The Ring star Daveigh Chase's autopsy reveals actress died from AIDS after painful health battle Clint Eastwood's son reveals shocking on-set spat with entitled Hollywood star: 'They think the world owes them' I thought my drinking was harmless until I realized I couldn't go a day without it. Then I discovered a $3 miracle pill that killed all my alcohol cravings... I'm completely cured I thought I knew the secret to great sex... then one man proved me so wrong: JANA HOCKING is mind-blown by trick that women over 40 are loving Hollywood nepo baby, 17, shows she has her father's unique style with edgy turn on red carpet... who is she? How well do you REALLY know America? Take our ultimate history quiz to find out... Stay-alert warnings issued as sharks return to one of America's busiest beaches Harry DOES want to bring Archie and Lili to the UK - but not without'proportionate protective security', team Sussex say: Duke and Duchess lay out demands after'state-funded guards turned down at 11th hour' Boy, 12, reveals how brother's quick thinking saved him from shark bite while on gorgeous Bahamas vacation Former FBI agent believes there's sinister motive behind new Nancy Guthrie ransom note... as desperation seeps in At 45 I was plagued by muscle pain, brain fog and memory loss... but it wasn't the menopause. I caught a disease while sitting on my sofa.

NFL great Chris Johnson reveals common first symptom of paralyzing ALS that's left him unable to speak or move: 'He thought it was a pinched nerve' Why Thylane Blondeau renounced her'most beautiful girl in the world' title after struggling with childhood honour - as she prepares to take on new name after marrying French DJ Ben Attal Stay-alert warnings issued as sharks return to one of America's busiest beaches I thought my drinking was harmless until I realized I couldn't go a day without it. Then I discovered a $3 miracle pill that killed all my alcohol cravings... I'm completely cured I thought I knew the secret to great sex... then one man proved me so wrong: JANA HOCKING is mind-blown by trick that women over 40 are loving How well do you REALLY know America? Take our ultimate history quiz to find out... Prince Harry vows to'explore every option to bring Archie and Lilibet to the UK' after hitting out at'bizarre decision' to deny armed security At 45 I was plagued by muscle pain, brain fog and memory loss... but it wasn't the menopause. I caught a disease while sitting on my sofa. I escaped Europe's dying democracies and discovered the real secret to America's enduring success: AYAAN HIRSI ALI Physical therapist who killed newborn and threw her in dumpster was finally snared 17 years later thanks to COSTCO receipt... as she's given incredibly light sentence for manslaughter I feel terrible saying my husband's penis isn't big enough for me.

Prince Harry vows to'explore every option to bring Archie and Lilibet to the UK' after hitting out at'bizarre decision' to deny armed security Sordid marriage secrets of country star Sam Hunt: Insiders reveal wife's brutal ultimatum... as singer's strange disappearance fuels Nashville whispers At 45 I was plagued by muscle pain, brain fog and memory loss... but it wasn't the menopause. I caught a disease while sitting on my sofa. Trump's crusade to kill mail-in ballots crushed by Supreme Court in a blow to his election obsession'Most beautiful girl in the world' Thylane Blondeau is married: Model stuns as she ties the knot with French DJ Ben Attal in Paris three months after getting engaged Suffocating'mega heat dome' to engulf 35 states as forecasters issue urgent health alert Physical therapist who killed newborn and threw her in dumpster was finally snared 17 years later thanks to COSTCO receipt... as she's given incredibly light sentence for manslaughter I feel terrible saying my husband's penis isn't big enough for me. I have an idea of how to fix it - but I'm worried it will crush him: ASK JANA The signs I missed that I was sleeping next to a killer: My husband dismembered his secret girlfriend with a machete. Family of teenager last seen partying at'Sex Rock' breaks silence on'disrespectful' posts after her body was found in Kentucky lake Mary-Kate and Ashley Olsen pose with ALL of their siblings in rare family photo for brother Trent's wedding World's first'pregnant man' Thomas Beatie reveals astonishing full story for the first time as his daughter turns 18... and confronts a hard truth about trans teens Burger King president reveals the unexpected menu item he won't eat: 'we can do better' Are you tired all the time, with weak erections and stubborn belly fat that won't shift?

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Objective gait assessment in Parkinson's Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce CARE-PD, the largest publicly available archive of 3D mesh gait data for PD, and the first multi-site collection spanning 9 cohorts from 8 clinical centers. All recordings (RGB video or motion capture) are converted into anonymized SMPL meshes via a harmonized preprocessing pipeline. CARE-PD supports two key benchmarks: supervised clinical score prediction (estimating Unified Parkinson's Disease Rating Scale, UPDRS, gait scores) and unsupervised motion pretext tasks (2D-to-3D keypoint lifting and full-body 3D reconstruction). Clinical prediction is evaluated under four generalization protocols: within-dataset, cross-dataset, leave-one-dataset-out, and multi-dataset in-domain adaptation. To assess clinical relevance, we compare state-of-the-art motion encoders with a traditional gait-feature baseline, finding that encoders consistently outperform handcrafted features. Pretraining on CARE-PD reduces MPJPE (from 60.8 mm to 7.5 mm) and boosts PD severity macro-F1 by 17 percentage points, underscoring the value of clinically curated, diverse training data. CARE-PD and all benchmark code are released for non-commercial research at https://neurips2025.care-pd.ca.

Clinical diagnosis is a highly specialized discipline requiring both domain expertise and strict adherence to rigorous guidelines. While current AI-driven medical research predominantly focuses on knowledge graphs or natural text pretraining paradigms to incorporate medical knowledge, these approaches primarily rely on implicitly encoded knowledge within model parameters, neglecting task-specific knowledge required by diverse downstream tasks. To address this limitation, we propose Retrieval-Augmented Diagnosis (RAD), a novel framework that explicitly injects external knowledge into multimodal models directly on downstream tasks. Specifically, RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guidelineenhanced contrastive loss that constrains the latent distance between multi-modal features and guideline knowledge, and the dual transformer decoder that employs guidelines as queries to steer cross-modal fusion, aligning the models with clinical diagnostic workflows from guideline acquisition to feature extraction and decision-making. Moreover, recognizing the lack of quantitative evaluation of interpretability for multimodal diagnostic models, we introduce a set of criteria to assess the interpretability from both image and text perspectives. Extensive evaluations across four datasets with different anatomies demonstrate RAD's generalizability, achieving state-of-the-art performance. Furthermore, RAD enables the model to concentrate more precisely on abnormal regions and critical indicators, ensuring evidence-based, trustworthy diagnosis. Our code is available at this repository.

Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations.

Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.e. when data cannot be fully shared or pooled across compute nodes. We adopt a Consensus Monte Carlo (CMC) approach, in which an MCMC algorithm is run independently within each data silo to estimate local posterior distributions, which are then aggregated to approximate the posterior over the full data. The variational CMC approach of Rabinovich, Angelino and Jordan (2015) [1] frames the aggregation step as a variational inference problem, but their application to mixtures assumes the number of clusters and key mixture parameters to be known. Our main methodological contributions are: (i) an extension of variational CMC to over-fitted Bayesian mixture models that infer the number of clusters and all model parameters, without requiring conjugacy; (ii) novel cluster-matching algorithms suitable for cross-silo settings in which not every cluster appears in each local dataset; (iii) a number of inference strategies for the aggregation step, matched to different federated learning constraints; and (iv) guidelines for choosing among these in practice. A comprehensive simulation study validates the framework and allows us to compare to state-of-the-art federated learning alternatives. Notably, we show that when the composition of local datasets reflects the underlying clustering structure in the data, our approach can recover small clusters with greater accuracy than standard MCMC applied to the pooled data. We illustrate the framework on large-scale electronic health record data, identifying multi-morbidity patterns in a British geriatric population.

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.

Large Language Models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of these, exact unlearning-- which retrains the model from scratch without the target data--is widely regarded as the gold standard for mitigating privacy risks in deployment. In this paper, we revisit this assumption in a practical deployment setting where both the pre-and post-unlearning logits API are exposed, such as in open-weight scenarios. Targeting this setting, we introduce a novel data extraction attack that leverages signals from the pre-unlearning model to guide the post-unlearning model, uncovering patterns that reflect the removed data distribution. Combining model guidance with a token filtering strategy, our attack significantly improves extraction success rates-- doubling performance in some cases--across common benchmarks such as MUSE, TOFU, and WMDP. Furthermore, we demonstrate our attack's effectiveness on a simulated medical diagnosis dataset to highlight real-world privacy risks associated with exact unlearning. In light of our findings, which suggest that unlearning may, in a contradictory way, increase the risk of privacy leakage during realworld deployments, we advocate for evaluation of unlearning methods to consider broader threat models that account not only for post-unlearning models but also for adversarial access to prior checkpoints.