Researchers trained a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) to identify skincancer by showing it more than 100,000 images of malignant melanomas (the most lethal form of skin cancer), as well as benign moles (or nevi).
The AI uses machine learning to identify and understand what it "sees" in images, with researchers saying it quickly learned the difference between malignant and benign moles.
The dermatologists' performance improved when they were given more information of the patients and their skin lesions.
Aadi Kalloo, an author of the study and senior data analyst at New York's Memorial Sloan Kettering Cancer Center, contends that "a human definitely always needs to be in the loop" when diagnosing skin cancer, adding that the CNN's diagnostic performance was superior to most, but not all, dermatologists.
Around 232,000 new cases of melanoma are diagnosed worldwide every year, which result in 55,500 deaths annually. It also missed fewer melanomas, meaning it was more sensitive than the human dermatologists in terms of detection. The doctors improved their diagnostic accuracy from 86.6 percent to 88.9 percent between phases, but the CNN was able to detect 95 percent of melanomas. "Before doing so, 100 of the most hard lesions were selected to test real dermatologists in comparison to the results of the CNN", he said. But even so, the CNN, which was still working only with the images and was given no additional information, still outperformed the humans. Within the United Kingdom circumstances have greater than doubled because the Nineties, with greater than 15,000 circumstances yearly. Once trained it was tested against 58 dermatologists from across 17 countries, who were shown benign moles and malignant melanomas.
Although the CNN algorithm will not replace human doctors, the researchers believe that it can be used to aid doctors to diagnose skin cancer faster and better.
They highlight a number of issues that would need to be addressed before AI could become standard in clinics, including the difficulty of imaging some melanomas on sites such as the fingers, toes and scalp, and how to train AI sufficiently to recognise atypical melanomas and ones that patients are unaware of.
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.
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