首个从医患对话中提取医疗指令的竞赛，助力临床记录自动化
Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations
- 设计首个医患对话提取医疗指令的任务与数据集
- 六支团队使用大模型参与，最高F1达0.812
- 适合医疗AI、临床信息提取方向研究者参考
临床文档越来越多地采用自动语音识别和摘要技术，但将医患对话转化为电子病历中的可执行医疗指令仍属空白。解决此问题可显著减轻临床医生的文书负担，并直接影响后续患者治疗。我们推出了MEDIQA-OE 2025共享任务，这是首个针对从医患对话中提取医疗指令的挑战。共有六支队伍参与，尝试了多种方法，包括闭源与开源的大语言模型（LLMs）。本文详述了MEDIQA-OE任务的设计、数据集构建、最终排行榜及各参赛团队的解决方案。
原文摘要 · Abstract (English)
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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