@inproceedings{alhuzali-etal-2025-arahealthqa,
    title = "{A}ra{H}ealth{QA} 2025: The First Shared Task on {A}rabic Health Question Answering",
    author = "Alhuzali, Hassan  and
      Shamout, Farah E.  and
      Abdul-Mageed, Muhammad  and
      Abouzahir, Chaimae  and
      Daoud, Mouath Abu  and
      Alasmari, Ashwag  and
      Al-Eisawi, Walid  and
      Al-Monef, Renad  and
      Alqahtani, Ali  and
      Ayash, Lama  and
      Habash, Nizar  and
      Kharouf, Leen",
    editor = "Darwish, Kareem  and
      Ali, Ahmed  and
      Abu Farha, Ibrahim  and
      Touileb, Samia  and
      Zitouni, Imed  and
      Abdelali, Ahmed  and
      Al-Ghamdi, Sharefah  and
      Alkhereyf, Sakhar  and
      Zaghouani, Wajdi  and
      Khalifa, Salam  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Hamed, Injy  and
      Alyafeai, Zaid  and
      Alowisheq, Areeb  and
      Inoue, Go  and
      Mrini, Khalil  and
      Alshammari, Waad",
    booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.arabicnlp-sharedtasks.18/",
    pages = "107--118",
    ISBN = "979-8-89176-356-2",
    abstract = "We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 co-located with EMNLP 2025. This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q{\&}A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA."
}