MedFrameQA: 一个多图像医学视觉问答基准用于临床推理
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning
AI总结：
MedFrameQA是一个多图像医学视觉问答基准，旨在评估多图像医学推理能力，揭示现有模型在处理复杂医学叙述时的不足。
摘要：
现实世界的临床实践需要多图像比较推理，但当前的医学基准仍局限于单帧解释。我们提出了MedFrameQA，这是首个专门设计用于通过教育验证的诊断序列测试多图像医学视觉问答的基准。为了构建这个数据集，我们开发了一个可扩展的管道，利用医学教育视频的叙述转录文本来对齐视觉帧与文本概念，自动产生2,851对高质量的多图像视觉问答对，具有明确的转录基础推理链。我们对11个先进的MLLMs（包括推理模型）的评估揭示了多图像合成中的严重缺陷，其中准确率大多低于50%，并且在不同图像数量下表现不稳定。错误分析表明，模型往往将图像视为孤立实例，无法跟踪病理进展或跨参考解剖变化。MedFrameQA为评估下一代MLLMs处理复杂、时间相关的医学叙述提供了严格的标准。
英文摘要：
Real-world clinical practice demands multi-image comparative reasoning, yet current medical benchmarks remain limited to single-frame interpretation. We present MedFrameQA, the first benchmark explicitly designed to test multi-image medical VQA through educationally-validated diagnostic sequences. To construct this dataset, we develop a scalable pipeline that leverages narrative transcripts from medical education videos to align visual frames with textual concepts, automatically producing 2,851 high-quality multi-image VQA pairs with explicit, transcript-grounded reasoning chains. Our evaluation of 11 advanced MLLMs (including reasoning models) exposes severe deficiencies in multi-image synthesis, where accuracies mostly fall below 50% and exhibit instability across varying image counts. Error analysis demonstrates that models often treat images as isolated instances, failing to track pathological progression or cross-reference anatomical shifts. MedFrameQA provides a rigorous standard for evaluating the next generation of MLLMs in handling complex, temporally grounded medical narratives.
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