---
license: mit
---

# PerMedCQA: Persian Medical Consumer QA Benchmark

**PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian**

PerMedCQA is the first large-scale, real-world benchmark for Persian-language medical consumer question answering. It contains anonymized medical inquiries from Persian-speaking users paired with professional responses, enabling rigorous evaluation of large language models in low-resource, health-related domains.

---

## 📊 Dataset Overview

- **Total entries**: 68,138 QA pairs  
- **Source platforms**: DrYab, HiSalamat, GetZoop, Mavara-e-Teb  
- **Timeframe**: Nov 10, 2022 – Apr 2, 2024  
- **Languages**: Persian only  
- **Licensing**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
- 
- ## 🔗 Paper

- 📄 [Paper on arXiv](https://arxiv.org/abs/2505.18331)  
- 📊 [Paper with Code page](https://paperswithcode.com/paper/permedcqa-benchmarking-large-language-models)  

---

## 🧬 Metadata & Features

Each example in the dataset includes:

- `instance_id`: Unique ID for each QA pair  
- `Title`: Short user-submitted title  
- `Question`: Full Persian-language consumer medical question  
- `Expert_Answer`: Doctor’s response  
- `Category`: Medical topic (e.g., “پوست و مو”)  
- `Specialty`: Expert’s medical field (e.g., “متخصص پوست و مو”)  
- `Age`: Reported patient age  
- `Weight`: Reported weight (optional)  
- `Sex`: Patient gender (`"man"` or `"woman"`)  
- `dataset_source`: Name of the platform (e.g., DrYab, Getzoop)  
- `Tag`: ICD‑11 label and rationale  
- `QuestionType`: Question classification tag (e.g., "Contraindication", "Indication") and reasoning  

---

## 📁 Dataset Structure

```json
{
  "Title": "قرمزی پوست نوزاد بعد از استفاده از پماد",
  "Category": "پوست و مو",
  "Specialty": "متخصص پوست و مو",
  "Age": "1",
  "Weight": "10",
  "Sex": "man",
  "dataset_source": "HiSalamat",
  "instance_id": 32405,
  "Tag": {
    "Tag": 23,
    "Tag_Reasoning": "The question addresses a skin reaction in an infant following the application of a cream, indicating a dermatological condition."
  },
  "Question": "سلام خسته نباشید. من واسه پسر ۱ ساله‌ام که جای واکسنش سفت شده بود، پماد موضعی استفاده کردم. ولی الان پوستش خیلی قرمز شده و خارش داره. ممکنه حساسیت داده باشه؟ باید چکار کنم؟",
  "Expert_Answer": "احتمالا پوست نوزاد به ترکیبات پماد حساسیت نشان داده است. مصرف آن را قطع کنید و در صورت ادامه علائم به متخصص پوست مراجعه کنید.",
  "QuestionType": {
    "Explanation": "The user asks about an adverse skin reaction following the use of a topical medication on an infant, which is a case of possible side effects.",
    "QuestionType_Tag": "SideEffect"
  }
}
```

---

## 📥 How to Load

```python
from datasets import load_dataset

ds = load_dataset("NaghmehAI/PerMedCQA", split="train")
print(ds[0])
```

---

## 🚀 Intended Uses

- 🧠 **Evaluation** of multilingual or Persian-specific LLMs in real-world, informal medical domains  
- 🛠️ **Few-shot or zero-shot** fine-tuning, instruction-tuning  
- 🌍 **Cultural insights**: Persian language behavior in health-related discourse  
- ⚠️ **NOT for clinical use**: Informational and research purposes only

---

## ⚙️ Data Processing Pipeline

### Stage 1: Column Transformation (`change_columns.py`)
- Reads CSV/JSON input and transforms them into structured JSON format
- Handles single-turn, multi-turn, and multi-expert Q&A data
- Cleans and formats the text, removing unnecessary whitespace and newlines
- Creates a chat-style JSON file with `user` and `assistant` roles

### Stage 2: QA Preprocessing (`preprocess_for_qa.py`)
- Truncates multi-turn dialogues to first Q&A pair
- Removes:
  - Empty or invalid messages
  - Q&A pairs shorter than 3 words
  - Duplicate Q&A instances
- Adds:
  - `dataset_source` and `instance_id` to each item
- Merges cleaned records into `All_QA_preprocessed.json`

### Dataset Cleaning Results:
| Dataset       | Step 1 Removed | Step 2 Removed | Step 3 Removed | Final Records |
|--------------|----------------|----------------|----------------|----------------|
| Dr_Yab       | 63             | 1083           | 25             | 37,905         |
| GetZoop      | 1005           | 1352           | 8              | 25,502         |
| Hi-Salamat   | 9580           | 121            | 1              | 5,220          |
| Mavara-e-Teb | 0              | 1034           | 92             | 4,789          |

---

## 📚 Citation

If you use **PerMedCQA**, please cite:

```bibtex
@misc{jamali2025permedcqa,
  title={PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language},
  author={Jamali, Naghmeh and Mohammadi, Milad and Baledi, Danial and Rezvani, Zahra and Faili, Heshaam},
  year={2025},
  eprint={2505.18331},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.18331}
}
```

---

## 📬 Contact

For collaboration or questions:

📧 Naghmeh Jamali – naghme.jamali.ai@gmail.com, Milad Mohammadi – miladmohammadi@ut.ac.ir, Danial Baledi – baledi.danial@gmail.com.