Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval

2026-08-26

AI Summary

研究通过构建Case2Flow任务和FlowAtlas数据集，利用多模态检索方法匹配患者案例与指南流程图，提出CRISP评分方法以提高检索准确性。

Abstract

Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guidelines covering plausible diseases and treatments. While guideline passages have supported end-to-end question answering, flowcharts remain largely underused in decision support despite their ability to encode actionable clinical pathways. We therefore introduce Case2Flow, a task designed to retrieve the most relevant guideline flowchart for a given patient case from a collection of guideline documents. To support it, we construct FlowAtlas, a curated corpus of 202 flowcharts extracted from 2,080 medical guidelines, together with a pipeline that synthesises 1,911 aligned case-flowchart pairs. Our evaluation of multimodal retrieval methods reveals systematic failure modes, including overreliance on keywords and spurious token-patch matches induced by uninformative background regions in flowcharts. Motivated by this, we propose CRISP, a training-free scoring method that sharpens late-interaction retrieval by suppressing uninformative patches, discounting ambiguous token matches, and incorporating bidirectional query-image alignment. CRISP improves Recall@1 by up to 18.71 percentage points, while a blinded physician assessment on published case narratives provides preliminary feasibility evidence beyond synthetic queries.

Problem

• Medical guidelines
• Decision support
• Flowcharts
• Patient cases

Innovation

• Case2Flow
• FlowAtlas
• multimodal retrieval
• CRISP
• bidirectional query-image alignment