Many aspects are being investigated, including aiding with appropriate exam selection, linking patients or healthcare providers with existing CDS tools, or obtaining data to help cast light on the future. Also referred to as "deep-learning reconstruction," deep-learning techniques are being developed to improve technical aspects of image acquisition -- e.g., to reconstruct virtual high-dose CT images from low-dose CT images with reduced metal artifact. Automating and refining aspects of image processing that should be more accurate and less time-consuming to assign to machines, such as measuring lesion or organ volume or counting large numbers of lesions (e.g., nodules or metastases). Probably the most talked about use of AI in radiology, where the AI either flags areas it thinks contain pathology or ultimately acts as a first or second reader. This may be useful in areas where there are reduced numbers of radiologists, e.g., a neural network trained to identify meniscal tears on knee MRI scans.