Santomartino Samantha M, Siegel Eliot, Yi Paul H
University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD.
University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD.
Acad Radiol. 2023 May;30(5):971-974. doi: 10.1016/j.acra.2022.07.011. Epub 2022 Aug 11.
With a track record of innovation and unique access to digital data, radiologists are distinctly positioned to usher in a new medical era of artificial intelligence (AI).
In this Perspective piece, we summarize AI initiatives that academic radiology departments should consider related to the traditional pillars of education, research, and clinical excellence, while also introducing a new opportunity for engagement with industry.
We provide early successful examples of each as well as suggestions to guide departments towards future success.
Our goal is to assist academic radiology leaders in bringing their departments into the AI era and realizing its full potential in our field.
凭借创新的历史记录和对数字数据的独特获取途径，放射科医生处于引领人工智能（AI）新医学时代的独特位置。
在这篇观点文章中，我们总结了学术放射科应考虑的与教育、研究和临床卓越的传统支柱相关的人工智能举措，同时还介绍了与行业合作的新机会。
我们提供了每个方面的早期成功案例以及指导各部门未来取得成功的建议。
我们的目标是帮助学术放射科领导者将其科室带入人工智能时代，并在我们的领域实现其全部潜力。
