Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911242520625152 |
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| author | Corbeil, Jean-Philippe Abacha, Asma Ben Tremblay, Jerome Swazinna, Phillip Daniel, Akila Jeeson Del-Agua, Miguel Beaulieu, Francois |
| author_facet | Corbeil, Jean-Philippe Abacha, Asma Ben Tremblay, Jerome Swazinna, Phillip Daniel, Akila Jeeson Del-Agua, Miguel Beaulieu, Francois |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations Corbeil, Jean-Philippe Abacha, Asma Ben Tremblay, Jerome Swazinna, Phillip Daniel, Akila Jeeson Del-Agua, Miguel Beaulieu, Francois Computation and Language Artificial Intelligence 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. |
| title | Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26974 |