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Google AI outperforms 58 dermatologists in detecting melanoma

pharmafile | May 30, 2018 | News story | Research and Development | AI< Google, Cancer, melanoma, pharma, skin cancer

An deep learning artificial intelligence system has demonstrated its ability to outperform over 50 human dermatologists in the detection of melanoma, a skin cancer which kills around 55,000 worldwide each year.

A team of researchers across France, Germany and the US trained Google’s Inception v4 convolutional neural network (CNN) to identify skin cancers by exposing it to 100,000 images of moles in order to distinguish between benign and malignant ones.

The team then pitted the CNN against 58 dermatologists from 17 countries; over half of the professionals were classified as experts with more than five years’ experience, 19% possessed between two and five years’ experience, and 29% had less than two years’ experience.

And the CNN came out on top, successfully identifying 95% of melanoma cases, compared to the 86.6% success rate by the dermatologists. Additionally, the CNN missed fewer melanomas and misdiagnosed fewer benign moles, which the team noted would result in less unnecessary surgery in the real world.

Importantly, the dermatologists performed better in their identification when they were given more information on the patient and their condition. The study reinforced the effectiveness of AI and machine learning platforms in the diagnosis of skin cancer specifically, which could allow for the disease to be caught early – a crucial requirement in achieving successful treatment. There are around 232,000 new cases of melanoma around the world each year.

To this end, the team noted that such tools could prove invaluable to humans in the field: “Irrespective of any physicians’ experience, they may benefit from assistance by a CNN’s image classification,” they noted.

Matt Fellows