Journal of the American College of Radiology : JACR
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Headaches in children are not uncommon and have various causes. Proper neuroimaging of these children is very specific to the headache type. Care must be taken to choose and perform the most appropriate initial imaging examination in order to maximize the ability to properly determine the cause with minimum risk to the child. ⋯ The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. ⋯ Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
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Publication metrics are useful in evaluating academic faculty for awarding grants, recruitment, and promotion. A new metric, the relative citation ratio (RCR), was recently released by the National Institutes of Health (NIH); however, no benchmark data yet exist. We sought to create benchmark data for physician faculty in academic radiation oncology (RO) and analyze correlations associated with increased academic productivity. ⋯ Current academic radiation oncologists have a high mean RCR value relative to the benchmark NIH RCR value of 1. All subgroups analyzed had an RCR value above 1 with professor or chair and previous NIH funding having the highest RCR and weighted RCR values overall. These data may be useful for self-evaluation of ROs as well as evaluation of faculty by institutional and departmental leaders.