OBJECTIVES: Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Sep 18, 2020
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially availab...
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role ...
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect ...
PURPOSE: To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance.
Bloom's Taxonomy, an integral component of learning theory since its inception, describes cognitive skill levels in increasing complexity (Remember, Understand, Apply, Analyze, Evaluate, and Create). Considering Bloom's Taxonomy when writing learning...
Journal of the American College of Radiology : JACR
Sep 2, 2020
Opportunities to share or sell images are common in radiology. But because these images typically originate as protected health information, their use admits a host of ethical and regulatory considerations. This article discusses four scenarios that ...
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