用大模型自动生成个性化放射科报告印象，减轻医生负担。
Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports
- 分步生成：先粗后精，结合机器学习与人类反馈强化
- 在芝加哥大学医学中心数据集上微调模型，确保准确性和风格匹配
- 适合希望提升报告效率的放射科医生和医疗AI团队
放射科报告中'印象'部分的手动撰写是导致放射科医生职业倦怠的主要原因。为解决此问题，我们提出一种粗到精框架，利用开源大语言模型（LLMs）从临床发现自动生成并个性化印象。系统首先生成初步印象，再通过机器学习与基于人类反馈的强化学习（RLHF）进行优化，使其符合个体放射科医生的写作风格，同时保证事实准确性。我们在芝加哥大学医学中心的大规模报告数据集上对LLaMA和Mistral模型进行微调。该方法旨在显著降低行政工作量，提升报告效率，同时维持高水平的临床精确性。
原文摘要 · Abstract (English)
The manual creation of the "Impression" section in radiology reports is a primary driver of radiologist burnout. To address this challenge, we propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings. The system first produces a draft impression and then refines it using machine learning and reinforcement learning from human feedback (RLHF) to align with individual radiologists' styles while ensuring factual accuracy. We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine. Our approach is designed to significantly reduce administrative workload and improve reporting efficiency while maintaining high standards of clinical precision.
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