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.

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.