Aim: To describe and quantify the range of non-interpretive tasks engaged in by consultant radiologists in Ireland today.
Materials And Methods: A multiple-choice electronic survey was circulated to over 200 Irish consultant radiologists and results were analysed.
Results: Responses were received from approximately 40% of the 267 full-time equivalent consultants in Ireland at the time of the survey. There was a wide sub-specialty mix, and responses from both clinical directors and those without designated administrative responsibility. Overall, the three most time-consuming activities were reported to be multidisciplinary meetings, vetting, and informal consultations. Non-interpretive tasks were estimated to account for 35% of the working week, with higher figures (up to 60%) for clinical directors.
Conclusion: Consultant radiologists in Ireland spend a significant proportion of their time engaged in non-interpretive radiology; acknowledgement and scheduling of non-interpretive tasks will need to be supported by appropriate workforce planning. Non-interpretive skills will also need to be addressed during training to adequately prepare trainees for the reality of the workplace.
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http://dx.doi.org/10.1016/j.crad.2017.10.023 | DOI Listing |
Pediatr Radiol
December 2024
University of North Carolina, 200 Old Clinic, CB #7510, Chapel Hill, NC, 27599, USA.
Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric radiologists to essential AI concepts, including topics such as use case, data science, machine learning, deep learning, natural language processing, and generative AI as well as basics of AI training and validating. We outline the unique challenges of applying AI in pediatric imaging, such as data scarcity and distinct clinical characteristics, and discuss the current uses of AI in pediatric radiology, including both image interpretive and non-interpretive tasks.
View Article and Find Full Text PDFCurr Probl Diagn Radiol
December 2023
Site Chair, Department of Diagnostic, Molecular, and Interventional Radiology, Mount Sinai West and Mount Sinai St. Luke's Hospitals, Icahn School of Medicine at Mount Sinai. 1000 10th Ave, Radiology Department, 4B 25, New York, NY 10019, USA.
Objective: Effort has been made to minimize the burden of non-interpretive tasks (NITs), in particular by hiring and training non-radiologist support staff as reading room coordinators (RRCs). Our medical center recruited and trained senior medical students from our affiliated school of medicine to work alongside on-call radiology residents as RRCs.
Methods: A 12-month Malpractice Carrier monetary grant was acquired to fund medical students at with the aim to reduce malpractice risk.
Front Radiol
August 2023
The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States.
With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice.
View Article and Find Full Text PDFDiagn Interv Imaging
January 2023
Department of Radiology, McMaster University, 1280 Main St. W., Hamilton, Ontario, L8S 3L8, Canada. Electronic address:
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care.
View Article and Find Full Text PDFCurr Probl Diagn Radiol
June 2022
Radiology Department, Massachusetts General Hospital, Boston, MA. Electronic address:
Rationale: Over the past decade, technological advances have provided new tools for radiologists. However, the effect of these technological advances on radiologist workload and detecting pathologies needs to be assessed.
Objective: The purpose of this study is to assess the workload, including non-interpretative tasks, associated with Computed Tomography Angiogram (CTA) of Aorta exams performed in the Emergency Department (ED) over a 10-year period and their relationship to detection of aortic pathology.
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