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Medical technology gives healthcare a shot in the arm

#artificialintelligence

Coronavirus has killed hundreds of thousands of people and has strained health systems around the world, but for Tony Young there may be a patch of a silver lining. The pandemic is accelerating use of technology to radically advance medicine and save lives in the future. "There are so many fantastic examples of the way in which technology is empowering our patients and our professionals," says Prof Young, a surgeon and national clinical lead for NHS England. Having launched his own medical-technology start-ups, he is helping to introduce innovations across the UK health service. Digital tools, whether for data management and drug development or enhanced diagnosis and treatment, have sharply improved the response to the threat of infection and all sorts of disease.


How artificial intelligence is changing the GP-patient relationship - Pulse Today

#artificialintelligence

'Alexa, what are the early signs of a stroke?' GPs may no longer be the first port of call for patients looking to understand their ailments. 'Dr Google' is already well established in patients' minds, and now they have a host of apps using artificial intelligence (AI), allowing them to input symptoms and receive a suggested diagnosis or advice without the need for human interaction. And policymakers are on board. Matt Hancock is the most tech-friendly health secretary ever, NHS England chief executive Simon Stevens wants England to lead the world in AI, and the prime minister last month announced £250m for a national AI lab to help cut waiting times and detect diseases earlier. Amazon even agreed a partnership with NHS England in July to allow people to access health information via its voice-activated assistant Alexa.


AI Technique Aims to Prevent Medical Imaging Cyber Threats

#artificialintelligence

In May 2017, National Health Service (NHS) hospitals in England and Scotland were virtually shut down for several days because of the global WannaCry cyberattack. The attack resulted in the cancellation of thousands of appointments and operations and some NHS services had to turn away noncritical emergencies. Up to 70,000 devices, including computers, MRI scanners, blood-storage refrigerators, and operating room equipment may have been affected. And in 2016, the Hollywood Presbyterian Medical Center in Los Angeles paid $17,000 in bitcoin to a hacker to unlock data that had been encrypted in an attack. Hospital staff struggled to deal with the loss of email and access to patient data.


The Impact of Artificial Intelligence on Surgery

#artificialintelligence

"Ten years of transition in a month" is a common explanation of how the pandemic is driving the use of telemedicine. Before the virus, video appointments accounted for just 1% of the 350 m consultations that the UK National Health Service manages each year. Companies like Docly, eConsult, and AccuRx are changing this. The latter states that 90% of primary care clinics in England are now using their video-calling method. Remote surgery is the most dramatic type of telemedicine.


NHS using drones to deliver coronavirus kit between hospitals

The Guardian

An NHS drone is being used to courier Covid-19 samples, blood tests and personal protective equipment between hospitals in England. It is hoped that the trials, backed by a £1.3m grant from the UK Space Agency, can establish a network of air corridors for electric drones to navigate using GPS. The remote-controlled drone, which will be piloted by an ex-military fast jet or helicopter instructor, will initially fly between Essex's Broomfield hospital, Basildon hospital and the Pathology First laboratory in Basildon. The project is the idea of Apian, a healthcare drone startup founded by Christopher Law and Hammad Jeilani. "Covid-19 has highlighted challenges in NHS supply chain logistics," said Law.


The Impact of Artificial Intelligence on Surgery

#artificialintelligence

"We've witnessed ten years of change in a month" is a typical description of how the pandemic is accelerating the use of telemedicine. Before the virus, video appointments made up only 1% of the 350m consultations which Britain's National Health Service handles each year. Companies like Docly, eConsult and AccuRx are changing that. The latter claims that 90% of primary care clinics in England are now using its video-calling system. The most dramatic form of telemedicine is remote surgery.


Funding boost for artificial intelligence in NHS to speed up diagnosis of deadly diseases

#artificialintelligence

Patients will benefit from major improvements in technology to speed up the diagnosis of deadly diseases like cancer thanks to further investment in the use of artificial intelligence across the NHS. A £50 million funding boost will scale up the work of existing Digital Pathology and Imaging Artificial Intelligence Centres of Excellence, which were launched in 2018 to develop cutting-edge digital tools to improve the diagnosis of disease. The 3 centres set to receive a share of the funding, based in Coventry, Leeds and London, will deliver digital upgrades to pathology and imaging services across an additional 38 NHS trusts, benefiting 26.5 million patients across England. Pathology and imaging services, including radiology, play a crucial role in the diagnosis of diseases and the funding will lead to faster and more accurate diagnosis and more personalised treatments for patients, freeing up clinicians' time and ultimately saving lives. Technology is a force for good in our fight against the deadliest diseases – it can transform and save lives through faster diagnosis, free up clinicians to spend time with their patients and make every pound in the NHS go further.


Government pledges £50m for AI to improve diagnosis of deadly disease

#artificialintelligence

The government has pledged £50 million for further investment in artificial intelligence to improve diagnostics across the NHS. The investment aims to speed up the diagnosis of deadly diseases like cancer through delivering digital upgrades to pathology and imaging services across the country. It will scale up the work of the existing Digital Pathology and Imaging AI Centres of Excellence, launched in 2018 to develop cutting-edge digital tools to improve the diagnosis of disease. The three centres set to receive a share of the funding, based in Coventry, Leeds and London, will deliver digital upgrades to pathology and imaging services across an additional 38 NHS Trusts, benefiting some 26.5 million patients across England, according to the Department of Health and Social Care (DHSC). It's hoped the funding will lead to faster and more accurate diagnosis and more personalised treatments for patients, freeing up clinicians' time and ultimately saving lives.


A unified machine learning approach to time series forecasting applied to demand at emergency departments

arXiv.org Machine Learning

There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. Using 8 years of electronic admissions data from two major acute care hospitals in London, we develop a novel ensemble methodology that combines the outcomes of the best performing time series and machine learning approaches in order to make highly accurate forecasts of demand, 1, 3 and 7 days in the future. Both hospitals face an average daily demand of 208 and 106 attendances respectively and experience considerable volatility around this mean. However, our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of +/- 14 and +/- 10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Our analysis compares machine learning algorithms to more traditional linear models. We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In addition to comparing and combining state-of-the-art forecasting methods to predict hospital demand, we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators.


Abolish the #TechToPrisonPipeline

#artificialintelligence

The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release.[38] At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.