Some computer scientists are enthralled by programs that can teach themselves how to perform tasks, such as reading X-rays.
Many of these programs are called “black box” models because the scientists themselves don’t know how they make their decisions. Already these black boxes are moving from the lab toward doctors’ offices.
The technology has great allure, because computers could take over routine tasks and perform them as well as doctors do, possibly better. But as scientists work to develop these black boxes, they are also mindful of the pitfalls.
Pranav Rajpurkar, a computer science graduate student at Stanford University, got hooked on this idea after he discovered how easy it was to create these models.
The National Institutes of Health one weekend in 2017 made more than 100,000 chest X-rays publicly available, each tagged with the condition that the person had been diagnosed with. Rajpurkar texted a lab mate and suggested they should build a quick and dirty algorithm that could use the data to teach itself how to diagnose the conditions linked to the X-rays.
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