Black-Box Algorithms: Ready For Medical Use? : Shots

Black-Box Algorithms: Ready For Medical Use? : Shots – Health News It’s hard for humans to check algorithms that computers devise on their own.

Zech and his medical school colleagues discovered that the Stanford algorithm to diagnose disease from X-rays sometimes “Cheated.” Instead of just scoring the image for medically important details, it considered other elements of the scan, including information from around the edge of the image that showed the type of machine that took the X-ray.

Black-box algorithms are the favored approach to this new combination of medicine and computers, but “It’s not clear you really need a black box for any of it,” says Cynthia Rudin, a computer scientist at Duke University.

She is pushing back against a trend in the field, which is to add an “Explanation model” algorithm that runs alongside the black-box algorithm to provide clues about what the black box is doing.

Shah developed an algorithm that could scan medical records for people who had just been admitted to the hospital, to identify those most likely to die soon.

It is equally important to avoid misuse of an algorithm, for example if a health insurer tried to use Shah’s death-forecasting algorithm to make decisions about whether to pay for medical care.

“I firmly believe that we should be thinking about algorithms differently,” Shah says.

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Inside the ‘Black Box’ of a Neural Network

Neural networks have proven tremendously successful at tasks like identifying objects in images, but how they do so remains largely a mystery.

On Wednesday, Carter’s team released a paper that offers a peek inside, showing how a neural network builds and arranges visual concepts.

Olah’s team taught a neural network to recognize an array of objects with ImageNet, a massive database of images.

Neural networks are composed of layers of what researchers aptly call neurons, which fire in response to particular aspects of an image.

Researchers trying to understand how neural networks function have been fighting a losing battle, he points out, as networks grow more complex and rely on vaster sums of computing power.

As an illustration, Olah pulls up an ominous photo of a fin slicing through turgid waters: Does it belong to a gray whale or a great white shark? As a human inexperienced in angling, I wouldn’t hazard a guess, but a neural network that’s seen plenty of shark and whale fins shouldn’t have a problem.

Neural networks are generally excellent at classifying objects in static images, but slip-ups are common-say, in identifying humans of different races as gorillas and not humans.

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