用轻量级大模型高效生成精准医学报告，减轻医生负担。
Resource-Efficient Medical Report Generation using Large Language Models
- 基于视觉增强大模型，采用前缀调优提升生成能力
- 在MIMIC-CXR数据集上达到媲美甚至更优的生成效果
- 适合医疗自动化场景，尤其资源受限的临床部署
医学报告生成旨在为胸部X光片自动生成放射科报告。人工撰写报告耗时且易出错，自动化生成可显著降低放射科医生工作负担，推动医疗领域智能化。本文提出一种基于视觉增强大型语言模型（LLM）的新框架，设计轻量级解决方案，在医学报告生成任务中表现优于或相当现有方法。通过大量实验探究不同模型规模及增强策略（如前缀调优），在主流大规模放射科报告数据集MIMIC-CXR上验证了该框架生成具有强医学上下文理解与高精度患者特异性报告的能力。
原文摘要 · Abstract (English)
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.
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