Cognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these…
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Medical image segmentation is vital for accurate diagnosis. While U-Net-based models are effective, they struggle to capture long-range dependencies in complex anatomy. We propose GH-UNet, a Group-wise Hybrid Convolution-ViT model within the U-Net framework,…
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The applicability of vision-language models (VLMs) for acute care in emergency and intensive care units remains underexplored. Using a multimodal dataset of diagnostic questions involving medical images and clinical context, we benchmarked several small…
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Endoscopic interventions are essential for diagnosing and treating gastrointestinal conditions. Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. This study evaluates a machine…
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Exergames are emerging tools for cognitive-motor neurorehabilitation, but their effects in adults with neurological conditions remain insufficiently explored. This systematic review examined studies from PubMed, Scopus, Embase, and Web of Science, assessing the impact…
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Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective…
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Traditional tuberculosis (TB) screening involves radiologists manually reviewing chest X-rays (CXR), which is time-consuming, error-prone, and limited by workforce shortages. Our AI model, AIRIS-TB (AI Radiology In Screening TB), aims to address these challenges…
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Lymph node micro-metastasis represents the initial stage of breast cancer spread or metastasis. However, the limited size of these hidden lesions restricts dataset expansion, presenting a significant challenge for manual examination and conventional deep…
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Digital self-help can improve access to mental health care, but poor engagement limits effectiveness. This single-blind 2 x 2 x 2 factorial randomized controlled trial examined whether an optimized graphical user interface (GUI), automated reminders (AR), and an…
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Patient portals are widely available to facilitate self-service interactions, including appointment booking, offering convenience to users. Promoting clinically appropriate care pathways, however, is complex; portals must recognize patient intent while striving for an intuitive…
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