Artificial Intelligence and the Trainee Experience in Radiology |
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| Institution: | 1. Assistant Professor, Clinical Radiology. Department of Radiology, Penn Presbyterian Medical Center; Associate Program Director, Radiology Residency, Hospital of the University of Pennsylvania, Department of Radiology; Director of Radiology Medical Student Education, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;2. Tessa S. Cook, MD, PhD, Director, Center for Translational Imaging Informatics, Perelmen School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;1. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts;2. MGH & BWH Center for Clinical Data Science, Boston, Massachusetts;3. National Jewish Health, Denver, Colorado;4. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts;1. Chief of Imaging Informatics, MedStar Georgetown University Hospital, Washington, DC;2. Associate Chair of Translational Informatics and Director of the Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, San Francisco, California;3. Associate Chair of Clinical Informatics for University of California San Francisco Health and the Associate Chair of Clinical Informatics, Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, San Francisco, California;1. Department of Radiology, Boston University Medical Center, Boston, Massachusetts;2. Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts;3. Imaging Institute, Cleveland Clinic, Cleveland, Ohio;4. Professor, Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin |
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| Abstract: | The hype around artificial intelligence (AI) in radiology continues unabated, despite the fact that the exact role AI will play in future radiology practice remains undefined. Nevertheless, education of the radiologists of the future is ongoing and needs to account for the uncertainty of this new technology. Radiology residency training has evolved even before the recent advent of imaging AI. Yet radiology residents and fellows will likely one day experience the benefits of an AI-enabled clinical training. This will offer them a customized learning experience and the ability to analyze large quantities of data about their progress in residency, with substantially less manual effort than is currently required. Additionally, they will need to learn how to interact with AI tools in clinical practice and, more importantly, understand how to evaluate AI outputs in a critical fashion as yet another piece of information contributing to the interpretation of an imaging examination. Although the exact role AI will play in the future practice of radiology remains undefined, it will surely be integrated into the education of future radiologists. |
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| Keywords: | Artificial intelligence radiology education radiology residency |
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