AI in Radiology

AI now helps in diagnosing dangerous lung diseases and adds it to its growing list of things.

A few months back a new arXiv paper by researchers from Stanford explains how CheXNet, the convolutional neural network they developed, achieved the feat. CheXNet was trained on a publicly available data set of more than 100,000 chest x-rays that were annotated with information on 14 different diseases that turn up in the images. The researchers had four radiologists go through a test set of x-rays and make diagnoses, which were compared with diagnoses performed by CheXNet. Not only did CheXNet beat radiologists at spotting pneumonia, but once the algorithm was expanded, it proved better at identifying the other 13 diseases as well. Early detection of pneumonia could help prevent some of the 50,000 deaths the disease causes in the U.S. each year. Pneumonia is also the single largest infectious cause of death for children worldwide, killing almost a million children under the age of five in 2015.

Stanford researchers trained a convolutional neural network on a data set of 40,895 images from 14,982 studies. The paper documents how the algorithm detected abnormalities (like fractures, or bone degeneration) better than radiologists in finger and wrist radiographs. However, radiologists were still better at spotting issues in elbows, forearms, hands, upper arms, and shoulders.

We’ve come a far way in AI, but still, we’ve miles of journey left. The results here clearly depict that AI is excelling humans, but does it mean that we don’t need humans? In the coming era of super intelligence, where are we standing?


(via; MitTechReview, arXiv)

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