Integration of artificial intelligence into healthcare systems prompts essential questions about efficacy, ethics and patient trust. Among imaging modalities, mammography stands out for its suitabili
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Reducing radiation exposure in computed tomography (CT) remains a core objective in clinical imaging, particularly where patients require repeated scans. In oncological imaging, however, th
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Artificial intelligence (AI) is revolutionising radiology, offering improved diagnostic precision and operational efficiency. Deep learning models, particularly within medical imaging, are
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With diabetes projected to affect over 1.3 billion individuals globally by 2050, delivering personalised support has become a critical component of disease management. This requires recogni
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Despite the availability of effective treatments, accessible strategies for heart failure (HF) risk stratification remain limited. Current clinical risk scores (e.g., PCP-HF, PREVENT, Health ABC)...
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Computer vision, a branch of artificial intelligence (AI), is rapidly transforming the healthcare landscape. Designed to replicate the way humans see and interpret the world, this technolog
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Retrieving structured information from radiology reports remains a major challenge within clinical and research settings. Traditional keyword-based systems, despite widespread use, often fa
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The clinical deployment of artificial intelligence (AI) in prostate cancer detection is advancing rapidly, particularly in the use of multiparametric MRI for diagnosing clinically significa
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Artificial Intelligence has emerged as a promising support tool for fracture diagnosis in clinical radiology, aiming to address persistent issues such as missed fractures and diagnostic var
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Magnetic resonance imaging (MRI) is the preferred method for evaluating internal knee injuries, particularly in sports-related cases. However, traditional MRI protocols often involve long s
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