自动化结构化放射报告生成与丰富临床上下文

摘要

基于胸部X光图像的自动化结构化放射报告生成（SRRG）具有显著潜力，能够通过生成符合临床报告标准、确保清晰度和一致性的结构化报告，减轻放射科医生的工作负担。尽管放射科医生在诊断推理中有效利用了可用的临床背景，但现有的SRRG系统却忽视了这些关键要素。这一根本性差距导致了诸如在引用不存在的临床背景时出现时间幻觉等严重问题。为解决这些局限，我们提出了情境化SRRG（C-SRRG），全面整合丰富的临床背景以支持SRRG。我们通过整合涵盖1）多视角X光图像、2）临床指征、3）成像技术及4）基于患者病史的既往研究与相应比较的全面临床背景，精心构建了C-SRRG数据集。通过对最先进的多模态大语言模型进行广泛基准测试，我们证明了结合所提出的C-SRRG融入临床背景能显著提升报告生成质量。我们公开了数据集、代码及检查点，以促进未来面向临床对齐的自动化RRG研究，访问地址为https://github.com/vuno/contextualized-srrg。

English

Automated structured radiology report generation (SRRG) from chest X-ray images offers significant potential to reduce workload of radiologists by generating reports in structured formats that ensure clarity, consistency, and adherence to clinical reporting standards. While radiologists effectively utilize available clinical contexts in their diagnostic reasoning, existing SRRG systems overlook these essential elements. This fundamental gap leads to critical problems including temporal hallucinations when referencing non-existent clinical contexts. To address these limitations, we propose contextualized SRRG (C-SRRG) that comprehensively incorporates rich clinical context for SRRG. We curate C-SRRG dataset by integrating comprehensive clinical context encompassing 1) multi-view X-ray images, 2) clinical indication, 3) imaging techniques, and 4) prior studies with corresponding comparisons based on patient histories. Through extensive benchmarking with state-of-the-art multimodal large language models, we demonstrate that incorporating clinical context with the proposed C-SRRG significantly improves report generation quality. We publicly release dataset, code, and checkpoints to facilitate future research for clinically-aligned automated RRG at https://github.com/vuno/contextualized-srrg.

自动化结构化放射报告生成与丰富临床上下文

Automated Structured Radiology Report Generation with Rich Clinical Context

摘要

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