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Artificial intelligence used to identify skin cancer

It can be daunting enough making a doctor’s appointment but imagine a scenario where you had the option to receive a diagnosis through your smartphone.

Although it is early days, researchers at Stanford University have developed an Artificial intelligence algorithm capable of identifying skin cancer in photographs, in hopes of creating better access to medical care.

The Stanford University team created a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer.

“We made a very powerful machine learning algorithm that learns from data,” said Andre Esteva, co-lead author of the paper. “Instead of writing into computer code exactly what to look for, you let the algorithm figure it out.”

Re-purposed from software developed by Google that had learned to spot the difference between images of cats and dogs, the AI learned to spot the hallmarks of the most common type of skin cancer: carcinoma, and the most deadly: melanoma.

Only one in 20 skin cancers are melanoma, yet the tumour accounts for three-quarters of skin cancer deaths.

“There’s no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own,” said Brett Kuprel, co-lead author of the paper and a graduate student in the Thrun lab.

“We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy – the labels alone were in several languages, including German, Arabic and Latin.”

From the very first test, it performed with inspiring accuracy and can identify skin cancer in photographs with the same accuracy as trained doctors, say scientists.

The Stanford University team said the findings were “incredibly exciting” and would now be tested in clinics.

Eventually, they are hopeful that using AI could revolutionise healthcare by transforming anyone’s smartphone into a cancer scanner.

“My main eureka moment was when I realized just how ubiquitous smartphones will be,” said Esteva.

“Everyone will have a supercomputer in their pockets with a number of sensors in it, including a camera. What if we could use it to visually screen for skin cancer? Or other ailments?”

The team believes it will be relatively easy to transition the algorithm to mobile devices but there still needs to be further testing in a real-world clinical setting.

Read more on the study and findings here.


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