The idea of a headband you can wear to make you smarter may sound like a device from the latest science fiction blockbuster. But experts have revealed such a device in reality – and claim it could increase learning by 40 per cent. And it may not be long before you can get your hands on one, with the designers predicting its use will be common in just five to ten years. The idea of a headband you can wear to make you smarter may sound like a device from the latest science fiction blockbuster. But experts have revealed such a device in reality – and claim it could increase learning by 40 per cent.

In May, Sundar Pichai, CEO of Google, discussed AI applications for digital pathology in his keynote speech to an audience of millions at Google's annual I/O event. Five weeks earlier, the FDA announced it had approved the first whole slide imaging system for primary diagnostic use in pathology. Both events point to the future of pathology and laboratory medicine: Software will soon dominate. Over the past 20 years, software has taken over the world. Retail was dominated by Amazon, Netflix put Blockbuster out of business, and Uber used software to take over the taxi industry.

Surgeons are trained to accurately operate on you when you need it, but robotic assistants could help them get to hard-to-reach areas and boost their accuracy even more. Senhance, the robotic surgical assistant that has just earned the FDA's approval, was designed to accomplish both of those. The machine can help surgeons carry out minimally invasive surgery -- in fact, the FDA has approved its use because after a pilot test involving 150 patients, the agency has concluded that Senhance is as accurate as the da Vinci robot when it came to gynecological and colorectal procedures. According to TransEnterix, the company that developed the machine, it's the first surgical assistant for the abdominal area to get the FDA's approval since 2000. The company claims it's also the first one with eye tracking and force feedback.

The Trump administration's new birth control rule is raising questions among some doctors and researchers. WASHINGTON -- The Trump administration's new birth control rule is raising questions among some doctors and researchers, who say it overlooks known benefits of contraception while selectively citing data that raise doubts about effectiveness and safety. "This rule is listing things that are not scientifically validated, and in some cases things that are wrong, to try to justify a decision that is not in the best interests of women and society," said Dr. Hal Lawrence, CEO of the American Congress of Obstetricians and Gynecologists, a professional society representing women's health specialists. Two recently issued rules -- one addressing religious objections and the other, moral objections -- allow more employers to opt out of covering birth control as a preventive benefit for women under the Obama health care law. Although the regulations ultimately address matters of individual conscience and religious teaching, they also dive into medical research and scholarly studies on birth control. It's on the science that researchers are questioning the Trump administration.

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Is there anyone working on either a decentralized deep learning algorithm, or a consumer facing app that uses AI to help people diagnose themselves? My wife was just diagnosed with CVID a couple of weeks ago, it's like AIDS except it's not Aquired, it's part genetic and part environmental - but it's a rare primary immunodeficiency disease. She's had this her entire life. She was misdiagnosed 3 or 4 times, most recently she was eating gluten free for the last 8 years because she was diagnosed as celiac disease. She's lost most of her hair over the last 6 months and has been in the hospital 3-4 times this year.

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There's a reason you've never seen fully autonomous exoskeletons that help the disabled walk without crutches: Building one is crazy hard. But the founders of a Paris-based startup called Wandercraft are uniquely qualified to do it. They're roboticists who happen to have loved ones in wheelchairs, giving them both the expertise and motivation to develop an exoskeleton that helps users walk again. After years of development, they're nearly ready to show it to the public, following a round of promising patient trials. Wandercraft ran successful preliminary trials with a handful of clients using "Atalante," its latest prototype.

Among the many startups on display at the TechCrunch Disrupt hall in San Francisco this week are companies focused on health and biotech. The products ranged from smart exercise bikes to breast pumps that look like they're from a science-fiction film. One of them, however, stood out from the rest with a large sign that simply read "Take the piss," with the last word in big bold letters. The company is called Testcard, and it claims to tell you if you have a urinary tract infection just by peeing on a postcard. It's just one of many medtech startups vying for legitimacy in an increasingly crowded field.

As humans we feel nothing more viscerally -- in the most literal sense -- than our health. That makes this year's gathering of the Medical Image Computing and Computer Assisted Interventions Society -- MICCAI 2017 -- in Quebec City, Canada, one of the best ways to understand how deep learning is improving the lives of people all around us. The conference brings together leading biomedical scientists, engineers and clinicians to talk about new technologies in medical imaging and computer-assisted intervention, providing an early look at trends poised to sweep through the $6.5 trillion healthcare industry. This will be the group's biggest conference yet, with 1,300 attendees. And deep learning -- which pairs vast quantities of data with sophisticated neural networks to give computers amazing new capabilities -- deserves a lot of the credit, organizers say.