Medical report generation is the task of automatically writing radiology reports for chest X-ray images. Manually composing these reports is a time-consuming process that is also prone to human errors. Generating medical reports can therefore help reduce the burden on radiologists. In other words, we can promote greater clinical automation in the medical domain. In this work, we propose a new framework leveraging vision-enabled Large Language Models (LLM) for the task of medical report generation. We introduce a lightweight solution that achieves better or comparative performance as compared to previous solutions on the task of medical report generation. We conduct extensive experiments exploring different model sizes and enhancement approaches, such as prefix tuning to improve the text generation abilities of the LLMs. We evaluate our approach on a prominent large-scale radiology report dataset - MIMIC-CXR. Our results demonstrate the capability of our resource-efficient framework to generate patient-specific reports with strong medical contextual understanding and high precision.

本研究解决了手动编写放射学报告的时间消耗和人为错误问题，通过提出一个新的框架，利用视觉增强的大语言模型进行医疗报告生成。结果显示，该资源高效的框架在生成具有强医疗背景理解和高精度的患者特定报告方面，表现出色，推动了医疗领域的临床自动化。 

基于大语言模型的资源高效医疗报告生成