  Radiology report generation (RRG) has attracted significant attention due to
its potential to reduce the workload of radiologists. Current RRG approaches
are still unsatisfactory against clinical standards. This paper introduces a
novel RRG method, \textbf{LM-RRG}, that integrates large models (LMs) with
clinical quality reinforcement learning to generate accurate and comprehensive
chest X-ray radiology reports. Our method first designs a large language model
driven feature extractor to analyze and interpret different regions of the
chest X-ray image, emphasizing specific regions with medical significance.
Next, based on the large model's decoder, we develop a multimodal report
generator that leverages multimodal prompts from visual features and textual
instruction to produce the radiology report in an auto-regressive way. Finally,
to better reflect the clinical significant and insignificant errors that
radiologists would normally assign in the report, we introduce a novel clinical
quality reinforcement learning strategy. It utilizes the radiology report
clinical quality (RadCliQ) metric as a reward function in the learning process.
Extensive experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the
superiority of our method over the state of the art.
