@inproceedings{yim-etal-2025-overview,
title = "Overview of the {MEDIQA}-{WV} 2025 Shared Task on Woundcare Visual Question Answering",
author = "Yim, Wen-wai and
Ben Abacha, Asma and
Yetisgen, Meliha and
Xia, Fei",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
month = oct,
year = "2025",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clinicalnlp-1.3/",
pages = "17--21",
abstract = "Electronic messaging through patient portals facilitates remote care, connecting patients with doctors through asynchronous communication. While convenient, this new modality places an additional burden on physicians, requiring them to provide remote care as well as to see patients in clinic. Technology that can automatically draft responses for physician review is a promising way to improve clinical efficiency. Here, building on the 2024 MEDIQA Multilingual Multi-modal Medical Answer Generation (MEDIQA-M3G) challenge on dermatology, we present the 2025 MEDIQA Woundcare Visual Question Answering (MEDIQA-WV) shared task focusing on generating clinical responses to patient text and image queries. Three teams participated and submitted a total of fourteen systems. In this paper, we describe the task, datasets, as well as the participating systems and their results. We hope that this work can inspire future research on wound care visual question answering."
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%0 Conference Proceedings
%T Overview of the MEDIQA-WV 2025 Shared Task on Woundcare Visual Question Answering
%A Yim, Wen-wai
%A Ben Abacha, Asma
%A Yetisgen, Meliha
%A Xia, Fei
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 7th Clinical Natural Language Processing Workshop
%D 2025
%8 October
%I Association for Computational Linguistics
%C Virtual
%F yim-etal-2025-overview
%X Electronic messaging through patient portals facilitates remote care, connecting patients with doctors through asynchronous communication. While convenient, this new modality places an additional burden on physicians, requiring them to provide remote care as well as to see patients in clinic. Technology that can automatically draft responses for physician review is a promising way to improve clinical efficiency. Here, building on the 2024 MEDIQA Multilingual Multi-modal Medical Answer Generation (MEDIQA-M3G) challenge on dermatology, we present the 2025 MEDIQA Woundcare Visual Question Answering (MEDIQA-WV) shared task focusing on generating clinical responses to patient text and image queries. Three teams participated and submitted a total of fourteen systems. In this paper, we describe the task, datasets, as well as the participating systems and their results. We hope that this work can inspire future research on wound care visual question answering.
%U https://aclanthology.org/2025.clinicalnlp-1.3/
%P 17-21
Markdown (Informal)
[Overview of the MEDIQA-WV 2025 Shared Task on Woundcare Visual Question Answering](https://aclanthology.org/2025.clinicalnlp-1.3/) (Yim et al., ClinicalNLP 2025)
ACL