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.

医疗影像科室面临日益增长的需求对放射科医生准时准确提交报告的压力。最近人工智能技术的进步已展示出自动生成放射学报告（ARRG）的巨大潜力，引发了大量研究。该调查论文通过方法学评审对当代 ARRG 方法进行了回顾，包括：评估基于特征（如可用性、大小和采用率）的数据集，研究深度学习训练方法（如对比学习和强化学习），探索先进的模型架构（包括 CNN 和 Transformer 模型的变种），通过多模态输入和知识图谱集成临床知识的技术，以及审查常用的 NLP 评估指标和定性临床审查的当前模型评估技术。此外，还分析了审查模型的定量结果，检查了表现最佳的模型以寻求进一步的见解。最后，强调了潜在的新方向，预测将采用其他放射学模态的附加数据集和改进的评估方法作为未来发展的重要领域。