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.

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.