Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer Generation

Wen-wai Yim, Asma Ben Abacha, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen, Martin Krallinger

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.
Anthology ID:
2024.clinicalnlp-1.55
Volume:
Proceedings of the 6th Clinical Natural Language Processing Workshop
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
Venues:
ClinicalNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
581–589
Language:
URL:
https://aclanthology.org/2024.clinicalnlp-1.55
DOI:
Bibkey:
Cite (ACL):
Wen-wai Yim, Asma Ben Abacha, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen, and Martin Krallinger. 2024. Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer Generation. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 581–589, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer Generation (Yim et al., ClinicalNLP-WS 2024)
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https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.55.pdf
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