Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Researchers are proposing using artificial intelligence technology to help diagnose autism spectrum disorder. In a recent article published in Scientific Reports, researchers from Brazil, France and Germany reportedly used magnetic resonance imaging to train a machine learning algorithm. The work – in which the "quantitative diagnostic method" is proposed – was based on brain imaging data for 500 people, with more than 240 that had been diagnosed with autism. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

"With the successful execution of our model, we predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender and age." said Zeeshan Ahmed, a core faculty member at the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH) and lead author of the study, published in Genomics. According to the World Health Organization, cardiovascular disease is the leading cause of death globally, yet it is estimated that more than 75 percent of premature cardiovascular disease is preventable. Atrial fibrillation and heart failure contribute to about 45 percent of all cardiovascular disease deaths. Despite significant advancements in cardiovascular disease diagnostics, prevention and treatment, about half of the affected patients reportedly die within five years of receiving a diagnosis because of a variety of reasons. Researchers said the use of AI and machine learning can accelerate our ability to identify genes that have important implications for cardiovascular disease, which can lead to improvements in diagnoses and treatment.

For an autonomous car to drive safely, being able to predict the behavior of other road users is essential. A research team at the Massachusetts Institute of Technology's CSAIL, along with researchers at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in Beijing, have developed a new ML system that could one day help driverless cars predict in real time the upcoming movements of nearby drivers, cyclists and pedestrians. They titled their study, " M2I: From Factored Marginal Path Prediction to Interactive Prediction." Qiao Sun, Junru Gu, Hang Zhao are the IIIS members who participated in this study while Xin Huang and Brian Williams represented MIT. Humans are unpredictable, which makes predicting road user behavior in urban environments de facto very difficult.

Identifying possible hotspots of crime in a city is an important issue for urban safety development and can help the authorities take necessary steps to make the city safer for its residents. The effectiveness of such preventive measures depends on the accuracy of the predictions, which are increasingly being made by artificial intelligence (AI)-based models. Most existing models use subjective perceptions of safe locations, socioeconomic status, and still images of crime scenes, and only a few violent crimes are categorized as input data. As a result, there is often a discrepancy between their predictions and reality. In a new study published in AAAI Conference on Artificial Intelligence, researchers from the Gwangju Institute of Science and Technology (GIST) in South Korea proposed a different strategy based on a large-scale dataset and the concept of "deviance," which included not only violent crimes but also civil complaints regarding behaviors violating social norms, which is also called "deviant behavior."

Editor’s note: This article was originally published in the Jan. 25, 2022 flipbook. A UT researcher, in collaboration with other schools and the Department of Energy, is using artificial intelligence to develop better strategies for flood predictions and preparedness. As part of the collaboration, University researcher Clint Dawson utilizes artificial intelligence and modeling to analyze...

Researchers have demonstrated that artificial intelligence can lead to better predictions of the onset and course of cardiovascular diseases, allowing physicians to prevent, treat or foresee serious heart problems even before the patient becomes aware of any underlying conditions. Although the focus of the study was for cardiovascular diseases, according to the researchers, the findings could pave the way for a new era of personalised, preventive medicine, allowing doctors to proactively alert patients on potential ailments and suggest treatments to alleviate the problems, before the onset of serious conditions. Martin Tristani-Firouzi, corresponding author of the study says, "We can turn to AI to help refine the risk for virtually every medical diagnosis. The risk of cancer, the risk of thyroid surgery, the risk of diabetes -- any medical term you can imagine." The current approaches for calculating the combined effects of various risk factors, including demographics and medical history on cardiovascular diseases are imperfect and subjective, with the methods failing to identify certain interactions that can have a profound effect on the health of the heart and the blood vessels.

"We know that flooding has been getting worse," said study lead author Frances Davenport, a PhD student in Earth system science in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth). "Our goal was to understand why extreme precipitation is increasing, which in turn could lead to better predictions about future flooding." Among other impacts, global warming is expected to drive heavier rain and snowfall by creating a warmer atmosphere that can hold more moisture. Scientists hypothesize that climate change may affect precipitation in other ways, too, such as changing when and where storms occur. Revealing these impacts has remained difficult, however, in part because global climate models do not necessarily have the spatial resolution to model these regional extreme events.

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.

The US power grid system is not only large but dynamic, which makes it especially challenging to manage. Human operators know how to maintain systems when conditions are static. But when conditions change quickly operators lack a clear way of anticipating how the system should best adapt to meet system security and safety requirements. At the US Department of Energy's (DOE) Argonne National Laboratory a research team has developed a novel approach to help system operators understand how to better control power systems with the help of artificial intelligence. Their new approach could help operators control power systems in a more effective way, which could enhance the resilience of America's power grid, according to a recent article in IEEE Transactions on Power Systems.

The Elephant Listening Project has been listening to elephant calls for 20 years to learn more about animals. But identifying the calls used to be laborious -- until scientists used AI.