  Radiologists face high burnout rates, partially due to the increasing volume
of Chest X-rays (CXRs) requiring interpretation and reporting. Automated CXR
report generation holds promise for reducing this burden and improving patient
care. While current models show potential, their diagnostic accuracy is
limited. Our proposed CXR report generator integrates elements of the
radiologist workflow and introduces a novel reward for reinforcement learning.
Our approach leverages longitudinal data from a patient's prior CXR study and
effectively handles cases where no prior study exist, thus mirroring the
radiologist's workflow. In contrast, existing models typically lack this
flexibility, often requiring prior studies for the model to function optimally.
Our approach also incorporates all CXRs from a patient's study and
distinguishes between report sections through section embeddings. Our reward
for reinforcement learning leverages CXR-BERT, which forces our model to learn
the clinical semantics of radiology reporting. We conduct experiments on
publicly available datasets -- MIMIC-CXR and Open-i IU X-ray -- with metrics
shown to more closely correlate with radiologists' assessment of reporting.
Results from our study demonstrate that the proposed model generates reports
that are more aligned with radiologists' reports than state-of-the-art models,
such as those utilising large language models, reinforcement learning, and
multi-task learning. The proposed model improves the diagnostic accuracy of CXR
report generation, which could one day reduce radiologists' workload and
enhance patient care. Our Hugging Face checkpoint
(https://huggingface.co/aehrc/cxrmate) and code
(https://github.com/aehrc/cxrmate) are publicly available.
