Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction - Ben Abacha, Asma; Yim, Wen-wai; Fu, Yujuan; Sun, Zhaoyi; Xia, Fei; Yetisgen, Meliha (ClinicalNLP@NAACL 2024, 2024)
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.