Journal of the American College of Radiology : JACR
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The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
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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.
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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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The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. ⋯ Radiologists have a fiduciary responsibility to protect the interest of their patients. As such, the community of radiology leaders, ethicists, and informaticists must have a conversation about the appropriate way to deal with these issues and help lead the way in developing capabilities in the most just, ethical manner possible.