BiMed-V-1.6M Dataset

Dataset Description

BiMed-V is a comprehensive Arabic-English multimodal bilingual instruction dataset comprising over 1.6M instructions designed for training medical Large Multimodal Models (LMMs). This dataset was curated as part of the BiMediX2 project, the first bilingual (Arabic-English) Bio-Medical Expert LMM for diverse medical modalities.

The dataset supports both English and Arabic languages, enabling the development of multilingual medical AI systems.

Dataset Structure

The dataset is split into two training stages:

Files

• BiMed-V_stage1.json: Pretraining data for vision-language projection alignment
• BiMed-V_stage2.json: Instruction finetuning data for task-specific adaptation

Data Fields

Each sample in the dataset contains:

• id: Unique identifier for the sample
• image: Path or reference to the associated medical image (None for text-only instruction samples)
• conversations: List of conversation turns with roles
• language: Arabic or English
• modality: Text or Vision

Citation

If you use BiMed-V dataset in your research, please cite:

@misc{mullappilly2024bimedix2biomedicalexpertlmm,
      title={BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities}, 
      author={Sahal Shaji Mullappilly and Mohammed Irfan Kurpath and Sara Pieri and Saeed Yahya Alseiari and Shanavas Cholakkal and Khaled Aldahmani and Fahad Khan and Rao Anwer and Salman Khan and Timothy Baldwin and Hisham Cholakkal},
      year={2024},
      eprint={2412.07769},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.07769}, 
}

License

BiMed-V dataset is released under the CC-BY-NC-SA 4.0 License. For more details, please refer to the LICENSE file.

Ethical Considerations

⚠️ Important Notice: This dataset is intended for research purposes only and is not ready for clinical or commercial use.

Users should be aware that:

• Medical AI models trained on this data should be validated by qualified healthcare professionals
• The dataset should not be used as the sole basis for medical diagnoses or treatment decisions
• Outputs from models trained on this data may contain errors, biases, or hallucinations
• Always verify AI-generated medical information with licensed healthcare providers