Scientists in Germany, France and the US have trained a convolutional neural network (CNN) to identify skin cancer by showing it more than 100,000 images of malignant melanomas and benign moles.
The research, published in the Annals of Oncology journal, found that the CNN missed fewer melanomas and misdiagnosed benign moles less often than a group of human dermatologists.
The AI uses machine learning to identify and understand what it "sees" in images, with researchers saying it quickly learned the difference between malignant and benign moles.
First author of the study, Professor Holger Haenssle, senior managing physician at the department of dermatology at the University of Heidelberg in Germany, said: "The CNN works like the brain of a child.
"To train it, we showed the CNN more than 100,000 images of malignant and benign skin cancers and moles and indicated the diagnosis for each image.
"Only dermoscopic images were used - that is lesions that were imaged at a 10-fold magnification. With each training image, the CNN improved its ability to differentiate between benign and malignant lesions.
"These findings show that deep learning convolutional neural networks are capable of outperforming dermatologists, including extensively trained experts, in the task of detecting melanomas."
The researchers maintain that, for now, there is no substitute for a thorough clinical examination, adding that more work needs to be done on the technology before it can be used in day-to-day medical treatment.