用于肺栓塞风险评估的高效多模态临床问答
Efficient Multimodal Clinical Question Answering for Pulmonary Embolism Risk Assessment
AI总结：
本研究构建了基于INSPECT数据集的肺栓塞多模态基准，通过结构化问答任务评估高效多模态大模型在CTPA和EHR输入下的诊断与预后性能，发现Gemma4模型在结合CTPA+EHR时表现更优，且诊断任务优于预后任务。
摘要：
肺栓塞（PE）是一种高风险的心肺疾病，其管理需要及时诊断和可靠的未来临床风险评估。由于PE护理通常结合计算机断层扫描肺动脉造影（CTPA）、放射学解读和纵向电子健康记录（EHR）证据，它为评估紧凑型多模态语言模型提供了临床上有意义的场景。在这项工作中，我们在INSPECT（一个包含来自19,402名患者的23,248项CTPA研究的多模态PE数据集）上，使用高效多模态大语言模型（MLLMs）构建了一个基准。我们将八个诊断和预后任务构建为结构化临床问答问题，并在仅CTPA、仅EHR和CTPA+EHR设置下，使用零样本和少样本提示对典型的高效MLLMs进行评估。结果表明，当EHR证据可用时，Gemma4 E4B和Gemma4 E2B表现更强，尤其是在CTPA+EHR输入下。任务级分析进一步表明，PE诊断的性能高于预后任务，特别是再入院预测。这些观察表明，紧凑型多模态模型在早期PE风险检测和解释方面具有巨大潜力。
英文摘要：
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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