Summary of Prompt-guided Generation Of Structured Chest X-ray Report Using a Pre-trained Llm, by Hongzhao Li et al.
Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM
by Hongzhao Li, Hongyu Wang, Xia Sun, Hua He, Jun Feng
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medical report generation in radiology automates descriptions from images, easing physicians’ burden 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). The LLM is trained on anatomy-based sentences that center on key visual elements in chest X-rays, establishing a structured report foundation. We also convert detected anatomy into textual prompts conveying anatomical comprehension to the LLM. 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 generates structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. Strong performance is demonstrated using language generation and clinical effectiveness metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical report generation in radiology helps doctors by automating image descriptions. Current methods don’t give doctors what they need: clear, helpful reports that are easy to understand. Our method uses a special kind of computer model to generate these reports. We teach the model to focus on important parts of chest X-ray images and use specific language to describe them. Then, we add clinical information to make sure the report is helpful for doctors. This helps doctors get accurate and helpful reports more easily. |
Keywords
» Artificial intelligence » Large language model » Prompt
