Dermatologists hate him! Meet the skin-cancer detecting robot

Dermatologists hate him! Meet the skin-cancer detecting robot

The 58 dermatologists involved in the study came from a variety of backgrounds and 17 countries.

"The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists", the study's first author Holger Haenssle of the University of Heidelberg said in a statement.

Then, four weeks later they were given clinical information about the patient, including age, sex and position of the lesion, and close-up images of the same 100 cases (level II) and asked for diagnoses and management decisions.

The paper appeared in the journal Annals of Oncology.

A group of researchers trained an artificial intelligence to accurately diagnose malignant melanomas and pitted it against actual dermatologists.

Each year, there are an estimated 232,000 new cases of malignant melanoma worldwide and around 55,500 deaths from the disease, according to the International Agency for Research on Cancer, a specialized cancer agency of the World Health Organization. Doctors correctly found skin cancer and benign moles 86.6 percent of the time, but CNN was correct 95 percent of the time.

The ISIC archive, led by Sloan Kettering, now contains more than 34,000 dermoscopic images collected from leading dermatology centers around the world, according to Kalloo, who is the primary data manager. But even so, the CNN, which was still working only with the images and was given no additional information, still outperformed the humans. Melanoma can be cured if diagnosed early, but unfortunately often the diagnosis is delayed when the cancer progresses.

"These findings present that deep studying convolutional neural networks are able to out-performing dermatologists, together with extensively educated consultants, within the process of detecting melanomas", Prof Haenssle mentioned. The CNN also had some limitations of its own, such as poor performance with images of melanomas on certain sites such as the fingers, toes, and scalp. "Currently, there is no substitute for a thorough clinical examination", concluded the researchers.

Scientists got the computer - known as a deep learning convolutional neural network (CNN) - to check pictures from 100 patients.

That being said, these impressive results indicate that we're about to experience a paradigm shift, not only in dermatology but in just about every medical field, thanks to developments in artificial intelligence.