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CNN : declared in ‘Automated dermatologist’ detects skin cancer with expert accuracy

according to By downloading digital images, researchers can “tell” the computer they are images of skin cancer, or without skin cancer.
“It learns kind of a series of mathematical transformations to essentially transform an image into an answer (to the question) ‘Is there skin cancer or not?’
With a team of researchers, Thrun developed a deep learning computer system to perform the first task in detecting skin cancer: identifying it at a glance.
Would their artificial intelligence system be able to recognize both the most common and the most deadly types of skin cancer: malignant carcinomas and melanomas, respectively?
He explained that doctors commonly use a dermatoscope, a specialized tool, to examine the skin for cancer.

as mentioned in

AI is nearly as good as humans at identifying skin cancer

And since you can train computer vision systems to recognize many object types, you could theoretically apply the technology to other visible conditions.
The algorithm would have to be refined and rigorously tested before put to use in the medical world.
If and when it’s ready for prime time, however, it could do more than save time when you’re at the clinic.
Ideally, you could use the algorithm on your smartphone — imagine taking a photo of an unusual mark on your body and getting an initial verdict without leaving home.
You don’t want a glitch leading to the wrong diagnosis.
Artificial intelligence used to identify skin cancer

as mentioned in Early detection could likely have an enormous impact on skin cancer outcomes.
Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.
What if we could use it to visually screen for skin cancer?
In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of dermatologists.
Diagnosing skin cancer begins with a visual examination.

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