Medicine

AI outperforms man in detecting skin cancer

AI outperforms man in detecting skin cancer

Specifically, they trained and validated Google's Inception v4 CNN architecture using dermoscopic images at a 10-fold magnification and corresponding diagnoses. The AI system was provided with the training to distinguish in between the hazardous skin lesions from benign ones.

The team of 58 dermatologists was separated into groups.

Another 19 per cent of specialists had between two and five years' experience, and 29 per cent were beginners with less than two years in their field. Meaning, humans still shouldn't rely on AI to make diagnoses.

At level I, humans could accurately detect melanomas 86.6% of the time and correctly identified benign lesions with an average score of 71.3%.

Malignant melanoma is the most deadly type of skin cancer.

The dermatologists were able to detect more melanomas when they had context about the patients' background - 88.9% of malignant melanomas and 75% of non-malignant lesions - yet, this still wasn't as accurate as the CNN. Over 50% of the dermatologists were experts over 5 years of experience. Haenssle said that the findings show that "deep learning convolutional neural networks are capable of out-performing dermatologists, including extensively trained experts, in the task of detecting melanomas". They also believe there are 232,000 new cases of skin cancer and 55,500 deaths are reported each year. In the United Kingdom cases have more than doubled since the Nineties, with more than 15,000 cases annually.

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"I have been involved in research projects that aim at improving the early detection of melanoma in its curable stages for nearly 20 years", Haenssle said.

The researchers said they did not foresee artificial intelligence to replace dermatologists, but would help them make better diagnoses in the future.

But despite the promising results from the experiment, the researchers do not envisage that the CNN would take over from dermatologists in diagnosing skin cancers, but that it could be used as an additional aid. "We are now planning prospective studies to assess the real-life impact of the CNN for physicians and patients", according to Haenssle. The researchers then developed a pair of test sets (which were not involved in the system's training) and collected 58 dermatologists from 17 different countries.

A form of artificial intelligence was taught to recognize signs of the disease after being shown more than 100,000 images of melanoma.

More research needs to be carried out ensure the CNN is accurate when diagnosing areas hard to image such as the fingers, toes and scalp, and to train it to pick up unusual melanomas, as well as lesions that patients haven't yet found.


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