@inproceedings{tang-etal-2026-medisketch,
title = "{M}edi{S}ketch: Test-Time Scaling for Multimodal Patient Education",
author = "Tang, Zhipeng and
Wang, Benlu and
Zhang, Zhenyu and
Liu, Wuao and
Xiang, Pengda and
Huang, Juncheng and
Ong, Chin Siang and
yu, Hong and
Yao, Zonghai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2280/",
pages = "48808--48859",
ISBN = "979-8-89176-462-0",
abstract = "Patient education is a major NLP application, but current evaluation largely focuses on text-only assistance. In radiology, this overlooks an important part of clinical communication: patients often need both a plain-language explanation and a visual aid showing where to look and what it refers to. We introduce MediSketch, a multimodal patient-education task in which an agent must produce an image{--}text bundle: an educational image and a grounded plain-language explanation. We instantiate this setting with a clinician-curated radiology testbed, a structured multimodal rubric, and a rubric-following judge that evaluates bundle quality rather than text-only correctness. Under a matched image-generation budget, we compare three test-time scaling strategies: parallel sampling, sequential revision, and hybrid search. Across five backbone models, the no-scaling baseline remains weak, while naive parallel search often provides little gain despite using the full budget. Hybrid performs best on 4 of 5 backbones, suggesting that test-time compute works best when exploration and revision are combined. Further analysis identifies weak multimodal self-critique as a major bottleneck: many models stop revising too early and overestimate the quality of their own image{--}text outputs. MediSketch exposes both an evaluation gap in multimodal patient education and a concrete failure mode in test-time scaling."
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<abstract>Patient education is a major NLP application, but current evaluation largely focuses on text-only assistance. In radiology, this overlooks an important part of clinical communication: patients often need both a plain-language explanation and a visual aid showing where to look and what it refers to. We introduce MediSketch, a multimodal patient-education task in which an agent must produce an image–text bundle: an educational image and a grounded plain-language explanation. We instantiate this setting with a clinician-curated radiology testbed, a structured multimodal rubric, and a rubric-following judge that evaluates bundle quality rather than text-only correctness. Under a matched image-generation budget, we compare three test-time scaling strategies: parallel sampling, sequential revision, and hybrid search. Across five backbone models, the no-scaling baseline remains weak, while naive parallel search often provides little gain despite using the full budget. Hybrid performs best on 4 of 5 backbones, suggesting that test-time compute works best when exploration and revision are combined. Further analysis identifies weak multimodal self-critique as a major bottleneck: many models stop revising too early and overestimate the quality of their own image–text outputs. MediSketch exposes both an evaluation gap in multimodal patient education and a concrete failure mode in test-time scaling.</abstract>
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%0 Conference Proceedings
%T MediSketch: Test-Time Scaling for Multimodal Patient Education
%A Tang, Zhipeng
%A Wang, Benlu
%A Zhang, Zhenyu
%A Liu, Wuao
%A Xiang, Pengda
%A Huang, Juncheng
%A Ong, Chin Siang
%A yu, Hong
%A Yao, Zonghai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tang-etal-2026-medisketch
%X Patient education is a major NLP application, but current evaluation largely focuses on text-only assistance. In radiology, this overlooks an important part of clinical communication: patients often need both a plain-language explanation and a visual aid showing where to look and what it refers to. We introduce MediSketch, a multimodal patient-education task in which an agent must produce an image–text bundle: an educational image and a grounded plain-language explanation. We instantiate this setting with a clinician-curated radiology testbed, a structured multimodal rubric, and a rubric-following judge that evaluates bundle quality rather than text-only correctness. Under a matched image-generation budget, we compare three test-time scaling strategies: parallel sampling, sequential revision, and hybrid search. Across five backbone models, the no-scaling baseline remains weak, while naive parallel search often provides little gain despite using the full budget. Hybrid performs best on 4 of 5 backbones, suggesting that test-time compute works best when exploration and revision are combined. Further analysis identifies weak multimodal self-critique as a major bottleneck: many models stop revising too early and overestimate the quality of their own image–text outputs. MediSketch exposes both an evaluation gap in multimodal patient education and a concrete failure mode in test-time scaling.
%U https://aclanthology.org/2026.findings-emnlp.2280/
%P 48808-48859
Markdown (Informal)
[MediSketch: Test-Time Scaling for Multimodal Patient Education](https://aclanthology.org/2026.findings-emnlp.2280/) (Tang et al., Findings 2026)
ACL
- Zhipeng Tang, Benlu Wang, Zhenyu Zhang, Wuao Liu, Pengda Xiang, Juncheng Huang, Chin Siang Ong, Hong yu, and Zonghai Yao. 2026. MediSketch: Test-Time Scaling for Multimodal Patient Education. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48808–48859, Budapest, Hungary. Association for Computational Linguistics.