@inproceedings{nicolson-etal-2025-impact,
    title = "The Impact of Auxiliary Patient Data on Automated Chest {X}-Ray Report Generation and How to Incorporate It",
    author = "Nicolson, Aaron  and
      Zhuang, Shengyao  and
      Dowling, Jason  and
      Koopman, Bevan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.9/",
    pages = "177--203",
    ISBN = "979-8-89176-251-0",
    abstract = "This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on data from a patient{'}s CXR exam, overlooking valuable information from patient electronic health records. Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we investigate the use of patient data from emergency department (ED) records {---} such as vital signs measured and medicines reconciled during an ED stay {---} for CXR report generation, with the aim of enhancing diagnostic accuracy. We also investigate conditioning CXR report generation on the clinical history section of radiology reports, which has been overlooked in the literature. We introduce a novel approach to transform these heterogeneous data sources into patient data embeddings that prompt a multimodal language model (CXRMate-ED). Our comprehensive evaluation indicates that using a broader set of patient data significantly enhances diagnostic accuracy. The model, training code, and dataset are publicly available."
}