Health & Medicine

Health system envisions center as a hub of collaboration between physicians, researchers, computer scientists and statisticians to find new ways to use AI technologies for improving patient outcomes. The new Cleveland Clinic Center for Clinical Artificial Intelligence, launched by Cleveland Clinic Enterprise Analytics, will leverage machine learning, deep learning and natural language processing to power diagnostics, disease prediction, as well as treatment planning. "We realize that there's a lot of opportunity in the space of artificial intelligence to advance patient care and outcomes," says Aziz Nazha, MD, who has been appointed director of the center and associate medical director for AI. "The mission is to harness the power of artificial intelligence to improve healthcare delivery and medical research by focusing on the clinical needs of the patients." By bringing together specialists from various departments, including genetics, IT, laboratory, pathology and radiology, the center will develop innovative clinical AI applications such as machine learning algorithms designed to reduce the risk of hospital readmission and to predict patient response to cancer treatments, according to Nazha.

It's now obvious that AI, Machine Learning and Deep Learning are no longer buzzwords as they're getting more and more present in every industry. Notwithstanding the trend has been overhyped in 2017, we are now certain that these technologies will be ubiquitous by 2020. Scientific research has not been left behind and AI has been the trigger of a significant change in this domain. Machine Learning techniques such as medical image processing, biological features identification by segmentation and AI-based medical diagnosis are officially adopted in by health researchers with an uncritical approach. This disruptive effect caused by AI is finally very simple to understand.

Dozens of scientists, health care professionals and academics have written a letter to the U.N. calling for an international ban of autonomous killer robots, saying recent advances in artificial intelligence "have brought us to the brink of a new arms race in lethal autonomous weapons." The letter, which has been signed by more than 70 health care professionals and was put together by the Future of Life Institute, states that lethal autonomous weapons could fall into the hands of terrorists and despots, lower the barrier to armed conflict and "become weapons of mass destruction enabling very few to kill very many." "Furthermore, autonomous weapons are morally abhorrent, as we should never cede the decision to take a human life to algorithms," the letter continues. "As healthcare professionals, we believe that breakthroughs in science have tremendous potential to benefit society and should not be used to automate harm. We therefore call for an international ban on lethal autonomous weapons."

According to the United Nations, 1 billion people globally live with disabilities, and as many as 70 million of them live in India. In India, individuals with disabilities face barriers to success from nonexistent or inaccessible infrastructure, as well as prejudicial beliefs and discriminatory laws. With those challenges in mind, Kyle Keane, lecturer and research scientist in MIT's Department of Materials Science and Engineering, was invited to conduct a 2018 summer workshop in Chennai, India. He reached out to MIT-India, part of MIT International Science and Technology Initiatives (MISTI), for support in bringing a student with him. They not only agreed, but MIT-India Managing Director Mala Ghosh replied, "Why not bring an entire class?"

The excitement around any new technology comes with a side order of fears about how that system or service will affect people. In key areas like cloud computing and social media, it often feels as if the regulators are having to play catch-up with the tech firms that create these innovations and the businesses that exploit them. Yet it is in the area of artificial intelligence (AI) that these fears are perhaps greater than anywhere else. Rather than just being a technology that people will themselves use, some experts believe AI could instead help to replace human decision-making at work and at home. So, how can businesses work to reduce fears and create AI systems that exploit big data ethically?

If you have recently started doing work in deep learning, especially image recognition, you might have seen the abundance of blog posts all over the internet, promising to teach you how to build a world-class image classifier in a dozen or fewer lines and just a few minutes on a modern GPU. What's shocking is not the promise but the fact that most of these tutorials end up delivering on it. To those trained in'conventional' machine learning techniques, the very idea that a model developed for one data set could simply be applied to a different one sounds absurd. The answer is, of course, transfer learning, one of the most fascinating features of deep neural networks. In this post, we'll first look at what transfer learning is, when it will work, when it might work, and why it won't work in some cases, finally concluding with some pointers at best practices for transfer learning.

In medicine, diseases can be detected at a much earlier stage, and we can support the elderly to live a more independent life, simply by identifying deviations from their usual behaviour and body movements. The UK Government recently announced that AI could help the National Health Service predict those in an early stage of cancer, to ultimately prevent thousands of cancer-related deaths by 2033. The algorithms will examine medical records, habits and genetic information pooled from health charities, the NHS and AI. Virtual nurses could transform patient care, being available round the clock to answer questions, monitor patients and provide quick answers. Beyond healthcare, AI could inform a better allocation of resources in energy, logistics and transport, as well as support the digital advertising industry with more efficient marketing.

In 1891, when the German biologist Hans Driesch split two-cell sea urchin embryos in half, he found that each of the separated cells then gave rise to its own complete, albeit smaller, larva. Somehow, the halves "knew" to change their entire developmental program: At that stage, the blueprint for what they would become had apparently not yet been drawn out, at least not in ink. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Since then, scientists have been trying to understand what goes into making this blueprint, and how instructive it is. It's now known that some form of positional information makes genes variously switch on and off throughout the embryo, giving cells distinct identities based on their location.

The patient appeared to be dying. She had chronic lung disease, and she had been told she had little reserve left and had barely survived on home oxygen for the past few years. Each time she picked up a lung infection, the buzzards circled closer. Now she had tripped, fallen, broken a bone, had surgery, and her subsequent infection seemed to have pushed her past the point of no return. Still, I held off the palliative care/comfort care team for as long as I could, and she rallied.

Robots might be a little more appealing -- and more practical -- if they're not made of hard, cold metal or plastic, but of a softer material. Researcher at Brown University believe they've developed a new material that could be ideal for "soft robotics." It's already demonstrated that it can pick up small, delicate objects, and it could form customized microfluidic devices -- sometimes called "labs-on-a-chip" and used for things like spotting aggressive cancers and making life-saving drugs in the field. The 3D-printed hydrogel is a dual polymer that's capable of bending, twisting or sticking together when treated with certain chemicals. One polymer has covalent bonds, which provide strength and structural integrity.