@inproceedings{ben-abacha-etal-2024-overview,
title = "Overview of the {MEDIQA}-{CORR} 2024 Shared Task on Medical Error Detection and Correction",
author = "Ben Abacha, Asma and
Yim, Wen-wai and
Fu, Yujuan and
Sun, Zhaoyi and
Xia, Fei and
Yetisgen, Meliha",
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-1.57/",
doi = "10.18653/v1/2024.clinicalnlp-1.57",
pages = "596--603",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction
%A Ben Abacha, Asma
%A Yim, Wen-wai
%A Fu, Yujuan
%A Sun, Zhaoyi
%A Xia, Fei
%A Yetisgen, Meliha
%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 ben-abacha-etal-2024-overview
%X 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.
%R 10.18653/v1/2024.clinicalnlp-1.57
%U https://aclanthology.org/2024.clinicalnlp-1.57/
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.57
%P 596-603
Markdown (Informal)
[Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction](https://aclanthology.org/2024.clinicalnlp-1.57/) (Ben Abacha et al., ClinicalNLP 2024)
ACL