Quartz state: Your smartphone could soon be a powerful tool for detecting skin cancer — Quartz

Research findings published in Nature today hint at a future where anyone, anywhere, might be able to perform a basic skin cancer screening on a smartphone. Utilizing machine learning, a Stanford team, including Udacity's Sebastian Thrun, was able to match the accuracy of dermatologists at identifying skin cancer. If caught early, skin cancer isn't particularly deadly. This work underscores efforts by Google's DeepMind and Microsoft to classify conditions that can lead to blindness using machine learning. Computational capability is still paramount for most tasks in machine learning, something that just doesn't exist yet on mobile.

Could your smartphone catch skin cancer?

In Australia, melanoma is the third most common cancer in Australian women and the fourth most common cancer in men, and the most common cancer in Australians aged 15-44. Sarah Wiedersehn Australian Associated PressA computer algorithm can catch skin cancer with the same accuracy as a doctor, a study has found. "We realised it was feasible, not just to do something well, but as well as a human dermatologist," Prof Thrun said. "Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," Prof Swetter said. Researchers say the "exciting" discovery could soon result in smartphones being used as cancer scanners.

Your smartphone could soon be a powerful tool for detecting skin cancer — Quartz

They'll be some of the weirder selfies you've ever taken, but a study using artificial intelligence to analyze images of skin lesions suggests that smartphones may soon help humans detect skin cancer. As it sees more images, it can draw more accurate conclusions about benign, malignant, and non-neoplastic lesions. (Non-neoplastic lesions could be inflammation.) For now, the Stanford team's algorithm only runs on full computers, but they're interested in pursuing a smartphone app. After looking at hundreds of images of a specific lesion, the AI begins to understand similarities between the images.
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