Early detection of skin cancer could be the difference between a simple mole removal or several rounds of chemotherapy. While skin care advice most commonly comes about at the brink of summer, your skin can get damaged by UV rays no matter what time of year, no matter what the weather. Skin cancer accounts for more diagnoses each year than all other cancers, but the good news is that early detection could be the difference between a simple mole removal or malignant cancer that spreads to other parts of the body. A handful of smartphone apps and devices claim to aid early detection and keep you on track with regular self-exams. You can capture photos of suspicious moles or marks and track them yourself, or send them off to a dermatologist for assessment.

As we embark on the next decade of innovations in AI, Daniel Pitchford looks back at the five biggest industry milestones of the 2010s, how they impacted investment in the sector and how they've shaped the advance of technology. The 2010s will be known for the advent of one of the most powerful technologies on the planet – Artificial Intelligence. Over the next decade, as more funding is made available for its development and it becomes more accepted by companies and consumers alike, it is worth reviewing some of the major milestones over the last decade that have made this advancement possible. The game is on, Watson: IBM's Jeopardy triumph The first major milestone of AI hitting the mainstream was when IBM's "super-computer" Watson beat long-standing Jeopardy champions Ken Jennings and Brad Rutter in 2011. Watson won the $1m TV game show with $77,147, leaving Jennings and Ruttner far behind at $24,000 and $21,600 respectively.

SkinVision app claims to detect the most common forms of skin cancer. It is an Android and iOS app that allows the user to assess and track changes in the skin spots over time. The user has to submit a photo of their skin using their smartphone camera. After analyzing the image with Artificial Intelligence algorithm, the app delivers the risk assessment. There are three levels of risk described by the app: low, low with symptoms or high.

Machine learning (ML) systems, an element of what is known as Artificial Intelligence (AI), can outperform people in a number of tasks though they are unlikely to replace people in all jobs, say researchers. In a "Policy Forum" commentary published in the journal Science, Tom Mitchell of Carnegie Mellon University and Erik Brynjolfsson of Massachusetts Institute of Technology (MIT), both in the US, said that tasks that are amenable to ML include those for which a lot of data is available. ML can be a game changer for tasks that already are online, such as scheduling, but its is not a good option if the user needs a detailed explanation for how a decision was made, according to the authors. The researchers explained that machine learning tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML approaches. Jobs that do not require dexterity, physical skills or mobility also are more suitable for ML, the researchers said.

A healthtech company using AI to digitize the diagnosis system has raised a new funding round. MetaOptima Technology has raised $8.6 million to grow its cutting-edge DermEngine platform. The Series A round was led by the Australian Skip Capital and AirTree Ventures, with respective fund principals Scott Farquhar and Daniel Petre joining the MetaOptima board. "Our vision is bold: we want to be in every major dermatology centre and skin cancer clinic in Australia, and we're well on track to making that a reality," said Maryam Sadeghi, CEO and co-founder of MetaOptima. "With the support of AirTree Ventures and Skip Capital, we're confident our platform will continue to shape and change the state of play for both healthcare professionals and patients."

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.

Skin cancer detection won't be turned over to machines anytime soon, but artificial intelligence detected skin cancer more accurately than a large group of international dermatologists in controlled testing, Agence France Presse reports. In an academic study and clinical trial published in Annals of Oncology, the study's lead author, Professor Holger A. Haenssle, of the University of Heidelberg Department of Dermatology, wrote, "Most dermatologists were outperformed by the CNN. Regardless of any physician's level of experience, they may benefit from assistance by a CNN's image classification." The study pitted 58 dermatologists from 17 countries against a deep learning convolutional neural network (CNN). Prior to the test, researchers from Germany, France, and the U.S. taught the CNN to differentiate benign skin lesions from dangerous melanomas. In the process, the team showed more than 100,000 images of correctly identified skin cancers to the neural network, which was designed with Google's Inception v4 CNN architecture.

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.

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.

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.