Medical report generation (MRG) aims to automatically generate reports from medical images, reducing the workload on radiologists. Research in this field is progressing rapidly with large pretrained vision-language models (VLMs), but most are trained on general image-text data and fail to capture critical medical findings. Effective chest X-ray (CXR) report generation requires fine-tuning on high-quality datasets, but inconsistent reporting styles remain a key challenge. The structured radiology report generation (SRRG) approach addresses this by using large language models (LLMs) to standardize and generate consistent structured reports. In this study, we introduce SRRG-benchmark to systematically evaluate state-of-the-art LLMs for converting free-text CXR reports into structured formsuitable for training VLMs. We primarily focus on assessing the medical image interpretation capabilities of VLMs across both structured and conventional free-text report generation tasks. Our results demonstrate that structured reporting improves VLMs’ medical image interpretation performance compared to free-text fine-tuning, increasing MedGemma’s clinical accuracy (GREEN) from 0.50 to 0.53 and RadGraph F1 from 0.27 to 0.38, with similar gains for Qwen3-VL.