The 2025 Voice AI Symposium represented a transition from conceptual research to clinical implementation in vocal biomarker science. Hosted by the NIH-funded Bridge2AI-Voice consortium, the meeting convened global experts to address the methodological, ethical, and translational challenges of integrating voice-based artificial intelligence (AI) into healthcare. This mini-review synthesizes symposium insights across six domains: multimodal integration, […]
Correction: Feasibility and reliability of a smartwatch to detect atrial fibrillation after cardiac surgery: a prospective study
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Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study
Background: “I’m not a doctor, but…” is a typical response when asking considerate laypeople for health advice. However, seeking medical advice has also shifted to digital settings, where the expertise of the other party is less transparent than in face-to-face interactions. Recently, large language models (LLMs) have emerged as easily accessible tools, offering a novel […]
Correction: Advanced EEG signal classification for neural prosthetic devices using metaheuristic and deep learning techniques
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Development of a generative AI agent for family support in implementing family-based treatment for children and adolescents with anorexia nervosa
IntroductionFamily-based treatment (FBT) is a first-line psychotherapy for children and adolescents with anorexia nervosa (AN). However, families must understand the principles of FBT, provide meal support, and manage their children’s pathological behaviors. Difficulties occur outside clinic hours when it is impossible to consult professionals. This “support gap” increases caregivers’ psychological distress and threatens their treatment […]
Digital therapy using dichoptic visual perceptual learning to improve stereopsis in children with intermittent exotropia
BackgroundIntermittent exotropia impairs binocular vision and stereopsis in children, and visual perceptual learning (VPL) with dichoptic stimulation offers a potential therapy. This prospective exploratory study aimed to evaluate the efficacy of an 8-week at-home dichoptic VPL program delivered through virtual reality (VR)-based digital therapy in improving stereopsis and binocular sensory function in children with intermittent […]
L2GTX: From Local to Global Time Series Explanations
arXiv:2603.13065v1 Announce Type: cross Abstract: Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging. Explanations for time series must respect temporal dependencies and identify patterns that recur across instances. Existing approaches face three limitations: model-agnostic XAI methods developed for images and tabular data do not readily […]
How animal movement influences wildlife-vehicle collision risk: a mathematical framework for range-resident species
arXiv:2507.17058v2 Announce Type: replace Abstract: Wildlife-vehicle collisions (WVC) threaten both biodiversity and human safety worldwide. Despite empirical efforts to characterize the major determinants of WVC risk and optimize mitigation strategies, we still lack a theoretical framework linking traffic, landscape, and individual movement features to collision risk. Here, we introduce such a framework by leveraging recent […]
Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
arXiv:2603.12279v1 Announce Type: new Abstract: Intracranial language brain-computer interfaces (BCIs) are a promising route for restoring communication in people with severe motor and speech impairments, but clinical translation remains limited by fragmented evidence and unresolved design trade-offs across neuroscience, hardware, algorithm, evaluation, and clinical deployment. This review synthesizes progress in neural mechanisms of overt, mimed, […]
Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning
arXiv:2603.12606v1 Announce Type: cross Abstract: Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aware language descriptions. To […]
Are General-Purpose Vision Models All We Need for 2D Medical Image Segmentation? A Cross-Dataset Empirical Study
arXiv:2603.13044v1 Announce Type: cross Abstract: Medical image segmentation (MIS) is a fundamental component of computer-assisted diagnosis and clinical decision support systems. Over the past decade, numerous architectures specifically tailored to medical imaging have emerged to address domain-specific challenges such as low contrast, small anatomical structures, and limited annotated data. In parallel, rapid progress in computer […]
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
arXiv:2603.12183v2 Announce Type: replace-cross Abstract: Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Materials (PCM) closes this gap through three stages: adversarial falsification […]