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The American College of Radiology Data Science InstituteTM (ACR DSI) is a co-sponsor for the National Institute of Biomedical Imaging and Bioengineering (NIBIB)’s Workshop on Artificial Intelligence (AI) in Medical Imaging. The two-day workshop aims to clarify the needs in foundational and translational research for machine learning in medical imaging.

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A new initiative kicks off today to evaluate the use of artificial intelligence (AI) and situational awareness technologies during critical incidents. The effort is a joint partnership between DHS S&T and Canada’s Department of National Defence.

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The American College of Radiology Data Science Institute (ACR DSI) began releasing its first use cases in the TOUCH-AI library to industry for comment this month, to generate feedback prior to the projected release of the use cases in the fall of 2018.

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A research team with funding from the National Institute of Biomedical Imaging and Bioengineering has developed an advanced computing technique for rapidly and cost effectively improving the quality of biomedical imaging. The technology, called AUTOMAP finds the best computational strategies to produce clear, accurate images for various types of medical scans.

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Want to be a good team player? Take a break. It may improve not only your own performance but the chances of your team winning overall, says a new study by a team of USC computer scientists.

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Researchers from the Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, Maryland, and collaborators at the Johns Hopkins School of Medicine, have developed image analysis and machine learning tools to detect age-related macular degeneration, and report in Nature Medicine that such tools can be applied to other image-based medical diagnoses.

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The US Department of Energy’s Oak Ridge National Laboratory is once again officially home to the fastest supercomputer in the world, according to the TOP500 List, a semiannual ranking of the world’s fastest computing systems.

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Four scientists at the Department of Energy’s SLAC National Accelerator Laboratory will receive Early Career Research Program awards for research that’s developing new ways to study fundamental particles with machine learning and study nanoscale objects and quantum materials with powerful X-ray laser beams.

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Scientists at Pacific Northwest National Laboratory are exploring deep learning to interpret data related to national security, the environment, the cosmos, and breast cancer. In one project a deep neural network is interpreting data about nuclear events as well as – sometimes better than – today’s best automated methods or human experts.

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In an effort to reduce errors in the analyses of diagnostic images by health professionals, a team of researchers from Oak Ridge National Laboratory has improved understanding of the cognitive processes involved in image interpretation, work that has enormous potential to improve health outcomes for the hundreds of thousands of American women affected by breast cancer each year. The ORNL-led team found that analyses of mammograms by radiologists were significantly influenced by context bias, or the radiologist’s previous diagnostic experiences.

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Researchers at Berkeley Lab are currently developing a web-based search engine for scientific data, called Science Search. The team is also building innovative machine learning tools to pull contextual information from scientific datasets and automatically generate missing metadata tags for each raw and simulated data files. As a proof-of-concept, the team is working with staff at the Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments.

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The concept of non-human beings endowed with intelligence dates back to at least Homer in the late eighth or early seventh century B.C. As society has developed and our ability to tell stories enhanced by technology, the idea of intelligent machines has captured the minds of societies across the globe.

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A team of ASU students built an AI drone to detect wildfires before they become catastrophic. The students will compete for a $100,000 prize in an international Microsoft pitch competition this summer.

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a team led by researchers at UC San Diego’s Department of Chemistry and Biochemistry and the San Diego Supercomputer Center (SDSC), has used machine learning techniques to develop models for simulations of water with “unprecedented accuracy.”

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RNA sequencing and machine learning applied to develop new asthma biomarker

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Scientists have developed an algorithm that predicts potentially dangerous low blood pressure, or hypotension, that can occur during surgery. The algorithm identifies hypotension 15 minutes before it occurs in 84 percent of cases, the researchers report in a new study published in the Online First edition of Anesthesiology.

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The U.S. Department of Energy’s Oak Ridge National Laboratory today unveiled Summit as the world’s most powerful and smartest scientific supercomputer.

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Machine learning using real-time symptom reports can accurately detect lymphedema, a distressing side effect of breast cancer treatment that is more easily treated when identified early, finds a new study led by NYU Rory Meyers College of Nursing and published in the journal mHealth.

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It takes an “A” team to make headway against glioblastoma, a highly aggressive type of brain cancer. Glioblastoma is the most common type of malignant brain tumor in adults. In addition to the caliber of the researchers involved, in this case “A” also stands for atlas. A key member of the team, Jill S. Barnholtz-Sloan, PhD, Sally S. Morley Designated Professor in Brain Tumor Research at Case Western Reserve University School of Medicine, and approximately 80 other internationally renowned neurologists, bioinformaticians, and pathologists from the United States and India recently published details of the Ivy Glioblastoma Atlas in Science.

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Premenopausal women with hormone receptor-positive, HER2-negative breast cancer and a high risk of recurrence who are treated with an aromatase inhibitor plus ovarian function suppression may gain 10 to 15 percent improvement in freedom from distant recurrence at eight years, according to a new clinical trial analysis reported at the annual meeting of the American Society of Clinical Oncology.

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The new MTSU Data Science Institute officially launched in mid-May with a mission to promote funded interdisciplinary research and develop public and private collaborations around the emerging field of “big data.”

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Artificial Intelligence (AI) will be deployed against Mount Sinai’s massive patient data warehouse biorepository and to innovate more accurate disease detection and management for introduction in 2019

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The race is on for companies to present their driverless cars to the public, but recent collisions involving autonomous vehicles developed by Uber Technologies Inc. and Tesla Inc. have led consumers to questions whether these vehicles can alleviate traffic issues and increase safety. A new study published in Risk Analysis examined the question “How safe is safe enough for self-driving vehicles (SDVs)?”

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In an effort to build the next generation of machine-learning methods to support its needs, the Air Force Office of Scientific Research and the Air Force Research Laboratory have awarded $5 million to establish a university center of excellence devoted to efficient and robust machine learning at the University of Wisconsin–Madison.

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ISPOR, the professional society for health economics and outcomes research, held a session, “Is Artificial Intelligence the Next Big Thing in Healthcare Decision Making?”, at ISPOR 2018 in Baltimore, MD, USA.

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Cornell University has teamed with the University of Bologna to establish the Cornell-Bologna Center for Vehicle Intelligence, a partnership that merges world-class research with some of the world’s most powerful and elegant automobiles.

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Join physicist Roger Melko for a live webcast May 2 as he explores the application of machine learning and artificial intelligence to questions in fundamental physics.

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A team of physicists has uncovered properties of a category of magnetic waves relevant to the development of neuromorphic computing—an artificial intelligence system that seeks to mimic human-brain function.

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On May 30, 2018 the American College of Radiology (ACR) Data Science Institute (DSI) and the Society for Imaging Informatics in Medicine (SIIM) will hold the Spring 2018 Data Science Summit: Economics of Artificial Intelligence (AI) in Health Care at the SIIM 2018 Annual Meeting.

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The American College of Radiology (ACR) and the Medical Image Computing and Computer Assistance Intervention (MICCAI) Society recently announced that they are working together to develop artificial intelligence (AI) algorithms to better meet the clinical needs of radiologists.

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USC’s Center for Artificial Intelligence in Society’s is developing a comprehensive algorithm that provides health practitioners the tool to form real-life peer support groups based on demographic, social and health-related data self-volunteered by patients.

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A major issue that limits modeling to predict cardiac arrhythmia is that it is impossible to measure and monitor all the variables that make our hearts tick, but researchers have now developed an algorithm that uses artificial intelligence to model the electrical excitations in heart muscle. Their work, appearing in Chaos, draws on partial differential equations describing excitable media and echo state networks to cross-predict variables about chaotic electrical wave propagations in cardiac tissue.

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The Center for Financial Studies in the Lally School of Management at Rensselaer Polytechnic Institute will host a one-day workshop titled Artificial Intelligence and Machine Learning in Financial Services. The workshop will take place on April 27 from 8 a.m. to 5 p.m. in the Center for Biotechnology and Interdisciplinary Studies Auditorium on campus.

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FSU wins $1.5 million grant for suicide research

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Machine learning algorithms excel at finding complex patterns within big data, so researchers often use them to make predictions. Researchers are pushing the technology beyond finding correlations to help uncover hidden cause-effect relationships and drive scientific discoveries. At the University of South Florida, researchers are integrating machine learning techniques into their work studying proteins. As they report in The Journal of Chemical Physics, one of their main challenges has been a lack of methods to identify cause-effect relationships in data obtained from molecular dynamics simulations.

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SLAC and its collaborators are transforming the way new materials are discovered. In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.

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Where machine learning meets spring planting and big data intersects with farming big and small, two Washington University in St. Louis researchers at Olin Business School have devised a computational model so farmers and seedmakers could take the guesswork out of which particular variety of, say, soybean to plant each year.

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Given the choice of riding in an Uber driven by a human or a self-driving version, which would you choose? Following last month’s fatal crash of a self-driving Uber that took the life of a woman in Tempe, Arizona, and the recent death of a test-driver of a semi-autonomous vehicle being developed by Tesla, peoples’ trust in the technology behind autonomous vehicles may also have taken a hit.

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Matching unique genetic information from cancer patients’ tumors with treatment options – an emerging area of precision medicine efforts – often fails to identify all patients who may respond to certain therapies. Other molecular information from patients may reveal these so-called “hidden responders."

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A team of California researchers has developed a robotic gripper that combines the adhesive properties of gecko toes and the adaptability of air-powered soft robots to grasp a much wider variety of objects than the state of the art. Researchers will present their findings at the 2018 International Conference on Robotics and Automation May 21 to 25 in Brisbane, Australia.

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An algorithm can predict which passengers survived the sinking of the Titanic in April 1912 and can do so with 97 percent accuracy—a result that both demonstrates the power of artificial intelligence and, more subtly, points to its shortcomings. AI may get things right, this finding shows, but for all the wrong reasons.

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Trust among surgical teams can be measured using EEG activity, according to new Roswell Park Comprehensive Cancer Center research published in Scientific Reports. The research team used brain activity patterns to objectively assess the level of trust between mentor and trainee during robot-assisted surgery.

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Our faces broadcast our feelings in living color—even when we don’t move a muscle. That’s the conclusion of a groundbreaking study into human expressions of emotion, which found that people are able to correctly identify other people’s feelings up to 75 percent of the time—based solely on subtle shifts in blood flow color around the nose, eyebrows, cheeks or chin.

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Opioid addicts and others battling compulsion around drugs or alcohol are using a new high-tech, low-risk method to practice saying no—through virtual reality.

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How will the farms of the future feed a projected 9.8 billion people by 2050? Berkeley Lab’s “smart farm” project marries microbiology and machine learning in an effort to reduce the need for chemical fertilizers and enhance soil carbon uptake, thus improving the long-term viability of the land while increasing crop yields.

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Researchers at DOE’s Oak Ridge National Laboratory (ORNL) have developed a novel method for more efficiently training large numbers of networks capable of solving complex science problems. Specifically, Mohammed Alawad, Hong-Jun Yoon, and Gina Tourassi of ORNL’s Computer Science and Engineering Division, have demonstrated that by converting deep learning neural networks (DNNs) to “deep spiking” neural networks (DSNNs) they can improve the efficiency of network design and training.

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Mayo Clinic e IBM Watson Health revelaron los resultados de la aplicación inicial de la Combinación Watson para Ensayos Clínicos, un sistema cognitivo de computación de IBM. La aplicación de este sistema en la práctica oncológica de Mayo Clinic se relacionó con mayor cantidad de pacientes inscritos en ensayos clínicos sobre cáncer.