After priming the algorithm over the course of a week with millions of images of ordinary items -- dogs, tables, chairs, etc. -- they then fed it a dataset of 129,450 clinical images of verified, biopsy-proven skin aberrations. These represented more than 2,000 different types of skin diseases and aberrations. All the while, the neural network took in that information and wrote -- "learned" -- rules around how to diagnose those skin aberrations.
Then came a diagnostic test on both the deadlier melanoma and non-melanoma skin cancers. The researchers put their convolutional neural network up against 21 board-certified dermatologists and found its diagnoses to be just as accurate as theirs. Per the study: "The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists."