Indiana University School of Medicine, Indianapolis, IN, USA.
University of Texas Medical Branch, Galveston, TX, USA.
J Digit Imaging. 2023 Dec;36(6):2329-2334. doi: 10.1007/s10278-023-00892-z. Epub 2023 Aug 9.
The incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additionally, radiology systems should be designed to avoid automation bias and the potential decline in radiologist performance. Designed solutions should cater to every level of expertise so that patient care can be enhanced and risks reduced. Ultimately, the radiology community must provide education so that radiologists can learn about algorithms, their inputs and outputs, and potential ways they may fail. This manuscript will present suggestions on how to train radiologists to use these new digital systems, how to detect AI errors, and how to maintain underlying diagnostic competency when the algorithm fails.
人工智能融入放射科临床工作流程即将成为现实。为确保这些工具的有效性，必须采取措施对放射科医生进行工具性能和故障模式方面的教育。此外，放射科系统的设计应避免自动化偏差和放射科医生绩效的潜在下降。设计的解决方案应满足各级专业知识水平，以提高患者护理质量并降低风险。最终，放射科领域必须提供教育，使放射科医生能够了解算法、其输入和输出以及可能的故障方式。本文将提出如何培训放射科医生使用这些新的数字系统的建议，如何检测人工智能错误以及在算法失败时如何保持基本诊断能力。
