@inproceedings{akhter-etal-2026-rad,
    title = "Rad-Flamingo: A Multimodal Prompt driven Radiology Report Generation Framework with Patient-Centric Explanations",
    author = "Akhter, Md. Tousin  and
      Lalwani, Devansh  and
      Jadhav, Kshitij Sharad  and
      Bhattacharyya, Pushpak",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.10/",
    pages = "166--188",
    ISBN = "979-8-89176-386-9",
    abstract = "In modern healthcare, radiology plays a pivotal role in diagnosing and managing diseases. However, the complexity of medical imaging data and the variability in interpretation can lead to inconsistencies and a lack of patient-centered insight in radiology reports. To address this challenge, a novel multimodal prompt-driven report generation framework Rad-Flamingo was developed, that integrates diverse data modalities{---}such as medical images, and clinical notes{---}to produce comprehensive and context-aware radiology reports. Our framework leverages innovative prompt engineering techniques to guide vision-language models in generating relevant information, ensuring these generated reports are not only accurate but also understandable to individual patients. A key feature of our framework is its ability to provide patient-centric explanations, offering clear and personalized insights into diagnostic findings and their implications. Additionally, we also demonstrate a synthetic data generation pipeline, to append any existing benchmark datasets' findings and impressions with patient-centric explanation. Experimental results demonstrate that this framework{'}s effectiveness in enhancing report quality, improving understandability, and could foster better patient-doctor communication. This approach represents a significant step towards human-centered medical AI systems."
}