@inproceedings{corbeil-etal-2025-overview,
    title = "Overview of the {MEDIQA}-{OE} 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations",
    author = "Corbeil, Jean-Philippe  and
      Ben Abacha, Asma  and
      Tremblay, Jerome  and
      Swazinna, Phillip  and
      Daniel, Akila Jeeson  and
      Del-Agua, Miguel  and
      Beaulieu, Francois",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
    month = oct,
    year = "2025",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/retractions/2025.clinicalnlp-1.2/",
    pages = "11--16",
    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."
}