README.md

---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
tags:
- medical
- vision
- multimodal
---

# Medical-Vision: High-Quality Medical Visual Question Answering Dataset (Aquiles-ai/Medical-Vision)

## Dataset Description

**Medical-Vision** is a curated dataset designed for Visual Question Answering (VQA) in medical contexts. This dataset combines high-quality medical images with corresponding questions and expert answers, making it ideal for training and evaluating vision-language models in healthcare applications.

### Key Features

- **8,035 high-quality examples**
- **Diverse medical imaging modalities** (X-rays, CT scans, MRI, pathology slides, etc.)
- **Natural question-answer pairs** covering clinical interpretations, diagnoses, and medical descriptions
- **Carefully curated and preprocessed** from multiple authoritative sources
- **Randomly shuffled** to prevent training biases

## Dataset Structure

### Data Fields

- `image`: PIL Image object containing the medical image
- `question`: String with the medical question about the image
- `answer`: String with the expert answer or description

### Data Splits

| Split | Examples |
|-------|----------|
| train | 8,035 |

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Aquiles-ai/Medical-Vision")

# Access the training split
train_data = dataset['train']

# View dataset info
print(f"Number of examples: {len(train_data)}")
print(f"Features: {train_data.features}")
```

### Example Usage

```python
from datasets import load_dataset
from PIL import Image
import matplotlib.pyplot as plt

# Load dataset
dataset = load_dataset("Aquiles-ai/Medical-Vision")

# Get a random example
example = dataset['train'][0]

# Display the image
plt.figure(figsize=(10, 6))
plt.imshow(example['image'])
plt.axis('off')
plt.title('Medical Image')
plt.show()

# Print Q&A
print(f"Question: {example['question']}")
print(f"\nAnswer: {example['answer']}")
```

**Output Example:**
```
Question: What do you see in this image? Describe it medically.

Answer: The chest X-ray shows bilateral infiltrates consistent with 
pulmonary edema. There is also cardiomegaly with an enlarged cardiac 
silhouette. The costophrenic angles are preserved, and no pleural 
effusion is visible.
```

## Applications

This dataset is suitable for:

- **Medical Visual Question Answering**: Training models to answer questions about medical images
- **Clinical Decision Support**: Developing AI assistants for radiologists and clinicians
- **Medical Education**: Creating interactive learning tools for medical students
- **Vision-Language Models**: Fine-tuning multimodal models (LLaVA, Qwen-VL, Asclepio, etc.)
-  **Medical Image Captioning**: Generating descriptive captions for medical images

## Dataset Creation

### Quality Assurance

- Manual verification of image-question-answer alignment
- Removal of duplicates and low-quality examples
- Validation of image loading and accessibility
- Consistency checks across all data fields

## Considerations for Use

### Intended Use

This dataset is intended for:
- Research in medical AI and computer vision
- Development of clinical decision support tools
- Educational purposes in medical AI
- Fine-tuning vision-language models for healthcare

### Limitations

- **Not for clinical diagnosis**: This dataset is for research and development only
- **Language**: Currently only available in English
- **Image quality**: Varies across source datasets
- **Medical scope**: May not cover all medical specialties equally
- **Requires expert validation**: Any clinical application requires validation by medical professionals

### Ethical Considerations

- All images are from publicly available medical datasets
- No patient identifiable information (PII) is included
- Users should follow appropriate ethical guidelines when deploying models trained on this data
- Medical AI outputs should always be reviewed by qualified healthcare professionals

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{medical_vision_2025,
  title={Medical-Vision: High-Quality Medical Visual Question Answering Dataset (Aquiles-ai/Medical-Vision)},
  author={Aquiles-ai},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Aquiles-ai/Medical-Vision}
}
```

## License

This dataset is released under the Apache 2.0 License. Please refer to individual source datasets for their specific licensing terms.

## Contact

For questions, issues, or contributions, please open an issue on the dataset repository or contact the maintainers.

- **More about [Aquiles-ai](https://aquiles-ai.vercel.app).**

- **Aquiles-ai on [GitHub](https://github.com/Aquiles-ai).**

- **Our collections at [HuggingFace](https://huggingface.co/Aquiles-ai/collections).**

**Disclaimer**: This dataset is provided for research and educational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.