  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.
