As climate change intensifies, healthcare systems will increasingly face unprecedented climatic emergencies surpassing assumptions underlying their emergency protocols. While conventional scenario-based preparedness and contingency planning will remain relevant for most events, they may falter…
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Precise localization of perfusion deficits in diffusion-weighted MRI (DWI) is critical for acute ischemic stroke management. However, existing deep learning methods typically produce discrete binary masks, failing to capture the continuous nature of ischemic…
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Excessive sodium intake is a major contributor to cardiovascular disease and mortality. Reducing daily sodium consumption to below 2 g (~5 g of salt) effectively lowers cardiovascular mortality rates. The growing reach of social media and…
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Mentorship in predictive large language models (LLMs) presents both opportunities and risks of inequity if these systems are not designed with care. Mentorship quietly shapes medical careers, yet opportunities for it remain unequal. LLMs…
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Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including recognised rare diseases and low-prevalence conditions in the ICU, remain underserved due to data scarcity…
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The translation of Artificial Intelligence (AI) into clinical practice demands high-quality Real-World Data (RWD), yet unstructured healthcare information poses a significant barrier. To address this, we developed S-RACE, a secure, cloud-based platform designed to…
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Vascular invasion assessment is critical for surgical planning in pancreatic ductal adenocarcinoma (PDAC). Current CT-based assessments often rely on radiologists’ subjective 2D interpretations, which may not capture the continuous, three-dimensional tumor–vessel interactions and multiple…
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Integrating complementary surface and cross sectional cues is central to preoperative assessment of colorectal cancer, but technically challenging because endoscopic images and pelvic CT encode anatomy at different scales. Here we present HydraMamba, a…
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Preoperative differentiation of preinvasive lesions, minimally invasive adenocarcinomas, and invasive adenocarcinomas within pure ground-glass nodules (pGGNs) is challenging. Herein, this study investigated the potential of vision-language models to assist radiologists in noninvasively predicting pGGN…
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Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial…
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