UMass Memorial Medical Center - UMass Chan Medical School, 55 Lake Avenue North, Worcester, MA, USA.
Emerg Radiol. 2023 Oct;30(5):647-657. doi: 10.1007/s10140-023-02154-5. Epub 2023 Jul 8.
Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.
人工智能工具在放射科的应用已经迅猛发展,针对特定发现的模块变得越来越普遍,并在日常急诊放射科实践中证明是有价值的。美国食品和药物管理局批准的与放射学相关的算法数量已经从 2017 年初的仅仅 10 个飙升到现在的 200 多个。本综述将集中讨论人工智能工具在临床急诊放射科环境中的当前应用,包括对该技术的局限性的简要讨论。作为放射科医生,我们必须接受这项技术,了解它的局限性,并利用它来改善患者的护理。