Film-based cultural narrative intervention to enhance mental health literacy and resilience in indigenous adolescents: a mixed-methods co-design study

BackgroundIndigenous adolescents in low- and middle-income countries experience heightened mental health risks while having limited access to culturally appropriate interventions. Mental health literacy, stigma, and resilience are critical determinants of help-seeking and long-term well-being, yet culturally grounded interventions addressing these factors remain scarce.ObjectiveThis study aimed to co-design and evaluate a culturally sensitive, film-based intervention to […]

An interpretable, clinically grounded framework for digital speech biomarker development in neurodegenerative diseases

IntroductionCommunication ability—a key determinant of quality of life—is frequently affected and progressively declines in neurodegenerative diseases. Effective management of progressive communication disorders requires a personalized approach to deliver timely interventions tailored to the evolving profiles of communicative impairment, thereby supporting functional communication throughout the disease course. To this end, reliable tools capable of detecting and […]

Assessment of frontier Large Language Models in sleep medicine

Study objectivesTo evaluate and compare the performance of two proprietary frontier large language models (LLMs), ChatGPT-5 and Grok-4, on diagnostic reasoning and foundational knowledge tasks within the specialty domain of sleep medicine.MethodsThe models were evaluated on two tasks: case-based reasoning using 79 clinical vignettes from the AASM Case Book of Sleep Medicine and knowledge assessment […]

Performance of large language models and prompt engineering strategies for data extraction in systematic reviews

BackgroundSystematic reviews depend on manual data extraction and synthesis, which are time-consuming and prone to human error. Although large language models (LLMs) have the potential to automate parts of this process, their accuracy, reproducibility, and efficiency across different models and prompt strategies remain insufficiently characterized.MethodsThis study evaluated the performance of three LLMs, including ChatGPT-4o, Claude […]

Beyond the algorithm: embedding ethics for trustworthy AI in radiology and oncology

BackgroundArtificial intelligence (AI) in radiology and oncology promises improvements in diagnostic accuracy and efficiency yet introduces complex ethical and societal challenges. Governance efforts frequently rely on high-level principles such as trustworthiness and fairness, which risk becoming ineffective when not grounded in specific contexts. This study presents findings from our work on ethical and societal aspects […]

The MediVoice implementation journey: ambient artificial intelligence for clinical documentation

Healthcare systems are increasingly turning to ambient Artificial Intelligence (AI) scribes to reduce documentation burden and lighten clinicians’ cognitive load. In this brief research report, we introduce MediVoice, an ambient AI scribe developed and implemented within the National University Health System, Singapore. MediVoice was piloted across multiple clinical settings and rapidly evaluated through Plan–Do–Study–Act cycles. […]

Essential Oil-enhanced digital hypnotherapy for subclinical generalized anxiety: a study protocol for a randomized controlled trial

BackgroundSubsyndromal generalized anxiety is highly prevalent and associated with impaired well-being, elevated stress, and functional limitations, yet affected individuals often do not meet criteria for guideline-based treatment. Scalable, low-threshold digital interventions that target psychophysiological regulation may help address this gap. Guided self-hypnosis and aromatherapy using essential oils have each demonstrated anxiolytic and relaxation-promoting effects. Combining […]

Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning

arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria and search algorithms, often treating layer redundancy as an inherent structural property of pretrained networks. In contrast, we adopt a emphfunctional perspective, where redundancy is jointly influenced by the […]

EVT-Based Generative AI for Tail-Aware Channel Estimation

arXiv:2604.25008v1 Announce Type: cross Abstract: Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such […]

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