---
license: cc-by-4.0
task_categories:
- question-answering
- multiple-choice
language:
- fr
tags:
- medical-qa
- open-ended-qa
- multiple-choice-qa
pretty_name: MediQAl
size_categories:
- 10K<n<100K
dataset_info:
- config_name: mcqu
  features:
  - name: id
    dtype: string
  - name: clinical_case
    dtype: string
  - name: question
    dtype: string
  - name: answer_a
    dtype: string
  - name: answer_b
    dtype: string
  - name: answer_c
    dtype: string
  - name: answer_d
    dtype: string
  - name: answer_e
    dtype: string
  - name: correct_answers
    dtype: string
  - name: task
    dtype: string
  - name: medical_subject
    dtype: string
  - name: question_type
    dtype: string
  splits:
  - name: train
    num_examples: 10113
  - name: validation
    num_examples: 2561
  - name: test
    num_examples: 4343
- config_name: mcqm
  features:
  - name: id
    dtype: string
  - name: clinical_case
    dtype: string
  - name: question
    dtype: string
  - name: answer_a
    dtype: string
  - name: answer_b
    dtype: string
  - name: answer_c
    dtype: string
  - name: answer_d
    dtype: string
  - name: answer_e
    dtype: string
  - name: correct_answers
    dtype: string
  - name: task
    dtype: string
  - name: medical_subject
    dtype: string
  - name: question_type
    dtype: string
  splits:
  - name: train
    num_examples: 5767
  - name: validation
    num_examples: 1466
  - name: test
    num_examples: 3384
- config_name: oeq
  features:
  - name: id
    dtype: string
  - name: clinical_case
    dtype: string
  - name: cc_question_number
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: medical_subject
    dtype: string
  - name: question_type
    dtype: string
  splits:
  - name: test
    num_examples: 4969
configs:
- config_name: mcqu
  data_files:
  - split: train
    path: mcqu/train.json
  - split: validation
    path: mcqu/validation.json
  - split: test
    path: mcqu/test.json
- config_name: mcqm
  data_files:
  - split: train
    path: mcqm/train.json
  - split: validation
    path: mcqm/validation.json
  - split: test
    path: mcqm/test.json
- config_name: oeq
  data_files:
  - split: test
    path: oeq/test.json
---

# MediQAl

**MediQAl** is a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and clinical reasoning. It includes **32,603 questions** sourced from French medical examinations across **41 medical subjects**.

The dataset contains three tasks:

- **MCQU**: Multiple-Choice Questions with a Unique correct answer  
- **MCQM**: Multiple-Choice Questions with Multiple correct answers  
- **OEQ**: Open-Ended Questions with Short Answers  

Each question is labeled as either **"Understanding"** or **"Reasoning"**, enabling analysis of the cognitive capabilities of language models.

## Usage Example

```python
from datasets import load_dataset

# Load the MCQU split
dataset = load_dataset("ANR-MALADES/MediQAl", name="mcqu")

# Explore an entry
print(dataset["train"][0])
```

---

## Dataset Details

- **Languages**: French (`fr`)
- **Domain**: Medical Education
- **License**: CC-BY-4.0
- **Source**: French national medical examinations
- **Total Examples**: 32,603
- **Medical Subjects**: 41 specialties

---

## Configurations

| Config | Task Type | Answer Format |
|--------|-----------|---------------|
| `mcqu` | Multiple choice (unique answer)   | One correct option (A–E) |
| `mcqm` | Multiple choice (multiple answers) | Multiple correct options (A–E) |
| `oeq`  | Open-ended question answering     | Short free-text answer |

---

## Dataset Structure

### Features

#### `mcqu` / `mcqm`
- `id`: Question ID
- `clinical_case`: Clinical scenario
- `question`: The main question
- `answer_a` to `answer_e`: Answer choices
- `correct_answers`: One (mcqu) or more (mcqm) correct choices (e.g., `"A,C,E"`)
- `task`: Task type (mcqu / mcqm)
- `medical_subject`: Specialty (e.g., cardiology)
- `question_type`: `"Understanding"` or `"Reasoning"`

#### `oeq`
- `id`: Question ID
- `clinical_case`: Clinical scenario
- `cc_question_number`: Sub-question number
- `question`: Open-ended question
- `answer`: Short answer
- `medical_subject`: Specialty
- `question_type`: `"Understanding"` or `"Reasoning"`

---

## Dataset Characteristics

|                                         | **MCQU** Understanding | **MCQU** Reasoning | **MCQU** Total | **MCQM** Understanding | **MCQM** Reasoning | **MCQM** Total | **OEQ** Understanding | **OEQ** Reasoning | **OEQ** Total |
|-----------------------------------------|-------------------------|--------------------|----------------|-------------------------|--------------------|----------------|------------------------|-------------------|----------------|
| **Total Number of Questions**           | 11,336                  | 5,681              | 17,017         | 7,742                   | 2,875              | 10,617         | 1,842                  | 3,125             | 4,969          |
| *# Isolated Questions*                  | 9,126                   | 961                | 10,087         | 6,200                   | 343                | 6,543          | 836                    | 179               | 1,015          |
| *# In-context Questions*                | 2,210                   | 4,720              | 6,930          | 1,542                   | 2,532              | 4,074          | 1,006                  | 2,946             | 3,954          |
| **Avg Question Length (words)**         | 18.95                   | 21.57              | 19.82          | 13.20                   | 16.12              | 13.99          | 16.79                  | 20.95             | 19.40          |
| **Avg Clinical Scenario Length (words)**| 83.50                   | 107.67             | 99.97          | 94.87                   | 114.77             | 107.24         | 109.71                 | 141.28            | 132.19         |
| **Avg Answer Length (words)**           | -                       | -                  | -              | -                       | -                  | -              | 25.26                  | 40.24             | 34.68          |


## Splits

| Split        | MCQU | MCQM | OEQ  |
|--------------|------|------|------|
| Train        | ✅  10,113  | ✅  5,767  | ❌    |
| Validation   | ✅  2,561  | ✅  1,466  | ❌    |
| Test         | ✅  4,343  | ✅  3,384  | ✅  4,969  |

---

## Citation
```latex
@article{bazoge2026mediqal,
  title={MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation},
  author={Bazoge, Adrien},
  journal={Scientific Data},
  year={2026},
  publisher={Nature Publishing Group UK London}
}
```