it comes to AI. In fact, scientists have struggled to understand some of the in the world—and the model from MIT is no exception. However, this one is further underscored by the grim implications of how it can be used and weaponized to harm people of color.
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