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. 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.
The NHS has started trials of a machine-learning system designed to help hospitals in England anticipate the demand on resources caused by COVID-19. The COVID-19 Capacity Planning and System (CPAS) is being piloted at four acute hospitals in England to demonstrate whether it can help the NHS predict the demand for equipment like ICU beds and ventilators. If successful, CPAS will be rolled out nationally. From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. NHS Digital told ZDNet the initiative marked the "first time any project of this scale and scope using machine learning has been rolled out in the NHS."
Amazon A2I helps developers add human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third party vendors, or their own employees. Amazon A2I makes it easier for developers to build the human review system, structure the review process, and manage the human review workforce. For example, developers could use Amazon A2I to quickly spin up and manage a workforce of humans to review and validate the accuracy of machine learning predictions for an application that extracts financial information from scanned mortgage documents or an application that uses image recognition to identify counterfeit items online, so that the quality of results improve over time. There are no upfront commitments to use Amazon A2I, and users pay only for each review needed. Today, machine learning provides highly accurate predictions (known as "inferences") for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language.
Trials have begun of a system that will use machine learning to help predict the upcoming demand for intensive care (ICU) beds and ventilators needed to treat patients with COVID-19 at individual hospitals and across regions in England. The COVID 19 Capacity Planning and Analysis System (CPAS), developed by NHS Digital data scientists and a team of researchers from the University of Cambridge, and using data from Public Health England, will support hospitals to plan more accurately and help ensure that resources are deployed to best effect to support COVID-19 throughout the NHS. The first stage alpha trials began this week at four hospitals, aiming to demonstrate the relative accuracy of the system and fine tune it to best meet the needs of hospitals. "With the pressure being placed on intensive care by the current coronavirus pandemic it is essential to be able to predict demand for critical care beds, equipment and staff,"says NHS Digital Chief Medical Officer Professor Jonathan Benger. "CPAS allows individual hospitals to plan ahead, ensuring they can give the best care to every patient. At the same time, the wider NHS can ensure that the ventilators, other equipment and drugs that each intensive care unit will need are in place at exactly the time they are required. In the longer term, it is hoped that CPAS can be used to predict hospital length of hospital stay, discharge planning and wider intensive care demand in the time that will come after the pandemic."
The NHS is turning to artificial intelligence (AI) to help predict upcoming demand for intensive care beds and ventilators during the coronavirus pandemic across England. Trials of the predictive system, known as the COVID 19 Capacity Planning and Analysis System (CPAS), began today at four hospitals. It harnesses the principles of machine learning – algorithms that find and apply patterns in data – to provide statistics, forecasts and simulation environments to the NHS to better plan resources during the pandemic. For example, predictions made by the machine learning system could inform a hospital that capacity will be reached in advance, giving it time to bring in extra resources or share capacity with neighbouring hospitals. If CPAS proves to be accurate, the NHS will look to roll it out across the rest of the country.
Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data. However, using the data available for prediction and decision making is hampered by the current lag in our ability to uncover and quantify true interactions that explain the outcomes.We are interested in areas such as intensive care medicine, which are characterised by i) continuous monitoring of multivariate variables and non-uniform sampling of data streams, ii) the outcomes are generally governed by interactions between a small set of rare events, iii) these interactions are not necessarily definable by specific values (or value ranges) of a given group of variables, but rather, by the deviations of these values from the normal state recorded over time, iv) the need to explain the predictions made by the model. Here, while numerous data mining models have been formulated for outcome prediction, they are unable to explain their predictions. We present a model for uncovering interactions with the highest likelihood of generating the outcomes seen from highly-dimensional time series data. Interactions among variables are represented by a relational graph structure, which relies on qualitative abstractions to overcome non-uniform sampling and to capture the semantics of the interactions corresponding to the changes and deviations from normality of variables of interest over time. Using the assumption that similar templates of small interactions are responsible for the outcomes (as prevalent in the medical domains), we reformulate the discovery task to retrieve the most-likely templates from the data.
Global tech giant Fujifilm has given Welsh healthcare professionals an exclusive first look at its latest medical innovation ahead of the product's global clinical launch. The Japanese multinational photography and imaging company, which is a pioneer in medical imaging and diagnostics equipment, previewed its new artificial intelligence (AI) software, which is integrated into a mobile radiography system, at an event hosted by Life Sciences Hub Wales. Flown in from Tokyo for the event, the FDR nano is a mobile X-ray unit that uses integrated AI technology to quickly identify and flag abnormalities that need further investigation. The product is the first Fujifilm AI-enabled mobile unit in Europe and is due to commence clinical trials in a UK hospital. The AI in the unit highlights suspicious areas on an image to the radiographer taking the X-ray using a heat map.
In order to help build increasingly effective care pathways in healthcare, modern artificial intelligence technologies must be adopted and embraced. Events such as the AI & Machine Learning Convention are essential in providing medical experts around the UK access to the latest technologies, products and services that are revolutionising the future of care pathways in the healthcare industry. AI has the potential to save the lives of current and future patients and is something that is starting to be seen across healthcare services across the UK. Looking at diagnostics alone, there have been large scale developments in rapid image recognition, symptom checking and risk stratification. AI can also be used to personalise health screening and treatments for cancer, not only benefiting the patient but clinicians too – enabling them to make the best use of their skills, informing decisions and saving time.
AI is changing more than what computers can do and how we communicate and interact with technology. AI is changing the very nature of work, of hiring, reinforcing the imperative for life-long learning, and serving as a catalyst for organisation-wide change. How to Prepare a Generation of AI-first Workers The NHS is set to lose an estimated 350,000 staff by 2030 in the UK--a quarter of its workforce. It's a Catch 22 situation: staff are leaving because of their intolerable workloads, caused by an already acute level of staff shortages. While the National Health Service (NHS) has no magic wand to summon up a small army of suitably qualified staff, they are looking to Artificial Intelligence (AI) to help.
The first systematic review and meta-analysis of its kind finds that artificial intelligence (AI) is just as good at diagnosing a disease based on a medical image as healthcare professionals. However, more high quality studies are necessary. A new article examines the existing evidence in an attempt to determine whether AI can diagnose illnesses as effectively as healthcare professionals. To the authors' knowledge -- that is, a vast team of researchers led by Professor Alastair Denniston from the University Hospitals Birmingham NHS Foundation Trust in the United Kingdom -- this is the first systematic review that compares AI performance with medical professionals for all diseases. Prof. Denniston and team searched several medical databases for all studies published between 1st of January 2012 and 6th of June 2019.