Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention. This presents a more practical and challenging application and moves towards bridging visual medical features and radiologist text. So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks (RNN) for natural language processing (NLP) and natural language generation (NLG). This is an area of research that we anticipate will grow in the near future. We focus our investigation on the following critical challenges: understanding radiology text/image structures and datasets, applying DL algorithms (mainly CNN and RNN), generating radiology text, and improving existing DL based models and evaluation metrics. Lastly, we include a critical discussion and future research recommendations. This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.

基于深度学习 (DL) 的自动化放射学报告模型的实施已经取得了实质性进展。这是由于大型医学文本/图像数据集的引入。生成比传统医学图像注释或基于单句的描述更多的放射学连贯段落一直是最近学术界关注的主题。这提供了更实用和更具挑战性的应用，并朝着弥合视觉医学特征和放射科医生文本的方向发展。到目前为止，最常见的方法是利用公开数据集并开发深度学习模型，将用于图像分析的卷积神经网络 (CNN) 与用于自然语言处理 (NLP) 和自然语言生成 (NLG) 的循环神经网络 (RNN) 集成在一起。我们预计这一研究领域将在不久的将来发展。我们的研究重点关注以下关键挑战：理解放射学文本/图像结构和数据集、应用深度学习算法（主要是 CNN 和 RNN）、生成放射学文本以及改进现有的基于深度学习的模型和评估指标。最后，我们进行了批判性讨论和未来的研究建议。这项调查对于对深度学习感兴趣的研究人员非常有用，特别是那些对将深度学习应用于放射学报告感兴趣的人。