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.
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.
For the last several years, artificial intelligence (AI) has represented the newest, most rapidly expanding frontier of radiology technology. Expo floors at all the major professional society meetings are full of vendors showcasing AI tools they have developed or integrated into their products, billed as efficiency and time-savings aids to help ease the workload of radiologists who are increasingly bogged down by vast amounts of data. Despite the promises and potential, however, widespread clinical implementation of AI in radiology has yet to occur. Early adopters are providing potential pathways for adoption, and vendors and clinicians continue to work together to ensure AI is actually doing what radiologists need it to do. According to numerous key opinion leaders in the fields of radiology and AI, there are a few main obstacles AI currently faces to widespread adoption.
The team's results far exceeded this goal. In fact, a single core of the Intel Xeon processor E5-2650 v4 demonstrated throughput nearly 150% greater than GE's overall performance target. Powering the solution with four of the processor's cores exceeded GE's throughput goal by almost 6x. We expect that the new Intel Xeon Platinum 8180 processor, which deliver up to 2.4x higher performance for a range of AI workloads compared to the previous generation, would yield even greater throughput.
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