The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.

中文翻译：

---

大数据，人工智能和结构化报告。

在过去的几年中，人们对放射学中的人工智能和机器学习应用的兴趣大大增加。但是，为了使这样的系统充分执行，需要大量的训练数据。理想地，这些数据应被标准化并具有足够的质量，以允许在人工智能算法的训练中进一步使用。不幸的是，在当前的许多临床和放射学信息技术生态系统中，很难获得相关信息。这主要是因为大部分信息是作为叙述文本的集合来处理的，并且仍然缺乏互操作性。这篇综述旨在简要概述结构化报告如何帮助推动人工智能和大数据背景下的研究。