Prompt-Guided Generation of Structured Chest X-Ray Report Using a
  Pre-trained LLM
Hongzhao Li Hongyu Wang Xia Sun Hua He Jun Feng
Medical report generation automates radiology descriptions from images,
easing the burden on physicians and minimizing errors. However, current methods
lack structured outputs and physician interactivity for clear, clinically
relevant reports. Our method introduces a prompt-guided approach to generate
structured chest X-ray reports using a pre-trained large language model (LLM).
First, we identify anatomical regions in chest X-rays to generate focused
sentences that center on key visual elements, thereby establishing a structured
report foundation with anatomy-based sentences. We also convert the detected
anatomy into textual prompts conveying anatomical comprehension to the LLM.
Additionally, the clinical context prompts guide the LLM to emphasize
interactivity and clinical requirements. By integrating anatomy-focused
sentences and anatomy/clinical prompts, the pre-trained LLM can generate
structured chest X-ray reports tailored to prompted anatomical regions and
clinical contexts. We evaluate using language generation and clinical
effectiveness metrics, demonstrating strong performance.