@inproceedings{alasmari-etal-2024-aramed,
    title = "{A}ra{M}ed: {A}rabic Medical Question Answering using Pretrained Transformer Language Models",
    author = "Alasmari, Ashwag  and
      Alhumoud, Sarah  and
      Alshammari, Waad",
    editor = "Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Mubarak, Hamdy  and
      Ali, Mona  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.osact-1.6",
    pages = "50--56",
    abstract = "Medical Question Answering systems have gained significant attention in recent years due to their potential to enhance medical decision-making and improve patient care. However, most of the research in this field has focused on English-language datasets, limiting the generalizability of MQA systems to non-English speaking regions. This study introduces AraMed, a large-scale Arabic Medical Question Answering dataset addressing the limited resources available for Arabic medical question answering. AraMed comprises of 270k question-answer pairs based on health consumer questions submitted to online medical forum. Experiments using various deep learning models showcase the dataset{'}s effectiveness, particularly with AraBERT models achieving highest results, specifically AraBERTv2 obtained an F1 score of 96.73{\%} in the answer selection task. The comparative analysis of different deep learning models provides insights into their strengths and limitations. These findings highlight the potential of AraMed for advancing Arabic medical question answering research and development.",
}

Medical Question Answering systems have gained significant attention in recent years due to their potential to enhance medical decision-making and improve patient care. However, most of the research in this field has focused on English-language datasets, limiting the generalizability of MQA systems to non-English speaking regions. This study introduces AraMed, a large-scale Arabic Medical Question Answering dataset addressing the limited resources available for Arabic medical question answering. AraMed comprises of 270k question-answer pairs based on health consumer questions submitted to online medical forum. Experiments using various deep learning models showcase the dataset{'}s effectiveness, particularly with AraBERT models achieving highest results, specifically AraBERTv2 obtained an F1 score of 96.73{\%} in the answer selection task. The comparative analysis of different deep learning models provides insights into their strengths and limitations. These findings highlight the potential of AraMed for advancing Arabic medical question answering research and development.
Alasmari, Ashwag Alhumoud, Sarah Alshammari, Waad
AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models