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A new artificial-intelligence technology for heart imaging can potentially improve care for patients, allowing doctors to examine their hearts for scar tissue while eliminating the need for contrast injections required for traditional cardiovascular magnetic resonance imaging.

A team of researchers who developed the technology, including doctors at UVA Health,reports the success of the approach in a new article in the scientific journal Circulation. The team compared its AI approach, known as virtual native enhancement, with contrast-enhanced cardiovascular magnetic resonance scans now used to monitor hypertrophic cardiomyopathy, the most common genetic heart condition. The researchers found that virtual native enhancement produced higher-quality images and better captured evidence of scar in the heart, all without the need for injecting the standard contrast agent required for cardiovascular magnetic resonance scans.

This is a potentially important advance, especially if it can be expanded to other patient groups, said researcher Dr.Christopher Kramer, the chief of the Division of Cardiovascular Medicine at UVA Health, Virginias only designated Center of Excellence by theHypertrophic Cardiomyopathy Association. Being able to identify scar in the heart, an important contributor to progression to heart failure and sudden cardiac death, without contrast, would be highly significant. Cardiovascular magnetic resonance scans would be done without contrast, saving cost and any risk, albeit low, from the contrast agent.

Hypertrophic cardiomyopathy is the most common inheritable heart disease, and the most common cause of sudden cardiac death in young athletes. It causes the heart muscle to thicken and stiffen, reducing its ability to pump blood and requiring close monitoring by doctors.

The new virtual native enhancement technology will allow doctors to image the heart more often and more quickly, the researchers say. It also may help doctors detect subtle changes in the heart earlier, though more testing is needed to confirm that.

The technology also would benefit patients who are allergic to the contrast agent injected for cardiovascular magnetic resonance scans, as well as patients with severely failing kidneys, a group that avoids the use of the agent.

The new approach works by using artificial intelligence to enhance T1-maps of the heart tissue created by magnetic resonance imaging. These maps are combined with enhanced MRI cines, which are like movies of moving tissue in this case, the beating heart. Overlaying the two types of images creates the artificial virtual native enhancement image.

Based on these inputs, the technology can produce something virtually identical to the traditional contrast-enhanced cardiovascular magnetic resonance heart scans doctors are accustomed to reading only better, the researchers conclude. Avoiding the use of contrast and improving image quality in [cardiovascular magnetic resonance] would only help both patients and physicians down the line, Kramer said.

While the new research examined virtual native enhancements potential in patients with hypertrophic cardiomyopathy, the technologys creators envision it being used for many other heart conditions as well.

While currently validated in the [hypertrophic cardiomyopathy] population, there is a clear pathway to extend the technology to a wider range of myocardial pathologies, they write. [Virtual native enhancement] has enormous potential to significantly improve clinical practice, reduce scan time and costs, and expand the reach of [cardiovascular magnetic resonance] in the near future.

The research team consisted of Qiang Zhang, Matthew K. Burrage, Elena Lukaschuk, Mayooran Shanmuganathan, Iulia A. Popescu, Chrysovalantou Nikolaidou, Rebecca Mills, Konrad Werys, Evan Hann, Ahmet Barutcu, Suleyman D. Polat, HCMR investigators, Michael Salerno, Michael Jerosch-Herold, Raymond Y. Kwong, Hugh C. Watkins, Christopher M. Kramer, Stefan Neubauer, Vanessa M. Ferreira and Stefan K. Piechnik.

Kramer has no financial interests in the research, but some of his collaborators are seeking a patent related to the imaging approach. A full list of disclosures is included in the paper.

The research was made possible by work funded by the British Heart Foundation, grant PG/15/71/31731; the National Institutes of Healths National Heart, Lung and Blood Institute, grant U01HL117006-01A1; the John Fell Oxford University Press Research Fund; and the Oxford BHF Centre of Research Excellence, grant RE/18/3/34214. The research was also supported by British Heart Foundation Clinical Research Training Fellowship FS/19/65/34692, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre at The Oxford University Hospitals NHS Foundation Trust, and the National Institutes of Health.

To keep up with the latest medical research news from UVA, subscribe to theMaking of Medicineblog.

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New Artificial Intelligence Technology Poised to Transform Heart Imaging - University of Virginia

Everyone is talking about artificial intelligence (AI) and machine learning, and some people in the legal field are already taking advantage of these technological capabilities. However, the extraordinary progress in legal AI technology has some lawyers worried about their prospects in their chosen profession, fearing that AI will soon replace them.

This fear is unfounded because it is challenging for AI and machine learning technology to replace the job of a legal professional. On the contrary, technology enables growth and productivity since it increases accuracy, making legal work more efficient.

AI algorithms can transform several tasks, offering excellent corporate compliance, contract management, discovery, and due diligence. Intelligent software is also better, cheaper, and faster for legal research, document retrieval, and predicting case outcomes.

Legal AI allows lawyers to focus on other cognitive tasks, and these are difficult to eliminate. Moreover, AI in the legal world cannot be ignored anymore because sooner or later, lawyers refusing to embrace it will be less efficient and productive.

Clients increasingly demand that their legal representatives and law firms embrace the technologies to offer enhanced services. This means that legal teams can offer their clients increased value and more sophisticated services. In addition, technology adoption allows lawyers to solve clients legal and business problems more effectively and efficiently.

According to Law Technology Today, even though technology can eliminate nearly half of all tasks, only 5% of these can be automated entirely. In numbers, that means that only 23% of a lawyers work can be automated. This means that routine legal tasks are either left to technology or non-lawyers. As a result, legal professionals have more time to work on jobs requiring their specialist and cognitive skills.

AI certainly plays a more significant role now than ever before in predictions. This means that the role of human predictions for outcomes will slowly decline. However, this is not a bad thing for lawyers because the predictions from AI can complement human insights, increasing their value.

This type of advanced legal technology allows legal teams in law firms and legal departments the advantage of being positioned to deliver immediate insights, saving their clients time and money. Artificial intelligence also offers improved decision-making and enhanced efficiency.

Technology cannot replicate what lawyers are trained for, which includes more than their higher cognitive thinking. Lawyers rely on their independent professional judgment, always based on their ability to practice critical thinking and creativity. However, AI allows them to complete their work faster, more efficiently, and more accurately.

AI solutions were initially more expensive to implement but are now far more affordable. Even smaller law firms can scale the available solutions and software to meet their needs.

Concerns about the effort and time required to implement AI initially prevented many legal firms from investing in the technology. However, as AI has become more mainstream, automated solutions are easier to use, requiring less training. There is no evidence that its implementation requires more work. AI improves productivity without requiring extensive training, and its accuracy means that fewer corrections are needed.

Because AI learns through training, the larger the data pool available, the better the AI performs. As a result, todays AI technology is ready to implement when installed into an office system.

The legal profession has embraced technology and has been transformed by it. There are no indications that it is about to make lawyers obsolete. Clients still require the expertise and the professional judgment of specialized and qualified lawyers. However, those lawyers who adopt technology can leverage it to provide excellent, cost-effective legal services and representation for their clients. These lawyers will have a competitive advantage, ensuring growth for their firms.

The senior editor of Legal Scoops, Jacob Maslow, has founded several online newspapers including Daily Forex Report and Conservative Free Press

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Will Robots and Artificial Intelligence Ever Make Lawyers Obsolete? - Legal Scoops

The new IPCC report is unequivocal: more action is urgently needed to avert catastrophic long-term climate impacts. With fossil fuels still supplying more than 80% of global energy, the energy sector needs to be at the heart of this action.

Fortunately, the energy system is already in transition: renewable energy generation is growing rapidly, driven by falling costs and growing investor interest. But the scale and cost of decarbonizing the global energy system remain gigantic, and time is running out.

To-date, most of the energy sectors transition efforts have focused on hardware: new low-carbon infrastructure that will replace legacy carbon-intensive systems. Relatively little effort and investment has focused on another critical tool for the transition: next-generation digital technologies, in particular artificial intelligence (AI). These powerful technologies can be adopted more quickly at larger scales than new hardware solutions, and can become an essential enabler for the energy transition.

Three key trends are driving AIs potential to accelerate energy transition:

1. Energy-intensive sectors including power, transport, heavy industry and buildings are at the beginning of historic decarbonization processes, driven by growing government and consumer demand for rapid reductions in CO2 emissions. The scale of these transitions is huge: BloombergNEF estimates that in the energy sector alone, achieving net-zero emissions will require between $92 trillion and $173 trillion of infrastructure investments by 2050. Even small gains in flexibility, efficiency or capacity in clean energy and low-carbon industry can therefore lead to trillions in value and savings.

2. As electricity supplies more sectors and applications, the power sector is becoming the core pillar of the global energy supply. Ramping up renewable energy deployment to decarbonize the globally expanding power sector will mean more power is supplied by intermittent sources (such as solar and wind), creating new demand for forecasting, coordination, and flexible consumption to ensure that power grids can be operated safely and reliably.

3. The transition to low-carbon energy systems is driving the rapid growth of distributed power generation, distributed storage and advanced demand-response capabilities, which need to be orchestrated and integrated through more networked, transactional power grids.

Navigating these trends presents huge strategic and operational challenges to the energy system and to energy-intensive industries. This is where AI comes in: by creating an intelligent coordination layer across the generation, transmission and use of energy, AI can help energy-system stakeholders identify patterns and insights in data, learn from experience and improve system performance over time, and predict and model possible outcomes of complex, multivariate situations.

AI is already proving its value to the energy transition in multiple domains, driving measurable improvements in renewable energy forecasting, grid operations and optimization, coordination of distributed energy assets and demand-side management, and materials innovation and discovery. But while AIs application in the energy sector has proven promising so far, innovation and adoption remain limited. That presents a tremendous opportunity to accelerate transition towards the zero-emission, highly efficient and interconnected energy system we need tomorrow.

AI holds far greater potential to accelerate the global energy transition, but it will only be realized if there is greater AI innovation, adoption and collaboration across the industry. That is why the World Economic Forum has today released Harnessing AI to Accelerate the Energy Transition, a new report aimed at defining and catalysing the actions that are needed.

The report, written in collaboration with BloombergNEF and Dena, establishes nine 'AI for the energy transition principles' aimed at the energy industry, technology developers and policy-makers. If adopted, these principles would accelerate the uptake of AI solutions that serve the energy transition by creating a common understanding of what is needed to unlock AIs potential and how to safely and responsibly adopt AI in the energy sector.

The principles define the actions that are needed to unlock AIs potential in the energy sector across three critical domains:

1. Governing the use of AI:

2. Designing AI thats fit for purpose:

3. Enabling the deployment of AI at scale:

AI is not a silver bullet, and no technology can replace aggressive political and corporate commitments to reducing emissions. But given the urgency, scale, and complexity of the global energy transition, we cant afford to leave any tools in the toolbox. Used well, AI will accelerate the energy transition while expanding access to energy services, encouraging innovation, and ensuring a safe, resilient, and affordable clean energy system. It is time for industry players and policy makers to lay the foundations for this AI-enabled energy future, and to build a trusted and collaborative ecosystem around AI for the energy transition.

Written by

Espen Mehlum, Head of Energy,Materials & Infrastructure Program-Benchmarking & Regional Action, World Economic Forum

Dominique Hischier, Program Analyst - Energy, Materials Infrastructure Platform, World Economic Forum

Mark Caine, Project Lead, Artificial Intelligence and Machine LearningProject Lead, Artificial Intelligence and Machine Learning, World Economic Forum

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Here's how AI will accelerate the energy transition - World Economic Forum

And naturally, the closer we get to smart intersections becoming more mainstream, the more technologies to power them go live, some of them with insanely advanced capabilities that nobody would have imagined some 10 years ago.

Iteris, for example, a company providing smart mobility infrastructure management, has come up with the worlds first 1080p high-definition (HD) video and four-dimensional (4D) radar sensor with integrated artificial intelligence (AI) algorithms.

In plain English, this is a traffic monitoring sensor that authorities across the world can install in their systems to get 1080p (thats HD resolution) video as well as 4D radar data using a technology bundling AI algorithms.

This means the new sensor is capable of offering insanely accurate detection, and just as expected, it can spot not only cars, but also trucks, bikes, and many other vehicle types. The parent company says the sensor has been optimized to also detect vulnerable road users, such as pedestrians.

In case youre wondering why a traffic management center (TMC) needs such advanced data, the benefits of this sensor go way beyond the simple approach when someone keeps an eye on the traffic in a certain intersection.

TMCs can be linked to connected cars, so the information collected by the sensor can be transmitted right back on the road where the new-generation vehicles can act accordingly based on the detected information. And this is why AI-powered detection is so important, as it offers extra accuracy, preventing errors and wrong information from being sent to connected cars.

In other words, it can help avoid collisions, reduce the speed when pedestrians are detected, and overall optimize the traffic flow because after all, everybody wants to get rid of traffic jams in the first place.

Were probably still many years away from the moment such complex sensors become more mainstream, but Iteris new idea is the living proof the future is already here. Fingers crossed, however, for authorities across the world to notice how much potential is hiding in this new-gen technology.

Excerpt from:
New Traffic Sensor Uses Artificial Intelligence to Detect Any Vehicle on the Road - autoevolution

The NIHR has awarded 2.5 million for new research led by the University of Birmingham that will use artificial intelligence (AI) to produce computer programmes and tools that will help doctors improve the choice of drugs in patients with clusters of multiple long-term conditions.

Called the OPTIMAL study (OPTIMising therapies, discovering therapeutic targets and AI assisted clinical management for patients Living with complex multimorbidity), the research aims to understand how different combinations of long-term conditions and the medicines taken for these diseases interact over time to worsen or improve a patients health.

The study will be led by Dr Thomas Jackson and Professor Krish Nirantharakumar at the University of Birmingham and carried out in collaboration with the University of Manchester, University Hospitals Birmingham NHS Foundation Trust, NHS Greater Glasgow & Clyde, University of St Andrews,and theMedicines and Healthcare Products Regulatory Agency.

An estimated 14 million people in England are living with two or more long-term conditions, with two-thirds of adults aged over 65 expected to be living with multiple long-term conditions by 2035.

Dr Thomas Jackson, Associate Professor in Geriatric Medicine at the University of Birmingham, said: Currently when people have multiple long-term conditions, we treat each disease separately. This means we prescribe a different drug for each condition, which may not help people with complex multimorbidity which is a term we use when patients have four or more long-term health problem.

A drug for one disease can make another disease worse or better, however, presently we do not have information on the effect of one drug on a second disease. This means doctors do not have enough information to know which drug to prescribe to people with complex multimorbidity.

Krish Nirantharakumar, Professor in Health Data Science and Public Health at the University of Birmingham, added: Through our research, we can group such people based on their mixes of disease. Then we can study the effects of a drug on each disease mix. This should help doctors prescribe better and reduce the number of drugs patients need. This will lead to changes in healthcare policy which would benefit most people with complex multimorbidity.

The research is one of a number of studies being funded by the NIHRs Artificial Intelligence for Multiple Long-Term Conditions (AIM) call, which is aligned to the aims of the NHSX AI Lab, that combine data science and AI methods with health, care and social science expertise to identify new clusters of disease and understand how multiple long-term conditions develop over the life course.

The call will fund up to 23 million of research in two waves, supporting a pipeline of research and capacity building in multiple long-term conditions research. The first wave has invested nearly 12 million into three Research Collaborations, nine Development Awards and a Research Support Facility, including the University of Birmingham-led study.

Improving the lives of people with multiple long-term conditions and their carers through research is an area of strategic focus for the NIHR, with its ambitions set out in its NIHR Strategic Framework for Multiple Long-Term Conditions Research.

Professor Lucy Chappell, NIHR Chief Executive and chair of the AIM funding committee, said: This large-scale investment in research will improve our understanding of clusters of multiple long-term conditions, including how they develop over a persons lifetime.

Over time, findings from this new research will point to solutions that might prevent or slow down the development of further conditions over time. We will also look at how we shape treatment and care to meet the needs of people with multiple long-term conditions and carers.

To date NIHR has invested 11million into research on multiple long-term conditions through two calls in partnership with the Medical Research Council, offering both pump-priming funds and funding to tackle multimorbidity at scale.

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New study will use artificial intelligence to improve treatments for people with multiple long-term conditions - University of Birmingham