Goto

Collaborating Authors

Minnesota


Artificial Intelligence Algorithm Analyzes Chest X-Rays to Detect COVID-19 in Seconds

#artificialintelligence

A team of researchers at the University of Minnesota (Minneapolis, MN, USA) recently developed and validated an artificial intelligence (AI) algorithm that can evaluate chest X-rays to diagnose possible cases of COVID-19. Working together with M Health Fairview and Epic, the algorithm will be available at no cost to other health systems through Epic, the medical records software used by many health care organizations across the country. When a patient arrives in the emergency department with suspected COVID-19 symptoms, clinicians order a chest X-ray as part of standard protocol. The algorithm automatically evaluates the X-ray as soon as the image is taken. If the algorithm recognizes patterns associated with COVID-19 in the chest X-ray - within seconds - the care team can see within Epic that the patient likely has the virus.


Black Women In Artificial Intelligence - Beyond The Lab - Elizabeth M. Adams

#artificialintelligence

Join us for the world premiere of Black Women In Artificial Intelligence - Beyond the Lab with our special guest author Elizabeth M. Adams a technology integrator, working at the intersection of Cyber Security, AI Ethics and AI Governance, focused on Ethical Tech Design. She also passionately teaches, advises, consults, speaks and writes on the critical subjects within Diversity & Inclusion in Artificial Intelligence, such as racial bias in Facial Recognition Technology, Video Surveillance, Predictive Analytics and Children's Rights. Beyond the Lab she has written several children's books including "Little Miss Minnesota", "Little A.I. and Peety" and the soon to be released book "I'm Beautiful".


How the Police Use AI to Track and Identify You

#artificialintelligence

Surveillance is becoming an increasingly controversial application given the rapid pace at which AI systems are being developed and deployed worldwide. While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them. With just hours to sift through thousands of CCTV camera feeds and other dragnet data streams, the police turned to a range of automated systems for help, reaching for information collected by automated license plate readers, CCTV-video analysis software, open-source geolocation tools, and Clearview AI's controversial facial recognition system. High above the city, an unarmed Predator drone flew in circles, outfitted with a specialized camera first pioneered by the police in Baltimore that is capable of identifying individuals from 10,000 feet in the air, providing real-time surveillance of protestors across the city. But Minneapolis is not an isolated case of excessive policing and technology run amok. Instead, it is part of a larger strategy by the state, local, and federal government to build surveillance dragnets that pull in people's emails, texts, bank records, and smartphone location as well as their faces, movements, and physical whereabouts to equip law enforcement with unprecedented tools to search for and identify Americans without a warrant.


University of Minnesota develops AI algorithm to analyze chest X-rays for COVID-19

#artificialintelligence

A team of researchers at the University of Minnesota recently developed and validated an artificial intelligence algorithm that can evaluate chest X-rays to diagnose possible cases of COVID-19. Working together with M Health Fairview and Epic, the algorithm will be available at no cost to other health systems through Epic, the medical records software used by many health care organizations across the country. Today, all 12 M Health Fairview hospitals use the new algorithm. When a patient arrives in the emergency department with suspected COVID-19 symptoms, clinicians order a chest X-ray as part of standard protocol. The algorithm automatically evaluates the X-ray as soon as the image is taken.


New artificial intelligence models show potential for predicting outcomes – IAM Network

#artificialintelligence

New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP) to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


New artificial intelligence models show potential for predicting outcomes

#artificialintelligence

New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP) to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


New artificial intelligence models show potential for predicting outcomes

#artificialintelligence

CHICAGO: New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP)1 to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable2 representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.


Researchers use artificial intelligence to detect COVID-19 in hospital patients

#artificialintelligence

Researchers are using artificial intelligence to detect COVID-19 from chest x-rays of hospital patients faster than traditional tests. MINNEAPOLIS (FOX 9) - Researchers at the University of Minnesota and M Health Fairview are developing a new technology that uses artificial intelligence to help doctors detect COVID-19 in hospital patients. "It does feel maybe science fiction-y, but this is probably going to be a part of our new normal," said Dr. Genevieve Melton-Meaux of M Health Fairview. Researchers at the University of Minnesota and M Health Fairview are developing a new technology that uses artificial intelligence to help doctors detect COVID-19 in hospital patients. Dr. Melton-Meaux explains artificial intelligence (AI) is having some major impacts on health care in 2020.


Sizing up a green carbon sink

Science

Forests are having their moment. Because trees can vacuum carbon from the atmosphere and lock it away in wood, governments and businesses are embracing efforts to fight climate change by reforesting cleared areas and planting trees on a massive scale. But scientists have warned that the enthusiasm and money flowing to forest-based climate solutions threaten to outpace the science. Two papers published this week seek to put such efforts on a firmer footing. One study quantifies how much carbon might be absorbed globally by allowing forests cleared for farming or other purposes to regrow. The other calculates how much carbon could be sequestered by forests in the United States if they were fully “stocked” with newly planted trees. Each strategy has promise, the studies suggest, but also faces perils. To get a worldwide perspective on the potential of second-growth forests, an international team led by ecologist Susan Cook-Patton of the Nature Conservancy (TNC) assembled data from more than 13,000 previously deforested sites where researchers had measured regrowth rates of young trees. The team then trained a machine-learning algorithm on those data and dozens of variables, such as climate and soil type, to predict and map how fast trees could grow on other cleared sites where it didn't have data. > Can the forest regenerate naturally, or can we do something to help? > > Susan Cook-Patton , the Nature Conservancy A TNC-led team had previously calculated that some 678 million hectares, an area nearly the size of Australia, could support second-growth forests. (The total doesn't include land where trees might not be desirable, such as farmland and ecologically valuable grasslands.) If trees were allowed to take over that entire area, new forests could soak up one-quarter of the world's fossil fuel emissions over the next 30 years, Cook-Patton and colleagues report in Nature . That absorption rate is 32% higher than a previous estimate, based on coarser data, produced by the Intergovernmental Panel on Climate Change. But the total carbon drawdown is 11% lower than a TNC-led team estimated in 2017. The study highlights “what nature can do all on its own,” Cook-Patton says. And it represents “a lightning step forward” in precision compared with earlier studies, says geographer Matthew Fagan of the University of Maryland, Baltimore County, who was not involved in the work. But, Fagan adds, “Natural regrowth is not going to save the planet.” One problem: There is often little economic incentive for private landowners to allow forests to bounce back. Under current policies and market pricing, “nobody will abandon cattle ranching or agriculture for growing carbon,” says Pedro Brancalion, a forest expert at the University of São Paulo in Piracicaba, Brazil. And even when forests get a second life, they often don't last long enough to store much carbon before being cleared again. Fagan notes that even in Costa Rica, renowned as a reforestation champion for doubling its forest cover in recent decades, studies have found that half of second-growth forests fall within 20 years. Given such realities, some advocates are pushing to expand tree planting in existing forests. To boost that concept, a team of researchers at the U.S. Forest Service (USFS) quantified how many additional trees U.S. forests could hold. Drawing on a federal inventory, they found that more than 16% of forests in the continental United States are “understocked”—holding fewer than 35% of the trees they could support. Fully stocking these 33 million hectares of forest would ultimately enable U.S. forests to sequester about 18% of national carbon emissions each year, up from 15% today, the team reports in the Proceedings of the National Academy of Sciences . But for that to happen, the United States would have to “massively” expand its annual tree-planting efforts, from about 1 billion to 16 billion trees, says lead author Grant Domke, a USFS research forester in St. Paul, Minnesota. Cook-Patton says planting trees might make sense in some places, but natural regeneration, where possible, provides more bang for the buck. “For any given site,” she says, “we should always ask ourselves first: ‘Can the forest regenerate naturally, or can we do something to help?’”


Physicist: The entire universe might be a neural network

#artificialintelligence

It's not every day that we come across a paper that attempts to redefine reality. But in a provocative preprint uploaded to arXiv this summer, a physics professor at the University of Minnesota Duluth named Vitaly Vanchurin attempts to reframe reality in a particularly eye-opening way -- suggesting that we're living inside a massive neural network that governs everything around us. In other words, he wrote in the paper, it's a "possibility that the entire universe on its most fundamental level is a neural network." For years, physicists have attempted to reconcile quantum mechanics and general relativity. The first posits that time is universal and absolute, while the latter argues that time is relative, linked to the fabric of space-time.