Parents in north London have been told their children could be excluded from school if they are not fully vaccinated against measles amid an outbreak of the highly-contagious disease. Unvaccinated pupils identified as close contacts of people with measles could be excluded for 21 days in accordance with national guidelines, Enfield Council said in a letter to all parents in the borough in late January. At least 34 children have contracted measles in Enfield so far this year, the UK Health Security Agency (UKHSA) has said, and a number sent to hospital. A local health chief meanwhile told the BBC: We are worried because actually, this is a significantly increased number than what we're used to. Asking unvaccinated, close contacts of measles cases to stay off school is fairly standard practice when there are local outbreaks.

How The Pitt's AI Drama is Playing Out in Real Hospitals In Thursday's episode of The Pitt, the long-simmering tensions over the use of AI at the Pittsburgh Trauma Medical Center boiled over. In season two of the five-time Emmy winning medical drama, a new attending physician, Baran Al-Hashimi (Sepideh Moafi), is determined to improve efficiencies at the hospital. She tells her skeptical staff that new AI systems can cut down their time spent on charting by 80%, allowing them to spend more time both at the bedside and at home. But in episode six, doctors discover that the AI tool has made up false details about a patient and confused "urology" for "neurology." "AI's two percent error rate is still better than dictation," Al-Hashimi says, adding that it needs to be proofread for errors.

A baby with a rare neurological disorder, airlifted to hospital after collapsing in a supermarket, is not out of the woods yet, said his father. Seven-month-old Rupert Smith, from Broughton, Flintshire, stopped breathing in a Tesco store in Broughton Park, on Monday. His mother Siobhan, 35, immediately called for help and administered CPR before emergency services, including paramedics, police and an air ambulance arrived. Rupert, who has a disorder called alternating hemiplegia of childhood (AHC), was flown to Alder Hey Children's Hospital in Liverpool for treatment. Dad Dave Smith said Rupert had continued to have quite significant seizures [in hospital] so they have been giving him medication and he has undergone various different tests.

An Immigration and Customs Enforcement (ICE) officer has shot a man in the leg in the US city of Minneapolis, where an ICE agent shot dead a woman last week. In a statement, the Department of Homeland Security (DHS) said federal officers initially pursued the man in a car chase because he was illegally in the US from Venezuela. The City of Minneapolis confirmed a man was shot and taken to hospital for non-life threatening injuries. An ICE officer was also taken to hospital to be treated for injuries, the DHS said. Minneapolis city officials said on X: We understand there is anger.

Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware con-formal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves target coverage (94.3% vs 95% target) with adaptive precision: 21% narrower intervals for low-uncertainty cases while appropriately widening for high-risk predictions. Critically, we demonstrate that well-calibrated Bayesian uncertainties alone severely under-cover (14.1%), highlighting the necessity of our hybrid approach. This framework enables risk-stratified clinical protocols, efficient resource planning for high-confidence predictions, and conservative allocation with enhanced oversight for uncertain cases, providing uncertainty-aware decision support across diverse healthcare settings.

How can one publish a dataset with sensitive attributes in a way that both preserves privacy and enables joins with other datasets on those same sensitive attributes? This problem arises in many contexts, e.g., a hospital and an airline may want to jointly determine whether people who take long-haul flights are more likely to catch respiratory infections. If they join their data by a common keyed user identifier such as email address, they can determine the answer, though it breaks privacy. This paper shows how the hospital can generate a private sketch and how the airline can privately join with the hospital's sketch by email address. The proposed solution satisfies pure differential privacy and gives approximate answers to linear queries and optimization problems over those joins. Whereas prior work such as secure function evaluation requires sender/receiver interaction, a distinguishing characteristic of the proposed approach is that it is non-interactive. Consequently, the sketch can be published to a repository for any organization to join with, facilitating data discovery. The accuracy of the method is demonstrated through both theoretical analysis and extensive empirical evidence.

Multiple Instance Learning (MIL) is a crucial weakly supervised learning method applied across various domains, e.g., medical diagnosis based on whole slide images (WSIs). Recent advancements in MIL algorithms have yielded exceptional performance when the training and test data originate from the same domain, such as WSIs obtained from the same hospital. However, this paper reveals a performance deterioration of MIL models when tested on an out-of-domain test set, exemplified by WSIs sourced from a novel hospital. To address this challenge, this paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which integrates Optimal Transport (OT) as the distance metric for nearest neighbor retrieval. The development of RAM-MIL is driven by two key insights. First, a theoretical discovery indicates that reducing the input's intrinsic dimension can minimize the approximation error in attention-based MIL. Second, previous studies highlight a link between input intrinsic dimension and the feature merging process with the retrieved data. Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. Furthermore, the use of the transportation matrix derived from OT renders the retrieval results interpretable at the instance level, in contrast to the vanilla $l_2$ distance, and allows for visualization for human experts.

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs.

Coldplay's Chris Martin made a surprise appearance at a couple's wedding to play the music for their first dance. The groom's mother had asked the singer for a video message to be played at the wedding of Abbie and James Hotchkiss from Stafford. He went one better, though, and said he would appear in person, with only the newlyweds and the groom's parents in on the secret. Surprised guests saw him walk into the wedding venue, Blithfield Lakeside Barns in Staffordshire, wearing a white beanie hat to perform All My Love at the piano while the couple danced. Guests took a while to notice it was actually him, but didn't want to ruin our wedding day so asked us loads of questions once he'd gone, Mrs Hotchkiss said.

Experts urge caution as Trump's big bill incentivizes AI in healthcare For states to receive certain funding stipulated in the Trump administration's "big, beautiful" bill, they must meet three of 10 criteria - including integrating more artificial intelligence ( AI) technology in healthcare settings - which experts say could have major benefits and liabilities for under-resourced hospitals, depending on how it's implemented. The Rural Health Transformation Fund is a carveout that will provide $50bn over a period of five years to states who meet certain application criteria, including "consumer-facing, technology-driven solutions for the prevention and management of chronic diseases," and "providing training and technical assistance for the development and adoption of technology-enabled solutions that improve care delivery in rural hospitals, including remote monitoring, robotics, artificial intelligence, and other advanced technologies". Analysts have noted that this $50bn will not be nearly enough to make up for the Congressional Budget Office's projected $911bn reduction in Medicaid spending over the next decade under the bill (Obba). These cuts will affect both patients who lose free health coverage under Medicaid, and hospitals who benefit from those patients' Medicaid reimbursements. Chenhao Tan, associate professor of data science at the University of Chicago, and Karni Chagal-Feferkorn, an assistant professor at the University of South Florida's college of AI and cybersecurity, said AI technology could provide major benefits to rural hospitals that are frequently under-resourced and under-staffed.