Department of Radiology, New York University Robert I Grossman School of Medicine, New York, NY, USA.
Charité - Universitätsmedizin Berlin, Humboldt-Universität and Freie Universität zu Berlin, Berlin, Germany.
Eur Radiol. 2020 Jun;30(6):3576-3584. doi: 10.1007/s00330-020-06672-5. Epub 2020 Feb 17.
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. KEY POINTS: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
人工智能（AI）有可能在不久的将来极大地改变放射科的工作方式，但在 AI 能够广泛应用于日常实践之前，需要解决几个问题。这些问题包括不同利益相关者在成像 AI 开发中的作用、AI 在医疗保健中的伦理开发和使用、每个开发的 AI 算法的适当验证、有效的数据共享机制的开发、AI 算法的监管障碍以及针对实践放射科医生和放射科住院医师的 AI 教育资源的开发。本文详细介绍了这些问题，并根据 2019 年国际战略放射学研究学会会议的讨论提出了可能的解决方案。
放射科医生应该了解 AI 研究中常见的不同类型的偏差，并了解它们可能的影响。
需要开发有效的数据共享方法，以训练、验证和测试 AI 算法。
所有放射科医生都必须了解 AI 的基本原则、潜力和局限性。
