Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. It can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia better than expert radiologists working alone.
A tool the researchers developed along with the algorithm produced these images, which are similar to heat maps and show the areas of the X-ray most indicative of pneumonia.
“Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there’s a lot of variability in the diagnoses radiologists arrive at,” said Pranav Rajpurkar, a graduate student in the Stanford Machine Learning Group and co-lead author of the paper.
Within a week the researchers had an algorithm that diagnosed 10 of the pathologies labeled in the X-rays more accurately than previous state-of-the-art results. In that short time span, CheXNet also outperformed the four Stanford radiologists in diagnosing pneumonia accurately.
After about a month of continuous iteration, the algorithm outperformed the four individual Stanford radiologists in pneumonia diagnoses.