让放射科报告生成更懂临床背景，避免虚构病史。
Automated Structured Radiology Report Generation with Rich Clinical Context
- 引入多视角胸片、检查指征等临床信息提升生成质量
- 相比传统方法，报告准确性显著提高，减少虚构病史问题
- 适合医疗AI研究者和临床系统开发者参考
从胸部X光图像自动生成结构化放射科报告（SRRG）具有减轻放射科医生工作负担的潜力，能生成清晰、一致且符合临床标准的报告。然而，现有SRRG系统忽视了放射科医生诊断时依赖的关键临床背景，导致时间上的幻觉问题（如引用不存在的既往检查）。为此，我们提出上下文感知的结构化报告生成（C-SRRG），全面融合丰富临床上下文：1）多视角X光图像，2）临床指征，3）成像技术，4）基于患者病史的既往研究及对比信息。我们构建了C-SRRG数据集，并在多个先进多模态大模型上进行广泛评测，结果表明，引入临床上下文可显著提升报告生成质量。相关数据集、代码与模型权重已开源，供未来临床对齐的自动化报告生成研究使用。
原文摘要 · Abstract (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.
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