BackgroundAcquired brain injury (ABI), which includes traumatic brain injury (TBI) and stroke, is a leading cause of disability. Evidence shows that sex may influence functional recovery post-acquired brain injury, potentially due to biological (e.g., hormones) and social factors (e.g., caregiver availability). Meanwhile, new neurorehabilitation technologies—such as virtual reality, robotic-assistance, and brain-computer interfaces—offer promising avenues for […]
Artificial intelligence in transitional care: practice, promise, and pitfalls—a scoping review
BackgroundCare transitions, which involve the movement of patients between different care settings are critical moments in the care continuum but are often compromised by fragmented care delivery or poor information transfer among providers. To address this, Transitional Care (TC) programs were developed to address these challenges. Recently, Artificial Intelligence (AI) tools were introduced to support […]
A randomized controlled trial on the application of a chronic disease management platform based on digital health technology combined with an innovative model of intelligent management for hypertension in patients with hypertension
ImportanceDigital health technology (DHT)-based chronic disease management platforms combined with smart hypertension models may improve patient self-management.ObjectiveTo compare the effect of Nantong University Affiliated Hospital’s DHT platform combined with an intelligent hypertension management model (providing education, follow-up, evaluation) vs. traditional offline management on patients’ systolic blood pressure (SBP).Design, setting, and participantsThis was a two-arm, parallel-group […]
The COMFORTage project: study protocol for the integration of multiple sources towards personalised preventions at Ace Alzheimer Center Barcelona
IntroductionAgeing is accompanied by gradual biological and cognitive changes that increase vulnerability to chronic diseases and neurodegenerative conditions. As populations age, dementia prevalence continues to rise, highlighting the need for earlier detection and personalised prevention strategies. Against this background, the COMFORTage project, funded by Horizon Europe, brings together a multidisciplinary consortium across 12 countries to […]
Multimodal learning for scalable representation of high-dimensional medical data
Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively leveraging multimodal data, particularly digital pathology whole slide images (WSIs) and genomic sequencing, remains a significant challenge due to the intrinsic heterogeneity of these modalities and the need for scalable and interpretable frameworks. Existing diagnostic […]
A GPT-reinforced social robot for patient communication: a pilot study
ProblemQuality healthcare requires effective patient communication. However, lack of personnel and increasing demands on healthcare professionals (HCPs) create a need for innovative solutions that enhance accessibility and delivery of information to patients.GoalWe propose an innovative method to convey treatment and disease information using an Artificial Intelligence (AI)-driven social robotic physical interface. The aim of this […]
A comparative accuracy study of multimodal LLMs, VLM and agent-based framework for pulmonary nodule detection on chest radiographs
BackgroundArtificial intelligence technologies are being actively introduced in clinical practice. The most promising solutions are AI-assistants based on large language models (LLMs). Determining the feasibility of integrating such applications in clinical practice requires independent performance assessments. This study assessed accuracy of several multimodal LLMs in detecting pulmonary nodules on chest radiographs (CXR).MethodsThis study included 9 […]
Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients
Amyotrophic lateral sclerosis (ALS) is a progressive and debilitating neurodegenerative disease. Digital biomarkers derived from smartphone data can enable scalable, low-cost, remote, unobtrusive, and quantitative measurement of physical activity (PA). These biomarkers offer opportunities for quasi-continuous assessment of PA levels, which may provide new methods for monitoring ALS disease progression in real time. In this […]
One Token Is Enough: Improving Diffusion Language Models with a Sink Token
arXiv:2601.19657v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have emerged as a compelling alternative to autoregressive approaches, enabling parallel text generation with competitive performance. Despite these advantages, there is a critical instability in DLMs: the moving sink phenomenon. Our analysis indicates that sink tokens exhibit low-norm representations in the Transformer’s value space, and that […]
Trustworthy Intelligent Education: A Systematic Perspective on Progress, Challenges, and Future Directions
arXiv:2601.21837v1 Announce Type: cross Abstract: In recent years, trustworthiness has garnered increasing attention and exploration in the field of intelligent education, due to the inherent sensitivity of educational scenarios, such as involving minors and vulnerable groups, highly personalized learning data, and high-stakes educational outcomes. However, existing research either focuses on task-specific trustworthy methods without a […]
RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendation
arXiv:2601.19120v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used to generate natural-language explanations in recommender systems, acting as explanation agents that reason over user behavior histories. While prior work has focused on explanation fluency and relevance under fixed inputs, the robustness of LLM-generated explanations to realistic user behavior noise remains largely unexplored. […]
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
arXiv:2601.21649v1 Announce Type: cross Abstract: The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suffer from a fundamental capability gap: unlike frontier large models, they lack the inference-time strong generalization to work with complicated, unfamiliar codebases. We identify that the prevailing Task-Centric […]