Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation

The current burnout rate of radiologists is high due to the large and ever growing number of Chest X-Rays (CXRs) needing interpretation and reporting. Promisingly, automatic CXR report generation has the potential to aid radiologists with this laborious task and improve patient care. Previous CXR report generation methods are limited by their diagnostic inaccuracy and their lack of alignment with the workflow of radiologists. To address these issues, we present a new method that utilises the longitudinal history available from a patient's previous CXR study when generating a report, which imitates a radiologist's workflow. We also propose a new reward for reinforcement learning based on CXR-BERT -- which captures the clinical semantic similarity between reports -- to further improve CXR report generation. We conduct experiments on the publicly available MIMIC-CXR dataset with metrics more closely correlated with radiologists' assessment of reporting. The results indicate capturing a patient's longitudinal history improves CXR report generation and that CXR-BERT is a promising alternative to the current state-of-the-art reward. Our approach generates radiology reports that are quantitatively more aligned with those of radiologists than previous methods while simultaneously offering a better pathway to clinical translation. Our Hugging Face checkpoint (https://huggingface.co/aehrc/cxrmate) and code (https://github.com/aehrc/cxrmate) are publicly available.