The number of inherited heart disease (IHD) studies using artificial intelligence (AI) has increased rapidly over the last years. In this scoping review, we aimed to present an overview of the current literature available on the applicability of AI w...
Task allocation research is often efficiency-focussed, but procedural and work-psychological perspectives are required to enable human-centred human-robot interaction (HRI). Hence, the motivational and cognitive outcomes of the degree of worker influ...
OBJECTIVES: Magnetic resonance imaging (MRI) is an important tool for diagnosis and monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for predicting disease worsening is still being debated. The aim of this study ...
BACKGROUND: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it ha...
BACKGROUND: Difficulties in establishing diagnosis of small bowel (SB) disorders, prevented their effective treatment. This problem was largely resolved by wireless capsule endoscopy (WCE), which has since become the first line investigation for susp...
Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technica...
OBJECTIVE: Deep learning image reconstruction (DLIR) is a new reconstruction method for maintaining image quality at reduced radiation dose. The purpose of this study was to compare image quality of reduced-dose DLIR images with the standard-dose ada...
Stroke is a common reason for motor disability and is often associated with spasticity and poor motor function of the upper limbs involved. Spasticity management is important to accelerate motor recovery. The objective of this study was to investiga...
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