Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph -- a graph representation of reports -- together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.

翻译：自动生成的医学图像报告有望改善放射科医生的工作流程。现有方法将图像到报告建模任务视为直接从图像生成完整报告，但这混淆了报告的内容（如发现及其属性）与风格（如格式和措辞），可能导致临床不准确的报告。为解决这一问题，我们提出了一种两步法生成放射学报告：首先从图像中提取内容；然后将提取的内容转化为匹配特定放射科医生风格的报告。为此，我们利用了报告图表示方法RadGraph与大型语言模型（LLMs）。定量评估表明，我们的方法表现出有益性能。临床评估者的人工评估强调，尽管仅利用少量示例作为上下文，AI生成的报告也能与个体放射科医生的风格无差异地定制。