Challenges with Using Machine Learning to Classify Chest X-Rays

from Linear Digressions
by Ben Jaffe and Katie Malone

Published 15 January 2018 (4 months ago) • Duration: 18 minutes

Another installment in our "machine learning might not be a silver bullet for solving medical problems" series. This week, we have a high-profile blog post that has been making the rounds for the last few weeks, in which a neural network trained to visually recognize various diseases in chest x-rays...

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Another installment in our "machine learning might not be a silver bullet for solving medical problems" series. This week, we have a high-profile blog post that has been making the rounds for the last few weeks, in which a neural network trained to visually recognize various diseases in chest x-rays is called into question by a radiologist with machine learning expertise. As it seemingly always does, it comes down to the dataset that's used for training--medical records assume a lot of context that may or may not be available to the algorithm, so it's tough to make something that actually helps (in this case) predict disease that wasn't already diagnosed.

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