metadata
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 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:
@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"
}