MedQA-MM: Shortcuts Behind Medical Visual Reasoning

2026-09-02

AI Summary

本文通过多种方法分析并解决医疗多模态选择题中因非图像线索导致的推理膨胀问题，构建了减少捷径依赖的数据集MedQA-MM。

Abstract

A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model's hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63%, while text-only and options-only settings achieve 53.96% and 29.71%, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21% and 12.33%. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence.

Problem

• Medical Multimodal MCQs
• Reasoning Inflation
• Benchmark Score
• Visual Reasoning
• Shortcut Cues

Innovation

• Medical Visual Reasoning
• Benchmark Score
• Reasoning Inflation
• Route-Level Evidence
• Shortcut Mitigation