WASHINGTON, DC (March 8, 2017)--Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting. The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment. "We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."
A radically revised ATC (Air Traffic Control) interface, developed from that described at HCI-Aero98, has been evaluated by trials using 26 postgraduate students (native French speakers). Twenty-four out of 26 students were able to control traffic at a nominal rate of 200 aircraft per hour after about one hour of training. All but two of 1620 potential conflicts were resolved correctly. All but five of 10468 aircraft left the simulated area at exactly the time position and height planned. The interface is briefly discussed, and the underlying assumptions and implications are suggested.
"130 years ago, we replaced the horse. Now it's the driver's turn" - an excerpt from an advertisement of a German automobile manufacturer designed to draw attention to the progress in driverless car technologies. A form of technological advancement in which the new technology is expected to be more intelligent and reliable than the current set-up, where the thinking and steering has been done by a human.
New York City's subway system really is complicated. And it may have nothing to do with the number of lines, but rather the subway map's confusing, color-coded design, according to researchers. A new study from scientists at the University of Kent and the University of Essex looked at the color-coding of New York City's subway map to determine how that impacts its usability. They discovered that color-coding plays a role, in addition to a number of irritating'navigational hazards'. A new study from scientists at the University of Kent and the University of Essex looked at the color-coding of New York City's subway map to determine how that impacts its usability.
Considerable research has been conducted to identify a useful set of Air Traffic Control complexity factors. It is now necessary to determine, on the one hand, how these factors affect ATC complexity and controller workload and, on the other hand, how ATC complexity and controller workload interact. This line of research should lead to elaboration of guidelines to improve sector configuration and traffic flow as well as to produce automation tools and procedures to reduce controller workload This paper addresses the problem of formulating a functional relationship between ATC complexity and workload using a parameter reflecting intrinsic air traffic complexity -a measure of disorder of aircraft trajectories assumed to estimate cognitive difficulty-, a computed index, the Traffic Load Index, and psychophysiological parameters to characterize workload. Preliminary results concerning workload assessment are reported.