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English(EN) Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models

LLM在放射学报告准确性和纵向数据提取方面的评估 · 已追踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员正在探索使用大型语言模型（LLM）来提高放射学报告的质量和提取纵向信息。一项研究将领域特定的BERT模型与开源LLM（如Qwen3-32B、Gemma-3:27B和Llama-3.3-70B）进行了比较，用于检测PET/CT报告中的错误，发现紧凑的领域特定模型取得了更高的准确性。另一篇论文开发了一个基于LLM的管道，利用Qwen2.5-32B自动标注放射学报告中的纵向信息，为评估报告生成模型创建了一个标准化的基准。 AI

影响 LLM在提高放射学报告分析的准确性和效率方面显示出潜力，可能有助于临床决策。

排序理由 两篇arXiv论文介绍了将LLM应用于放射学报告分析的研究。

在 arXiv cs.AI 阅读 →

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LLM在放射学报告准确性和纵向数据提取方面的评估 · 已追踪2个来源

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两篇arXiv论文介绍了将LLM应用于放射学报告分析的研究。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hermione Warr, Harry Anthony, Lilli J Freischem, Yasin Ibrahim, Daniel R McGowan, Konstantinos Kamnitsas ·

    PET/CT放射学报告的错误检测：领域特定模型 vs 大型语言模型

    arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are often subtle and require domain expertise to detect. Although large language models …

  2. arXiv cs.AI TIER_1 Italiano(IT) · Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Xin Chen ·

    利用大型语言模型标注标准化纵向放射学报告评估

    arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is crucial for monitoring disease progression and guiding clinical decisions. Many r…

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