Advanced Symbolics Inc. (ASI), who are an artificial intelligence-driven market research company, have an AI tool called "Polly". This AI program was shown to be more accurate than many polling companies, such as YouGov, when it came to predicting the U.K. General Election result and the large majority of the Conservative and Unionist Party. According to commentary from ASI's head Erin Kelly, the company and its artificial intelligence technology have a strong record of predicting elections and referenda. The success rate embraces the 2015 Canadian Federal Election, the referendum leading to the U.K.'s exit from the European Union in 2016 ('Brexit'), the U.S. 2016 election heartrending in Donald trump, plus the 2019 Canadian Federal Election. The 2019 UK election surprised many political observers by delivering the Conservative and Unionist Party, led by right-winger Boris Johnson, an 80-set majority over the combined opposition parties.

Your data has value, but unlocking it for your own benefit is challenging. Understanding how valuable data are collected and approved for use can help you to get there. Two primary means for differentiating audiences by their data collection methods are site-authenticated data collection and people-based data collection, suggested a recent piece in BulletinHealthcare written by Justin Fadgen, chief corporate development officer for the firm. Site-authenticated data are sourced from individual authentication events, such as when a user completes an online form, and generally agrees to a privacy policy that includes a data use agreement. User data are then be combined with other data sources that add meaning, becoming the basis of advertising targeting for instance.

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic. What gets used in hospitals and clinicians' offices may be much simpler, and a lot less like AI than you would think. In the latest issue of Nature magazine, DeepMind researchers published the results of a deep learning project that can predict kidney failure of patients in the hospital up to 48 hours before the onset of symptoms, with far greater accuracy than existing computer programs for such predictive uses. Also this week, the DeepMind team published the results of a third-party survey of the use of a computer program called "Streams," which uses no artificial intelligence but which can be useful to physicians for things such as being alerted to warning signs about a patient. The first project, the deep learning one, has some ways to go to be put into practice, while the Streams software is already in use by doctors and hospital staff.

New technologies promise to achieve what no politician has yet managed in the 70-year history of the UK's National Health Service (NHS): improving patient care while simultaneously saving money. In the government's 10-year-plan announcement this month, far-reaching new technological measures were announced. They demonstrate just how key digital transformation will be to the future of the health service. Changes in the pipeline include referrals being centralised into the NHS e-Referral Service. GP appointments via Skype are set to become more commonplace too.

DeepMind and its partners in the research, London's Moorfields Eye Hospital and the University College London Institute of Ophthalmology, said they plan prospective clinical trials of the technology in 2019. If those trials are successful, DeepMind said it would seek to create a regulator-approved product that Moorfields could roll out across the U.K. It said the product would be free for an initial five-year period. The software would be the first time a DeepMind AI algorithm using machine learning has ended up in a healthcare product. Alphabet has several initiatives aimed at using artificial intelligence to improve healthcare. Earlier this year, Verily, an Alphabet company that says its goal is to extend human lifespans, teamed up with AI experts from Alphabet's Google, to develop an algorithm that could spot a range of cardiovascular issues from a different kind of retinal image.

University College London Hospitals (UCLH), one of the largest hospitals in London, announced today that it will recruit artificial intelligence to carry out some tasks currently undertaken by doctors and nurses, with the goal of improving emergency room admittance rates, follow-up appointment attendance, and speed for routine tests. Machine learning algorithms supplied by the Alan Turing Institute will pore over admittance data to track how doctors and patients move through the hospital and identify potential bottlenecks. According to a March survey published by the U.K.'s National Health Service, just 76.4 percent of patients requiring urgent care at London hospitals were treated within four hours -- the lowest proportion since 2010, when records began. UCLH CEO Marvel Levi told the Guardian that a future version of the software might prioritize patients based on the severity of their symptom, such as fast-tracking a person suffering from abdominal pain who is likely to have appendicitis, kidney disease, or another critical ailment. A second project, which was developed by UCLH clinical research associate Parashkev Nachev, will flag patients who are most likely to miss appointments, taking into account factors such as age, address, and weather conditions, and will automatically text reminders or even reschedule visits.

This paper describes an avenue for artificial and computational intelligence techniques applied within games research to be deployed for purposes of physical therapy. We provide an overview of prototypical research focussed on the application of motion sensor input devices and virtual reality equipment for rehabilitation of motor impairment: an issue typical of patients of traumatic brain injuries. We highlight how advances in procedural content generation and player modelling can stimulate development in this area by improving quality of rehabilitation programmes and measuring patient performance.

Following Jeremy Corbyn and Theresa May's heated debate over the state of the NHS during yesterday's PMQs, some experts believe that the use of artificial intelligence could hold the key to saving the UK's NHS. AI, in particular cognitive agents that can hold a human-like conversation with the patients, is the key to rescuing the NHS and giving patients and taxpayers the level of care that they expect. Indeed, David Champeaux, director, Global Cognitive Health Solutions at IPsoft, the digital labour company suggests that AI may be the "miracle pill" for the NHS. See also: British public'would use AI' to relieve NHS pressures "The NHS is at risk of a winter of discontent," said Champeaux. "Our healthcare system is buckling under immense pressure resulting from growing demand and capacity constraints. One way to address the staff shortages is to train digital employees equipped with artificial intelligence (AI) to assist doctors and nurses and relieve them from the high volume of routine and administrative tasks and free up more time for patients."

Google DeepMind is launching a project to reduce the time it takes doctors to prepare treatment for head and neck cancers. Alphabet's London-based artificial intelligence division has partnered with the UK's National Health Service and will be conducting the research in coordination with the University College London Hospital. Head and neck cancers are hard to plan treatment for because of their close proximity to important parts of the body. Before any kind of radiation treatment, clinicians will prepare a detailed map of where radiation will be administered on a patient in order to avoid damaging surrounding tissue. DeepMind says planning can take doctors up to four hours for head and neck cancers, and it hopes that by applying machine learning it will be able to automate parts of the process and reduce that planning time down to an hour.

Google DeepMind is launching a project to reduce the time it takes doctors to prepare treatment for head and neck cancers. Alphabet's London-based artificial intelligence division has partnered with the UK's National Health Service and will be conducting the research in coordination with the University College London Hospital. Head and neck cancers are hard to plan treatment for because of their close proximity to important parts of the body. Before any kind of radiation treatment, clinicians will prepare a detailed map of where radiation will be administered on a patient in order to avoid damaging surrounding tissue. DeepMind says planning can take doctors up to four hours for head and neck cancers, and it hopes that by applying machine learning it will be able to automate parts of the process and reduce that planning time down to an hour.