通过放射报告的非专业总结提升健康素养：BioNER与检索增强生成的评估

中文摘要 AI 辅助

放射报告主要面向临床医生撰写，其专业术语常使患者难以理解。因此，尽管存在有据可查的事实不准确和幻觉风险，许多患者仍求助于公开可用的大语言模型（LLM）来解释报告。自动非专业摘要生成已成为一种有前景的替代方案，但针对放射学特定场景，检索增强型和临床知情方法的有效性仍未得到充分探索。本研究调查了检索增强生成（RAG）和命名实体识别（NER）在多大程度上能提升自动生成的非专业摘要的质量、事实一致性和可读性，相较于标准的基于LLM的生成。我们开发了一个框架，结合基于NER的临床相关发现提取与用于语境 grounding 的RAG机制，在两个模型（Qwen、BioBART）的少样本和微调变体上进行评估。结果显示，NER持续提升可读性和整体质量，而单独使用RAG无益处，还可能引入不相关检索术语带来的幻觉；在少样本设置中，RAG与NER结合会降低性能，但微调时能提升可读性；微调后的BioBART搭配NER实现了最佳整体性能，凸显实体感知提取是提升患者友好型摘要的主要驱动因素。

英文摘要

Radiology reports are written primarily for clinicians, and their specialized terminology often makes them difficult for patients to interpret. As a result, many patients turn to publicly available Large Language Models (LLMs) to help explain their reports, despite well-documented risks of factual inaccuracies and hallucinations. Automated lay-summary generation has emerged as a promising alternative, yet the effectiveness of retrieval-enhanced and clinically informed approaches for radiology-specific communication remains underexplored. This study investigates the extent to which Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) improve the quality, factual consistency, and readability of automatically generated lay summaries compared with standard LLM-based generation. We develop a framework combining NER-based extraction of clinically relevant findings with a RAG mechanism for contextual grounding, evaluated across few-shot and fine-tuned variants of two models (Qwen, BioBART). Results show that NER consistently improves readability and overall quality, while RAG alone offers no benefit and can introduce hallucinations from irrelevant retrieved terms. Combining RAG with NER degrades performance in few-shot settings but improves readability when fine-tuned. Fine-tuned BioBART with NER achieves the best overall performance, highlighting entity-aware extraction as the primary driver of improved patient-friendly summaries.

发表机构

• Özyeğin University（厄兹耶金大学）
• Galatasaray University（加拉塔萨雷大学）

机构由 AI 辅助整理，请以论文原文为准。