The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence.