Journal of the American College of Radiology : JACR
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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.
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The purpose of this study was to adapt our radiology reports to provide the documentation required for specific International Classification of Diseases, tenth rev (ICD-10) diagnosis coding. ⋯ The number of radiology studies with a specific ICD-10 code can be improved through quality improvement methodology, specifically through the use of technologist-acquired clinical histories and structured reporting.
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Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.
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Radiologists seek mentors to facilitate career advancement and to help overcome professional and personal challenges. Characteristics of effective mentors include altruism, honesty, active listening skills, a collaborative approach, and accessibility. ⋯ Strategies to support mentor-mentee relationships include effective pairing of mentors with mentees, maintenance of confidentiality, clear definition of expectations, voluntary participation, and allowing mentees to change mentors without judgment or repercussions. A culture shift is needed in radiology departments to enable successful mentor-mentee relationships.