Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction
Asma Ben Abacha, Wen-wai Yim, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen
Abstract
Automatic detection and correction of medical errors enables a more rigorous validation of medical documentation as well as clinical notes generated by large language models. Such solutions can ensure the accuracy and medical coherence of clinical texts and enhance patient care and health outcomes. The MEDIQA-CORR 2024 shared task focused on detecting and correcting different types of medical errors in clinical texts. Seventeen teams participated in the shared task and experimented with a broad range of approaches and models. In this paper, we describe the MEDIQA-CORR task, datasets, and the participants’ results and methods.- Anthology ID:
- 2024.clinicalnlp-1.57
- 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:
- 596–603
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.57
- DOI:
- Cite (ACL):
- Asma Ben Abacha, Wen-wai Yim, Yujuan Fu, Zhaoyi Sun, Fei Xia, and Meliha Yetisgen. 2024. Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 596–603, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction (Ben Abacha et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.57.pdf