Journal of the American College of Radiology : JACR
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In December 2019, a novel coronavirus (COVID-19) pneumonia emerged in Wuhan, China. Since then, this highly contagious COVID-19 has been spreading worldwide, with a rapid rise in the number of deaths. Novel COVID-19-infected pneumonia (NCIP) is characterized by fever, fatigue, dry cough, and dyspnea. ⋯ The purpose of the present review is to briefly discuss the known epidemiology and the imaging findings of coronavirus syndromes, with a focus on the reported imaging findings of NCIP. Moreover, the authors review precautions and safety measures for radiology department personnel to manage patients with known or suspected NCIP. Implementation of a robust plan in the radiology department is required to prevent further transmission of the virus to patients and department staff members.
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This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. ⋯ It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.
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Acute pancreatitis (AP) is divided into two types: interstitial edematous and necrotizing. AP severity is classified clinically into mild, moderately severe, and severe, depending on the presence and persistence of organ failure and local or systemic complications. The revised Atlanta classification divides the clinical course of AP into an early (first week) and late phase (after first week) and the clinical phase determines the role of imaging. ⋯ 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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The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. ⋯ We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.
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Our understanding of human health may be significantly enhanced in the near future because of the unprecedented volume of digitized health care data and the availability of artificial intelligence to mine these data for correlations that could drive new research hypotheses and improved patient care. Observational studies and randomized trials are traditional methods to generate and test hypotheses. ⋯ In 2018, the National Institutes of Health unveiled its Strategic Plan for Data Science, which includes a far-reaching plan for the use of big data to stimulate new research discoveries. Both researchers and physicians will need to learn and apply new skills in understanding the use of artificial intelligence and other tools, as well as in the direct application of data collection and mining in their own practices and patients.