@inproceedings{wu-etal-2025-healthcards,
    title = "{H}ealth{C}ards: Exploring Text-to-Image Generation as Visual Aids for Healthcare Knowledge Democratizing and Education",
    author = "Wu, Qian  and
      Gao, Zheyao  and
      Gou, Longfei  and
      Hou, Yifan  and
      Lau, Ann Sin Nga  and
      Dou, Qi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1401/",
    pages = "27524--27546",
    ISBN = "979-8-89176-332-6",
    abstract = "The evolution of text-to-image (T2I) generation techniques has introduced new capabilities for information visualization, with the potential to advance knowledge democratization and education. In this paper, we investigate how T2I models can be adapted to generate educational health knowledge contents, exploring their potential to make healthcare information more visually accessible and engaging. We explore methods to harness recent T2I models for generating health knowledge flashcards{---}visual educational aids that present healthcare information through appealing and concise imagery. To support this goal, we curated a diverse, high-quality healthcare knowledge flashcard dataset containing 2,034 samples sourced from credible medical resources. We further validate the effectiveness of fine-tuning open-source models with our dataset, demonstrating their promise as specialized health flashcard generators. Our code and dataset are available at: https://github.com/med-air/HealthCards."
}