@inproceedings{mullappilly-etal-2025-bimedix2,
    title = "{B}i{M}edi{X}2 : Bio-Medical {EX}pert {LMM} for Diverse Medical Modalities",
    author = "Mullappilly, Sahal Shaji  and
      Kurpath, Mohammed Irfan  and
      Pieri, Sara  and
      Alseiari, Saeed Yahya  and
      Cholakkal, Shanavas  and
      Aldahmani, Khaled M  and
      Khan, Fahad Shahbaz  and
      Anwer, Rao Muhammad  and
      Khan, Salman  and
      Baldwin, Timothy  and
      Cholakkal, Hisham",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.756/",
    doi = "10.18653/v1/2025.findings-emnlp.756",
    pages = "14051--14071",
    ISBN = "979-8-89176-335-7",
    abstract = "We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn conversation in Arabic and English and supports diverse medical imaging modalities, including radiology, CT, and histology. To train BiMediX2, we curate BiMed-V, an extensive Arabic-English bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions. This dataset supports a range of medical Large Language Model (LLM) and Large Multimodal Model (LMM) tasks, including multi-turn medical conversations, report generation, and visual question answering (VQA). We also introduce BiMed-MBench, the first Arabic-English medical LMM evaluation benchmark, verified by medical experts. BiMediX2 demonstrates excellent performance across multiple medical LLM and LMM benchmarks, achieving state-of-the-art results compared to other open-sourced models. On BiMed-MBench, BiMediX2 outperforms existing methods by over 9{\%} in English and more than 20{\%} in Arabic evaluations. Additionally, it surpasses GPT-4 by approximately 9{\%} in UPHILL factual accuracy evaluations and excels in various medical VQA, report generation, and report summarization tasks. Our trained models, instruction set, and source code are available at - https://github.com/mbzuai-oryx/BiMediX2"
}