---
language:
- fa
size_categories:
- n<1K
task_categories:
- visual-question-answering
- multiple-choice
- question-answering
pretty_name: PerMed-MM

configs:
- config_name: default
  data_files:
  - split: test
    path: PerMed-MM.json

tags:
- medical
---

# PerMed-MM: A Multimodal, Multi-Specialty Persian Medical Benchmark

[**🤗 Dataset**](https://huggingface.co/datasets/universitytehran/PerMed-MM) | [**📖 Paper**](https://aclanthology.org/2025.ijcnlp-short.21/) | [**📄 PDF**](https://aclanthology.org/2025.ijcnlp-short.21.pdf)

## Dataset Description

**PerMed-MM** is a multimodal, multi-specialty benchmark designed to evaluate Vision Language Models (VLMs) on Persian medical question answering. 

The dataset consists of **733 multiple-choice questions** sourced from the Iranian National Medical Board Exams (years 2021 and 2023). Each question is paired with **1 to 5 clinically relevant images**, totaling **944 images** across **46 medical specialties** and multiple visual modalities.

---

## Dataset Statistics

### Image Modality Distribution
| Image Modality | Count | Percentage (%) |
| :--- | :---: | :---: |
| Microscopic Pathology | 175 | 18.5% |
| Charts, Diagrams & Tables | 138 | 14.6% |
| X-ray | 131 | 13.9% |
| CT | 115 | 12.2% |
| Clinical / Gross Photography | 97 | 10.3% |
| Ultrasound | 76 | 8.1% |
| Electrophysiology (ECG / EEG) | 73 | 7.7% |
| Nuclear Medicine | 72 | 7.6% |
| MRI | 46 | 4.9% |
| Endoscopy | 21 | 2.2% |

### Images per Question
- **1 image:** 82.3% (603 questions)
- **2 images:** 11.3% (83 questions)
- **3 images:** 2.0% (15 questions)
- **4 images:** 4.1% (30 questions)
- **5 images:** 0.3% (2 questions)

---

## Citation

If you use this dataset or find it helpful in your research, please cite our paper:

```bibtex
@inproceedings{khoramfar-etal-2025-permed,
    title = "{P}er{M}ed-{MM}: A Multimodal, Multi-Specialty {P}ersian Medical Benchmark for Evaluating Vision Language Models",
    author = "Khoramfar, Ali  and Dousti, Mohammad Javad  and Faili, Heshaam",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.ijcnlp-short.21/",
    doi = "10.18653/v1/2025.ijcnlp-short.21",
    pages = "232--241",
    ISBN = "979-8-89176-299-2"
}
```