Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
J Am Coll Radiol. 2019 Oct;16(10):1464-1470. doi: 10.1016/j.jacr.2019.06.009. Epub 2019 Jul 15.
Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org).
人工智能（AI）将在未来几年重塑放射学。放射学界有着积极采用新技术进行变革的悠久历史，而人工智能也不例外。与任何新技术一样，快速、成功的实施都面临着一些挑战，这将需要创造和采用新的集成技术。本文描述了对 AI 实际应用很重要的用例，包括临床注册、AI 研究、AI 产品验证和放射科报告的计算机辅助。此外，还描述了成功实施用例所需的信息学技术，包括开放的计算机辅助放射科医生决策支持、ACR Assist、ACR 数据科学研究所用例、通用数据元素（radelement.org）、RadLex（radlex.org）、LOINC/RSNA RadLex 操作手册（loinc.org）和放射科报告模板（radreport.org）。
