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Algorithms used in medicine are trained on data from only a few states
Sep 25, 2020
Most medical algorithms were developed using information from people treated in Massachusetts, California, or New York, according to a new study . Those three states dominate patient data — and 34 other states were simply not represented at all, according to the research published this week in the Journal of the American Medical Association . The narrow geographic distribution of the data used for these algorithms may be an unrecognized bias, the study authors argue.
The algorithms that the researchers were looking at are designed to make medical decisions based on patient data. When researchers build an algorithm that they want to guide patient diagnosis — like to examine a chest X-ray and decide if it has signs of pneumonia — they feed it real-world examples of patients with and without the condition they want it to look for. It’s well-recognized that gender and racial diversity is important in those training sets: if an algorithm only gets men’s X-rays during training, it may not work as well when it’s given an X-ray from a woman who is hospitalized with difficulty breathing. But while researchers have learned to watch for some forms of bias, geography hasn’t been highlighted.
“There are all these things that end up getting baked into the dataset and become implicit assumptions in the data, which may not be valid assumptions nationwide,” study author and Stanford University researcher Amit Kaushal told Stat News .
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