This review summarizes the current literature on the use of wearable devices for collecting physiological data in pediatric and young adult (0−25 years) oncology. Searches were conducted in MEDLINE, PubMed and Embase, focusing on pediatric…
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Diagnosing vestibular disorders remains challenging due to complex symptoms and extensive history-taking required. While machine learning approaches have shown promise in medical diagnostics, their application to vestibular disorder classification has been limited. We developed…
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Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzed 142 studies published between 2013 and 2024, focusing on safety (n = 55), efficacy (n = 46),…
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Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based…
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Stroke is a leading cause of global morbidity and mortality, disproportionately impacting lower socioeconomic groups. In this study, we evaluated three generative LLMs—GPT, Claude, and Gemini—across four stages of stroke care: prevention, diagnosis, treatment,…
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Brain-based technologies for human augmentation face challenges in personalization and real-world translation. We present an AI-driven personalized Bayesian optimization algorithm that remotely adjusts neurostimulation parameters based on baseline ability and head anatomy to enhance…
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Digital Health Technologies represent a marked shift from current medical technologies in use, the approach to health and healthcare and stakeholders engaged in healthcare delivery. What the digitalized future of medicine will look like…
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Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained…
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Cognitive impairment is a frequent complication of Parkinson’s disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from…
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Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique…
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