Improving Health Literacy through Lay Summarization of Radiological Reports: An Evaluation of BioNER and Retrieval-Augmented Generation

  • 类型:arxiv
  • 标识:2609.02396
  • 链接:http://arxiv.org/abs/2609.02396v1
  • 主分类:rag
  • 形态:benchmark
  • TLDR: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-Au
  • 副分类:evaluation
  • 待LLM分类:否
  • 来源文件
  • /inbox/tom/_candidates/2026-09-03-agent-rag-longcontext-candidates.json