Dept. of Biomedical Eng. Faculty of Engineering, Tel-Aviv University, Tel-Aviv, Israel.
Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Med Image Anal. 2020 Dec;66:101800. doi: 10.1016/j.media.2020.101800. Epub 2020 Aug 19.
In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.
在这份立场文件中，我们提供了一系列关于人工智能在 COVID-19 大流行中的作用的观点，从临床需求到基于人工智能系统的设计，再到开发工具在临床上的转化。我们强调了设计系统解决方案的关键因素——针对特定任务；以及在国家层面管理疾病方面的设计问题。我们专注于三个可以构建人工智能系统的特定用例：早期疾病检测、医院环境中的管理，以及构建需要将影像学与其他临床数据相结合的患者特异性预测模型。将描述两个欧洲国家的基础设施考虑因素和人口建模。这场大流行使人工智能解决方案的实际和科学挑战变得非常明确。本文以人工智能面临的挑战清单结束讨论。
