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Radiology Extenders Outperform Radiology Residents with Chest X-ray Interpretations

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Radiology extenders who read chest X-rays save attending radiologists more time during the day than radiology residents do, potentially streamlining workflow and alleviating provider burnout. At least that has been the experience for researchers at the University of Pennsylvania. Radiologists in their department read more cases per hour when the drafts came from radiology extenders than from residents, resulting in nearly an hour – 51 minutes – of provider time saved each day. The authors shared their experience on Oct. 13 in the Journal of the American College of Radiology. "Interpreting these radiographs entails a disproportionate amount of work (eg., retrieving patient history, completing standard dictation templates, and ensuring proper communication of important findings before finalization of reports). Given low reimbursement rates for these studies, economic necessities push radiologists to provide faster interpretations, contributing to burnout," said the team led by Arijitt Borthakur, MBA, Ph.D., senior research investigator in the Perelman School of Medicine radiology department.


Postdoctoral and Research Fellow positions in Artificial Intelligence -- FCAI

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Finnish Center for Artificial Intelligence FCAI is searching for exceptional postdoctoral researchers and research fellows interested in tackling challenges in machine learning and in creating artificial intelligence that is data-efficient, trustworthy, and understandable. FCAI is a vibrant research center for Artificial Intelligence in Helsinki, bringing together the expertise in AI research from both academia and industry. It was initiated by Aalto University, University of Helsinki, and VTT Technical Research Centre of Finland and has a total budget of 250 M€ over the next 8 years. FCAI is built on the long tradition and track record of decades of pioneering machine learning research in Helsinki. It was recently selected to host one of the first ELLIS (European Laboratory for Learning and Intelligent Systems) units that assemble European top talent in machine learning.


Roche Postdoctoral Fellow (RPF): Machine-Learning (ML) applied to Crystal Structure Prediction (CSP) (November 2020 or upon availability, 2 years temporary)

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The duration of RPF project is initially set for two years, with the possibility of extension for a third year. You will be based in Basel, Switzerland with possible stays at the academic partners. The start date of this fellowship is within 2020 or on availability. Please clearly indicate your preferred starting date on your motivation letter. All applications need to include a CV, motivation letter, a publication list and, if available, your PhD.



Artificial Intelligence in Radiology: Summary of the AUR Academic Radiology and Industry Leaders Roundtable

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Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. The Association of University Radiologists (AUR9) in its role of organizing and representing the interests of academic radiologists and those of radiology at large, convened a roundtable to help radiologists and industry leaders share their points of view and their goals in order to foster a shared understanding about the impact and benefits of AI applications in the field of radiology. There is a clear mutual interdependence between the radiology community and industry partners, which, in the case of AI, should foster collaboration between the two groups. In order to advance radiological sciences and to bridge the gap between clinicians and engineers, members of both groups need to work together so as to ensure the development of common goals, shared understanding, and mutually productive efforts. This type of collaboration occurs most frequently at the local level between a single radiology academic department and a single manufacturer.