Hospital patient falls remain a critical and costly challenge worldwide. While conventional fall prevention systems typically rely on post-fall detection or reactive alerts, they also often suffer from high false positive rates and fail to address the underlying patient needs that lead to bed-exit attempts. This paper presents a novel system architecture that leverages the Internet of Robotic Things (IoRT) to orchestrate human-robot-robot interaction for proactive and personalized patient assistance. The system integrates a privacy-preserving thermal sensing model capable of real-time bed-exit prediction, with two coordinated robotic agents that respond dynamically based on predicted intent and patient input. This orchestrated response could not only reduce fall risk but also attend to the patient's underlying motivations for movement, such as thirst, discomfort, or the need for assistance, before a hazardous situation arises. Our contributions with this pilot study are three-fold: (1) a modular IoRT-based framework enabling distributed sensing, prediction, and multi-robot coordination; (2) a demonstration of low-resolution thermal sensing for accurate, privacy-preserving preemptive bed-exit detection; and (3) results from a user study and systematic error analysis that inform the design of situationally aware, multi-agent interactions in hospital settings. The findings highlight how interactive and connected robotic systems can move beyond passive monitoring to deliver timely, meaningful assistance, empowering safer, more responsive care environments.

Viz.ai announced today that it partnered with Hyperfine (Nasdaq:HYPR) to bring magnetic resonance imaging (MRI) to the patient's bedside. San Francisco-based Viz.ai develops the Viz LVO, its flagship product designed to leverage advanced deep learning to communicate time-sensitive information about stroke patients straight to a specialist who can intervene and treat the patient. Hyperfine develops Swoop, which it said is the world's first MR imaging system capable of providing neuroimaging at the point of care, expanding patient access to MRI by bringing imaging to the bedside. Swoop offers a way for physicians to make quick and informed clinical decisions for patients, eliminating hours of wait and transport time, reducing the potential for adverse events associated with transporting critically ill patients. According to a news release, the partnership and subsequent implementation into neuro ICUs provide the potential to further decrease the time from admission to treatment by expanding clinician access to MRI and increasing access to time-critical diagnostics in multiple phases of care.

It's quite a Canadian success story – (Ottawa) has decided that this is something we need to invest in,

Perhaps many people are like me in that hearing the word "machine learning" for the first time brings forth images of Skynet from The Terminator movies or Haley Joel Osment's character from the Steven Spielberg's film A.I. Artificial Intelligence. However, machine learning has now become a regular part of our vernacular when it comes to predictive modeling in many conditions. Ramgopal et al use machine learning methods to derive and validate a new prediction model for risk stratification of febrile infants 60 days of age. Using various machine learning approaches, the authors developed a prediction model with high sensitivity and specificity compared with recent prediction models for febrile infants. So, are machine learning models the new paradigm for risk stratification of febrile infants? The results are intriguing, particularly the high specificity of the model, but further work must be done, as explained nicely by Chamberlain et al in an accompanying commentary (10.1542/peds.2020-012203).

Much like other aspects of health care, nursing has become increasingly saturated with technology over the past several decades. Existing technolog...

Most experiments with artificial intelligence in medicine thus far have worked on the diagnostic side. AI systems have used computer vision to examine images like X-rays or pathology slides, and they have combed through data in electronic medical records to spot subtle patterns that humans can miss. Just last week, IEEE Spectrum reported on hospitals that are trying out AI systems that identify patients with the first signs of sepsis, a life-threatening condition where the body responds to infection with widespread inflammation, which can lead to organ failure. Sepsis is the third leading cause of death worldwide, and the primary cause of death in hospitals. But the technology that goes by the name AI Clinician, described today in a paper in Nature Medicine, doesn't diagnose--it makes decisions.

Dan Olley was recently named to the prestigious CIO Hall of Fame by CIO Magazine. In many ways, however, Olley has not been a traditional chief information officer. For one, he has largely held chief technology officer roles. Moreover, he has also had customer-facing, product-centric roles. In his current role as Chief Technology Officer and Executive Vice President of Product Development of Elsevier, his purview is quite broad.

We have taken a look at some of the challenges to overcome and predictions for its implementation from experts in the field who envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh actionable insights. A deep learning system is, in short, a multi-layered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep. For example, when processing thousands of images of human faces, it recognises objects based on a hierarchy of simpler building blocks: straight lines and curved lines at the basic level; then eyes, mouths, and noses; entire faces; and finally, specific facial features. Besides image recognition, deep learning offers the potential to approach complex challenges such as speech comprehension, human-machine conversation, language translation, and vehicle navigation, amongst others. How can we expect this technology to be implemented in the coming year?