Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other radiological modalities and improved evaluation methods predicted as important areas of future development.

翻译：医学影像部门日益增长的工作量正在影响放射科医生及时、准确出具报告的能力。人工智能技术的最新进展已展现出自动放射学报告生成（ARRG）的巨大潜力，从而引发了研究热潮。本综述论文对当代ARRG方法进行了方法学评述，具体包括：（i）基于可用性、规模和采用率等特征评估数据集；（ii）考察对比学习与强化学习等深度学习训练方法；（iii）探索包含CNN与Transformer模型变体在内的前沿模型架构；（iv）概述通过多模态输入与知识图谱整合临床知识的技术；（v）审视当前模型评估技术（包括常用自然语言处理指标与定性临床评审）。此外，本文分析了所评述模型的量化结果，并对表现最优的模型进行深入探究以获取进一步洞见。最后，本文强调了潜在的新方向，其中采用其他放射学模态的附加数据集及改进评估方法预计将成为未来发展的重点领域。