In what's expected to soon be commonplace, artificial intelligence is being harnessed to pick up signs of cancer more accurately than the trained human eye. This latest AI model has a near 100% success rate and serves as a clear sign of things to come.
Accuracy in a classification context is defined as (N correct classifications / total classifications). So classifying everything as cancer would, in a balanced dataset, give you ~50% accuracy.
This article is indeed badly written PR fluff. I linked the paper in a sister comment. Both the confusion matrix and the ROC curve look phenomenal. Train/test/validation split seems fine too, as do the training diagnostics, so I’m optimistic that it isn’t a case of overfitting.
Ofcourse 3rd party replication would be welcome, and I can’t speak to the medical relevanve of the dataset. But the computer vision side of things seems well executed.
Thx for the comment! I edited my post accordingly.