用多模态模型融合影像与病历，提升肺栓塞风险评估效率。
Efficient Multimodal Clinical Question Answering for Pulmonary Embolism Risk Assessment
- 构建包含23,248例的多模态数据集，设计8个临床问答任务。
- 在影像+病历输入下，Gemma4 E4B模型准确率显著提升。
- 诊断任务表现优于预后预测，适合早期风险筛查场景。
肺栓塞（PE）是高危心肺疾病，其管理需及时诊断与未来风险可靠评估。由于PE诊疗常结合计算机断层肺动脉造影（CTPA）、影像解读和纵向电子健康记录（EHR）证据，为此提供了评估紧凑型多模态大模型的临床场景。本文基于包含23,248例CTPA研究、来自19,402名患者的INSPECT多模态数据集，构建基准测试，采用高效多模态语言模型（MLLMs）在仅CTPA、仅EHR及两者结合三种设置下，通过零样本与少样本提示评估八项诊断与预后任务。结果显示，当引入EHR信息时，Gemma4 E4B与Gemma4 E2B表现更优，尤其在CTPA+EHR联合输入下。任务级分析表明，诊断任务性能高于预后任务，尤其是再入院预测。这说明紧凑型多模态模型在肺栓塞早期风险检测与解释中具有巨大潜力。
原文摘要 · Abstract (English)
Pulmonary embolism (PE) is a high risk cardiopulmonary condition whose management requires both timely diagnosis and reliable assessment of future clinical risk. Because PE care routinely combines computed tomography pulmonary angiography (CTPA), radiology interpretation, and longitudinal electronic health record (EHR) evidence, it provides a clinically meaningful setting for evaluating compact multimodal language models. In this work, we build a benchmark using efficient multimodal large language models (MLLMs) on INSPECT, a multimodal PE dataset containing 23,248 CTPA studies from 19,402 patients. We formulate eight diagnostic and prognostic tasks as structured clinical question answering problems and evaluate on typical efficient MLLMs under CTPA-Only, EHR-Only, and CTPA+EHR settings with zero-shot and few-shot prompting. Results show that Gemma4 E4B and Gemma4 E2B perform more strongly when EHR evidence is available, especially under CTPA+EHR input. Task level analysis further shows that PE diagnosis achieves higher performance than prognostic tasks, particularly readmission prediction. These observations suggest that compact multimodal models have the great potential in early stage PE risk detection and explanation.
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