Simpson Scott A, Cook Tessa S
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.
Tessa S. Cook, MD, PhD, Director, Center for Translational Imaging Informatics, Perelmen School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
J Am Coll Radiol. 2020 Nov;17(11):1388-1393. doi: 10.1016/j.jacr.2020.09.028. Epub 2020 Oct 1.
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.
尽管人工智能（AI）在放射学领域的炒作仍未减弱，但人工智能在未来放射学实践中的确切作用仍不明确。然而，针对未来放射科医生的教育正在进行，且需要考虑这项新技术的不确定性。甚至在成像人工智能最近出现之前，放射科住院医师培训就已经有所发展。然而，放射科住院医师和研究员未来可能会体验到人工智能辅助临床培训的好处。这将为他们提供定制化的学习体验，以及分析大量有关其住院医师培训进展数据的能力，所需的人工工作量比目前大幅减少。此外，他们需要学习如何在临床实践中与人工智能工具互动，更重要的是，要理解如何以批判性的方式评估人工智能的输出，将其作为有助于影像检查解读的另一项信息。尽管人工智能在未来放射学实践中的确切作用仍不明确，但它肯定会被纳入未来放射科医生的教育中。
