Chest X-Ray (CXR) report generation is a promising approach to improving the efficiency of CXR interpretation. However, a significant increase in diagnostic accuracy is required before that can be realised. Motivated by this, we propose a framework that is more inline with a radiologist's workflow by considering longitudinal data. Here, the decoder is additionally conditioned on the report from the subject's previous imaging study via a prompt. We also propose a new reward for reinforcement learning based on CXR-BERT, which computes the similarity between reports. We conduct experiments on the MIMIC-CXR dataset. The results indicate that longitudinal data improves CXR report generation. CXR-BERT is also shown to be a promising alternative to the current state-of-the-art reward based on RadGraph. This investigation indicates that longitudinal CXR report generation can offer a substantial increase in diagnostic accuracy. Our Hugging Face model is available at: https://huggingface.co/aehrc/cxrmate and code is available at: https://github.com/aehrc/cxrmate.

翻译：胸部X光（CXR）报告生成是提升CXR解读效率的一种有前景的方法。然而，在实现这一目标之前，必须显著提高其诊断准确性。受此启发，我们提出了一种更贴合放射科医生工作流程的框架，该框架通过考虑纵向数据，利用患者既往影像学检查的报告作为提示对解码器进行额外条件约束。我们还提出了一种基于CXR-BERT的强化学习新奖励机制，用于计算报告之间的语义相似度。我们在MIMIC-CXR数据集上进行了实验，结果表明纵向数据能够改善CXR报告生成质量。同时，CXR-BERT被证明是当前基于RadGraph的最先进奖励机制的一种有前景的替代方案。本研究表明，纵向CXR报告生成可以显著提升诊断准确性。我们的Hugging Face模型可在https://huggingface.co/aehrc/cxrmate获取，代码已开源至https://github.com/aehrc/cxrmate。