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arXiv · 2505.16964

MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning

Suhao Yu·Haojin Wang·Juncheng Wu·Luyang Luo·Jingshen Wang·Cihang Xie·Pranav Rajpurkar·Carl Yang·Yang Yang·Kang Wang·Yannan Yu·Yuyin Zhou
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Abstract

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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Suhao Yu, Haojin Wang, Juncheng Wu, Luyang Luo, Jingshen Wang, Cihang Xie, Pranav Rajpurkar, Carl Yang, Yang Yang, Kang Wang, Yannan Yu, Yuyin Zhou. 2025-05-22. MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning. https://arxiv.org/abs/2505.16964

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