A reinforcement learning framework enables precise control over clinical precision and recall in radiology report generation. By integrating a clinical reward and group-relative training, the model improves clinical efficacy beyond language fluency metrics, outperforming state-of-the-art methods on the MIMIC-CXR dataset.