Apple is edging its way a little further into health care with the release of new iPhone apps that patients can use to manage their own medical conditions -- from diabetes to pregnancy and even depression. While there are hundreds of health-related apps on the market, Apple wants to put its stamp on a new ecosystem of treatment programs. Rather than build the apps itself, the tech giant developed a set of software tools and templates, called "CareKit," that health-care groups and health-tech startups can use to create their own programs. Apple says it wanted to help developers build easy-to-use apps for patients to record symptoms, get useful information, track their progress and even send reports to a doctor. Experts say the CareKit program could help bring standards to a relatively new and unruly industry, while giving Apple a toehold in the growing health-tech market.
Now that it's upending the way you play music, cook, shop, hear the news and check the weather, the friendly voice emanating from your Amazon Alexa-enabled smart speaker is poised to wriggle its way into all things health care. Amazon has big ambitions for its devices. It thinks Alexa, the virtual assistant inside them, could help doctors diagnose mental illness, autism, concussions and Parkinson's disease. It even hopes Alexa will detect when you're having a heart attack. At present, Alexa can perform a handful of health care-related tasks: "She" can track blood glucose levels, describe symptoms, access post-surgical care instructions, monitor home prescription deliveries and make same-day appointments at the nearest urgent care center.
Interpretability is a key factor in the design of automatic classifiers for medical diagnosis. Deep learning models have been proven to be a very effective classification algorithm when trained in a supervised way with enough data. The main concern is the difficulty of inferring rationale interpretations from them. Different attempts have been done in last years in order to convert deep learning classifiers from high confidence statistical black box machines into self-explanatory models. In this paper we go forward into the generation of explanations by identifying the independent causes that use a deep learning model for classifying an image into a certain class. We use a combination of Independent Component Analysis with a Score Visualization technique. In this paper we study the medical problem of classifying an eye fundus image into 5 levels of Diabetic Retinopathy. We conclude that only 3 independent components are enough for the differentiation and correct classification between the 5 disease standard classes. We propose a method for visualizing them and detecting lesions from the generated visual maps.
Syneos Health Communications surveyed around 800 patients in three disease areas (atrial fibrillation, breast cancer, and Type 2 diabetes), as well as 200 Parkinson's disease caregivers. Has the surge in the use of artificial intelligence in and around healthcare touched the lives of those patients and caregivers?
Who wants to live forever? Until recently, the quest to slow ageing or even reverse it was the stuff of legends – or scams. But, today, an evidence-based race to delay or prevent ageing is energising scientists worldwide. Scientists say there are already a number of things we can do to extend life and health, while promising that current and ongoing large-scale trials of drugs and other interventions mean the once-mythical goal of healthy, longer-lived lives is not far away. "Death is inevitable but ageing is not," said Dr Nir Barzilai, founding director of the Institute for Aging Research at the Albert Einstein College of Medicine, New York.