Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring…
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Approximately 50 million people suffer from sepsis yearly, and 13 million die from it. For every hour a patient with septic shock is untreated, their survival rate decreases by 8%. Therefore, rapid detection and…
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Many rare genetic diseases have recognizable facial phenotypes that serve as diagnostic clues. While Large Language Models (LLMs) have shown potential in healthcare, their application to rare genetic diseases still faces challenges like hallucination…
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Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition, and accurate prediction of functional outcomes is critical for optimizing patient management within the initial 3 days of presentation. However, existing clinical scoring systems and imaging…
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Large language models (LLMs) have shown promising capabilities across diverse domains, yet their application to complex clinical prediction tasks remains limited. In this study, we present CARE-AD (Collaborative Analysis and Risk Evaluation for Alzheimer’s…
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Healthcare delivery is under strain, and the reusing of routinely collected data promises improved outcomes. Still, concerns remain about the public’s willingness to share their health data. This study examines worldwide willingness to share…
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Visual monitoring of pre-term infants in intensive care is critical to ensuring proper development and treatment. Camera systems have been explored for this purpose, with human pose estimation having applications in monitoring position, motion,…
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Age is the most important risk factor for the majority human diseases, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (BA). These predictors offer promising insights…
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Early Autism Spectrum Disorder (ASD) identification is crucial but resource-intensive. This study evaluated a novel two-stage multimodal AI framework for scalable ASD screening using data from 1242 children (18–48 months). A mobile application collected…
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Preterm birth (PTB) significantly contributes to maternal and perinatal mortality and lifelong morbidity. While large language models (LLM) offer considerable potential for disease risk prediction and early detection, their application to PTB prediction using…
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