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.

Chronic kidney disease (CKD) is caused by diabetes and hypertension. In 2017, the global prevalence of CKD was 9.1 percent, which is approximately 700 million cases. Chronic kidney damage is assessed by scoring the amount of interstitial fibrosis and tubular atrophy (IFTA) in a renal biopsy sample. Although image digitization and morphometric (measuring external shapes and dimensions) techniques can better quantify the extent of histologic damage, a more widely applicable way to stratify kidney disease severity is needed. Now, researchers from Boston University School of Medicine (BUSM) have developed a novel Artificial Intelligence (AI) tool to predict the grade of IFTA, a known structural correlate of progressive and chronic kidney disease.

Leicester [England], March 26 (ANI): International genomics research led by the University of Leicester has used artificial intelligence (AI) to study an aggressive form of cancer, which could improve patient outcomes. Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months. New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.

To understand the "clicks" sperm whales make to talk (stringing together a series of statements called "codas"), researchers have launched a five-year-long quest called Protect CETI. It'll be the most comprehensive attempt at interspecies communication ever, the team believes. CETI (Cetacean Translation Initiative) begins with the capturing and cataloging of millions of morse-code-like whale vocalizations. Video and audio tools will be used, and the data will be fed to an AI that uses natural language processing (the tech that brought us Siri and Alexa) to decode it. Cues from captured video will provide context to the conversations and, if all works according to plan, will bring researchers closer to a breakthrough than ever before.