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Abstract
In recent years, artificial intelligence (AI) has proven to be an important technology for enhancing the radiology workflow, leading to improvements in cutting-edge practices. Historically, AI has only been seen as a fully automatic Computer-Aided Diagnosis tool. Nowadays, its applications have significantly been extended from imaging interpretation to image quality control and image acquisition, effective medical big data mining and utilization, to study individual radiologist performance and automate the higher quality radiology workflow, making it an important part of the growing interest in the development of quality of care. Radiologist-driven automation (RDA) is defined as automated tools that are built to aid radiologists in their workflow, with such tools still requiring a report by the radiologist as the endpoint of care. Most, if not all, RDA tools can be classified as using AI
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