@inproceedings{yu-etal-2026-medframeqa,
title = "{M}ed{F}rame{QA}: A Multi-Image Medical {VQA} Benchmark for Clinical Reasoning",
author = "Yu, Suhao and
Wang, Haojin and
Wu, Juncheng and
Luo, Luyang and
Wang, Jingshen and
Xie, Cihang and
Rajpurkar, Pranav and
Yang, Carl and
Yang, Yang and
Wang, Kang and
Yu, Yannan and
Zhou, Yuyin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2482/",
pages = "56345--56378",
ISBN = "979-8-89176-459-0",
abstract = "Real-world clinical practice demands multi-image comparative reasoning, yet current medical benchmarks remain limited to single-frame interpretation. We present MedFrameQA, a video-derived, transcript-grounded benchmark for multi-image reasoning VQA, built from 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 13 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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<abstract>Real-world clinical practice demands multi-image comparative reasoning, yet current medical benchmarks remain limited to single-frame interpretation. We present MedFrameQA, a video-derived, transcript-grounded benchmark for multi-image reasoning VQA, built from 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 13 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.</abstract>
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%0 Conference Proceedings
%T MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning
%A Yu, Suhao
%A Wang, Haojin
%A Wu, Juncheng
%A Luo, Luyang
%A Wang, Jingshen
%A Xie, Cihang
%A Rajpurkar, Pranav
%A Yang, Carl
%A Yang, Yang
%A Wang, Kang
%A Yu, Yannan
%A Zhou, Yuyin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-medframeqa
%X Real-world clinical practice demands multi-image comparative reasoning, yet current medical benchmarks remain limited to single-frame interpretation. We present MedFrameQA, a video-derived, transcript-grounded benchmark for multi-image reasoning VQA, built from 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 13 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.
%U https://aclanthology.org/2026.emnlp-main.2482/
%P 56345-56378
Markdown (Informal)
[MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning](https://aclanthology.org/2026.emnlp-main.2482/) (Yu et al., EMNLP 2026)
ACL
- Suhao Yu, Haojin Wang, Juncheng Wu, Luyang Luo, Jingshen Wang, Cihang Xie, Pranav Rajpurkar, Carl Yang, Yang Yang, Kang Wang, Yannan Yu, and Yuyin Zhou. 2026. MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56345–56378, Budapest, Hungary. Association for Computational Linguistics.