@inproceedings{yim-etal-2024-overview,
title = "Overview of the {MEDIQA}-{M}3{G} 2024 Shared Task on Multilingual Multimodal Medical Answer Generation",
author = "Yim, Wen-wai and
Ben Abacha, Asma and
Fu, Yujuan and
Sun, Zhaoyi and
Xia, Fei and
Yetisgen, Meliha and
Krallinger, Martin",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.55",
doi = "10.18653/v1/2024.clinicalnlp-1.55",
pages = "581--589",
abstract = "Remote patient care provides opportunities for expanding medical access, saving healthcare costs, and offering on-demand convenient services. In the MEDIQA-M3G 2024 Shared Task, researchers explored solutions for the specific task of dermatological consumer health visual question answering, where user generated queries and images are used as input and a free-text answer response is generated as output. In this novel challenge, eight teams with a total of 48 submissions were evaluated across three language test sets. In this work, we provide a summary of the dataset, as well as results and approaches. We hope that the insights learned here will inspire future research directions that can lead to technology that deburdens clinical workload and improves care.",
}
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%0 Conference Proceedings
%T Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer Generation
%A Yim, Wen-wai
%A Ben Abacha, Asma
%A Fu, Yujuan
%A Sun, Zhaoyi
%A Xia, Fei
%A Yetisgen, Meliha
%A Krallinger, Martin
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yim-etal-2024-overview
%X Remote patient care provides opportunities for expanding medical access, saving healthcare costs, and offering on-demand convenient services. In the MEDIQA-M3G 2024 Shared Task, researchers explored solutions for the specific task of dermatological consumer health visual question answering, where user generated queries and images are used as input and a free-text answer response is generated as output. In this novel challenge, eight teams with a total of 48 submissions were evaluated across three language test sets. In this work, we provide a summary of the dataset, as well as results and approaches. We hope that the insights learned here will inspire future research directions that can lead to technology that deburdens clinical workload and improves care.
%R 10.18653/v1/2024.clinicalnlp-1.55
%U https://aclanthology.org/2024.clinicalnlp-1.55
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.55
%P 581-589
Markdown (Informal)
[Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer Generation](https://aclanthology.org/2024.clinicalnlp-1.55) (Yim et al., ClinicalNLP-WS 2024)
ACL