In clinical scenarios, radiology reports are crucial for radiologists in disease diagnosis, while writing reports manually is laborious and subjective. Existing radiology report generation (RRG) approaches primarily introduce external clinical knowledge to improve the generation quality of radiology reports, but they overlook the inherent ranking information among the injected knowledge elements. In this study, we propose a rank-aware framework named RankRRG, which incorporates a ranking loss to strengthen the model’s representation learning capability. It encourages the model to learn the relative ordering of semantically similar reports through similarity-based supervision, enabling the model to learn more discriminative and clinically meaningful features. Experiments conducted on the widely-used Chest X-ray benchmark MIMIC-CXR demonstrate that our proposed RankRRG achieves competitive performance compared with state-of-the-art methods, specifically surpassing the second top-tier with a margin of 0.7% BLEU-1, 1.2% precision, 1.9% recall, and 1.5% \(F_1\) score, highlighting the effectiveness of incorporating ranking information into RRG.