  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.
