BERLIN: An artificial intelligence system can better detect skin cancer than experienced dermatologists, a study has found. Researchers trained a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) to identify skin cancer 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). They compared its performance with that of 58 international dermatologists and found that the CNN missed fewer melanomas and misdiagnosed benign moles less often as malignant than the group of dermatologists. "The CNN works like the brain of a child. To train it, we showed the CNN more than 100,000 images of malignant and benign skin cancers and moles and indicated the diagnosis for each image," said Holger Haenssle, from the University of Heidelberg in Germany.

We hear and read in the popular media about Artificial Intelligence (AI) all the time. We have movies about them. We hear about Elon Musk and Stephen Hawking warning us about AI's apocalyptic consequences. We hear from the World Economics Forum about AI's effect on taking away our jobs. We hear about how disruptive AI will be for businesses.

Artificial intelligence beats experienced dermatologists when it comes to skin cancer diagnosis, according to a study published in the journal Annals of Oncology. Researchers trained a deep learning convolutional neural network (CNN) to distinguish malignant melanomas from benign moles using more than 100,000 photographs. Then, they compared its success rate against those of 58 dermatologists from 17 countries. "The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists, and it misdiagnosed fewer benign moles as malignant melanoma, which means it had a higher specificity; this would result in less unnecessary surgery," Holger Haenssle, senior managing physician at the Department of Dermatology at the University of Heidelberg, Germany, said in a statement. Neural networks are a type of machine learning software that operate a bit like the brain's neural networks.

Accepted Manuscript Editorial 28 May 2018 Artificial intelligence for melanoma diagnosis: How can we deliver on the promise? Published: 28 May 2018 Section: Editorial Melanoma Corrected Proof Research Article 28 May 2018 Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists H A Haenssle; C Fink; R Schneiderbauer; F Toberer; T Buhl ... Annals of Oncology, mdy166, https://doi.org/10.1093/annonc/mdy166 Published: 28 May 2018 Section: Original Article Corrected Proof Review Article 28 May 2018 Gastrointestinal stromal tumours: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy095, https://doi.org/10.1093/annonc/mdy095 Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 28 May 2018 Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy096, https://doi.org/10.1093/annonc/mdy096 Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 23 May 2018 Hodgkin lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up D A Eichenauer; B M P Aleman; M André; M Federico; M Hutchings ... Annals of Oncology, mdy080, https://doi.org/10.1093/annonc/mdy080 Published: 23 May 2018 Section: clinical practice guidelines Accepted Manuscript Review Article 22 May 2018 Advances in the systemic treatment of melanoma brain metastases I C Glitza Oliva; G Schvartsman; H Tawbi Annals of Oncology, mdy185, https://doi.org/10.1093/annonc/mdy185

It's a well-worn path that's followed when new technology comes around. It often starts in universities or large companies with big research budgets and an appetite for taking chances. There the new thing can be sussed out by patient students or engineers not graded on productivity and not facing a tape-out or go-to-market deadline. While they're willing to struggle along, knowing that they might be on the cusp of something great, at some point, the tedious aspects of making the new technology work become… well… tedious. And so they cobble together rough-and-ready tools that will save them some work.

Artificial intelligence (AI) is becoming a huge tool for people in many fields, especially in healthcare. A new artificial intelligence, deep learning network called deep learning convolutional neural network (CNN) has been developed to help dermatologists. Through testing, the new CNN has been proven to detect skin cancer even better than expert dermatologists. The CNN was developed by researchers from around in the world, including Germany, the United States and France. The new development is important because 232,000 new malignant melanoma cases are found around the world every year and 55,500 people die from melanoma cases each year. If the CNN is developed to be used as a tool in a dermatologist's office, many lives could be saved.

In January last year scientists reported that artificial intelligence was almost as effective at identifying skin cancer as dermatologists. In an experiment between a deep learning convolutional neural network (or CNN) and 58 dermatologists, researchers found that human dermatologists accurately identified 86.6 percent of skin cancers from a range of images, compared to 95 percent for the CNN. The study's first author, Holger Haenssle of the University of Heidelberg, said that the CNN missed fewer melanomas, "meaning it had higher sensitivity than the dermatologists". He added that it misdiagnosed fewer benign moles as malignant melanoma, which would ultimately result in less unnecessary surgery. The AI was taught to distinguish dangerous skin lesions from benign ones after being shown more than 100,000 images.

Douglas Hofstadter in his book "I am a Strange Loop" coined this idea: Where he describes this self-referential mechanism as what describes the unique property of minds. The strange loop is a cyclic system that traverses several layers in a hierarchy. By moving through this cycle one finds oneself where one originally started. Coincidentally enough, this'strange loop' is in fact is the fundamental reason for what Yann LeCun describes as "the coolest idea in machine learning in the last twenty years." Loops are not typical in Deep Learning systems.

A computer was better than human dermatologists at detecting skin cancer in a study that pitted people against machines in the quest for better, faster diagnostics, researchers said on Tuesday. A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The machine – a deep learning convolutional neural network or CNN – was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at "expert" level with more than five years of experience, 19% had between two and five years' experience, and 29% were beginners with less than two years under their belt. "Most dermatologists were outperformed by the CNN," the research team wrote in a paper published in the journal Annals of Oncology.

PARIS - A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against machine in the quest for better, faster diagnostics, researchers said Tuesday. A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The machine -- a deep learning convolutional neural network or CNN -- was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at'expert' level with more than five years of experience, 19 percent had between two and five years' experience, and 29 percent were beginners with less than two years under their belt. 'Most dermatologists were outperformed by the CNN,' the research team wrote in a paper published in the journal Annals of Oncology.