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arXiv · 2510.26974

Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations

Jean-Philippe Corbeil·Asma Ben Abacha·Jerome Tremblay·Phillip Swazinna·Akila Jeeson Daniel·Miguel Del-Agua·Francois Beaulieu
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Abstract

Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-OE 2025 shared task, the first challenge on extracting medical orders from doctor-patient conversations. Six teams participated in the shared task and experimented with a broad range of approaches, and both closed- and open-weight large language models (LLMs). In this paper, we describe the MEDIQA-OE task, dataset, final leaderboard ranking, and participants' solutions.

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Jean-Philippe Corbeil, Asma Ben Abacha, Jerome Tremblay, Phillip Swazinna, Akila Jeeson Daniel, Miguel Del-Agua, Francois Beaulieu. 2025-10-30. Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations. https://arxiv.org/abs/2510.26974

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