Researchers at the University of Leeds and collaborators from the University of California San Diego won the Best Paper Award at RoboSoft, the leading international conference focused on soft robotics research. Soft robotics is gaining attention in medical applications because compliant machines can interact more safely with delicate objects and complex anatomy. The award-winning paper describes a 1.8 mm soft growing robot that can be steered magnetically, sense its own shape in real time, and operate without internal pressure. These advances could help improve patient outcomes following minimally invasive procedures. We spoke with lead author Benjamin Calmé about the team's work.

Plus: Space data centers don't exist yet, but people already oppose them. Supercooled kidneys have been transplanted into pigs in a "landmark achievement" When it comes to organ donation, time is everything. As soon as an organ has been removed from a donor's body, it starts to deteriorate. Surgeons have only a matter of hours to get it into a recipient. In most cases, organs will be kept on ice during that time, at around 4 C (39 F). They cannot be frozen--in previous attempts, ice has formed, causing all kinds of damage.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Wealthy New York doctor allegedly promised stripper lover he'd leave his wife in these smitten texts... then called cops when iconic movie poster sent shivers down his spine Eric Swalwell's past romance with Stephen Miller's now-WIFE revealed amid reported'Fang Fang' drama America's young dementia epidemic: The 14 lifestyle habits driving early-onset cases revealed by world-leading experts... and changes that can REVERSE your risk Rising star Democrat Abdul El-Sayed blasted for saying he LOATHES paradise Midwest island labeled the'most American place in Michigan' Keith Urban's secret'RELIEF' about Nicole Kidman's new lover: Friends say she may finally let go of'hold over him' as daughters meet beau... and brutal question that sparked ugly split Haunting footage shows girl, 16, sneak out door of family's home in dead of night before vanishing without a trace Six-foot-10 former Knicks ...

San Francisco's brightest minds are stuffing themselves into hacker houses. The living room of the Accler8 hacker house in San Francisco, where the author stayed for a week. O n a Friday in April, I hopped into an Uber to a fish market in San Francisco with a couple of tech founders on a mission to buy lobsters. Not for dinner, but for science: The duo dreamed of one day altering human consciousness, but they would start by toying around with some crustaceans. They intended to perform neurosurgery on the lobsters in the hopes of controlling them with an AI bot. Leading the way was Elliot Roth, a bearded 32-year-old wearing a black T-shirt with Longevity printed across the chest and a silver chain with a double-helix pendant. To push the boundaries of the five senses, Roth has implanted a magnet in his left ring finger.

Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative across clients while disentangling clientspecific factors without supervision. We thus introduce FORLA, a novel framework for federated object-centric representation learning and feature adaptation using unsupervised slot attention. At the core of our method is a shared feature adapter, trained collaboratively across clients to adapt features from foundation models, and a shared slot attention module that learns to reconstruct the adapted features.

Large language models (LLMs) have achieved significant performance progress in various natural language processing applications. However, LLMs still struggle to meet the strict requirements for accuracy and reliability in the medical field and face many challenges in clinical applications. Existing clinical diagnostic evaluation benchmarks for evaluating medical agents powered by LLMs have severe limitations. Firstly, most existing medical evaluation benchmarks face the risk of data leakage or contamination.

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and thirdperson perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery, EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR's multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.

Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic ultrasound can further enhance its utility by reducing operator dependence and improving access to complex anatomical regions. For this, while deep reinforcement learning (DRL) and imitation learning (IL) have shown potential for autonomous navigation, their use in complex surgical tasks such as anatomy reconstruction and surgical guidance remains limited -- largely due to the lack of realistic and efficient simulation environments tailored to these tasks. We introduce SonoGym, a scalable simulation platform for complex robotic ultrasound tasks that enables parallel simulation across tens to hundreds of environments. Our framework supports realistic and real-time simulation of US data from CT-derived 3D models of the anatomy through both a physics-based and a generative modeling approach.

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and third-person perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery, EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR's multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.

'You can't control everything': the rise in plastic surgeons asked to create'AI face' Growing numbers of people are seeking improbable cosmetic surgery based on chatbots' recommendations Plastic surgeons are increasingly concerned about the rise of "AI face", as more and more clients arrive in their offices with unrealistic AI-generated visions of what they want to look like. Dr Nora Nugent, a cosmetic surgeon from Tunbridge Wells, has seen this first hand. Clients have started coming to her office with photos of themselves beautified by AI and a false expectation that those results are achievable with surgery. She is also the president of the British Association of Aesthetic Plastic Surgeons, and says many colleagues are having similar experiences. "I can only predict an increase, given the rate AI has been incorporated into every aspect of life," she said.