What happens when diagnosis is automated? | By Siddhartha Mukherjee | The New York Times | March 27, 2017

 

In some trials, “deep learning” systems have outperformed human experts.

One evening last November, a fifty-four-year-old woman from the Bronx arrived at the emergency room at Columbia University’s medical center with a grinding headache. Her vision had become blurry, she told the E.R. doctors, and her left hand felt numb and weak. The doctors examined her and ordered a CT scan of her head.

A few months later, on a morning this January, a team of four radiologists-in-training huddled in front of a computer in a third-floor room of the hospital. The room was windowless and dark, aside from the light from the screen, which looked as if it had been filtered through seawater. The residents filled a cubicle, and Angela Lignelli-Dipple, the chief of neuroradiology at Columbia, stood behind them with a pencil and pad. She was training them to read CT scans.

“It’s easy to diagnose a stroke once the brain is dead and gray,” she said. “The trick is to diagnose the stroke before too many nerve cells begin to die.” Strokes are usually caused by blockages or bleeds, and a neuroradiologist has about a forty-five-minute window to make a diagnosis, so that doctors might be able to intervene—to dissolve a growing clot, say. “Imagine you are in the E.R.,” Lignelli-Dipple continued, raising the ante. “Every minute that passes, some part of the brain is dying. Time lost is brain lost.”

She glanced at a clock on the wall, as the seconds ticked by. “So where’s the problem?” she asked.

Strokes are typically asymmetrical. The blood supply to the brain branches left and right and then breaks into rivulets and tributaries on each side. A clot or a bleed usually affects…

Read the rest of the article at https://www.newyorker.com/magazine/2017/04/03/ai-versus-md

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