README.md

# DoctorIA-MED-R-7B

# DoctorIA Medical Solutions: Revolutionizing Healthcare with AI-Powered Diagnostics

![Hugging Face Badge](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue) ![License](https://img.shields.io/badge/license-Apache%202.0-green)

[[Read the Paper]](https://example.com/paper) [[Demo]](https://example.com/demo) [[Hugging Face Model](https://huggingface.co/your-username/doctoria-model)]

**DoctorIA** is an innovative AI-powered solution designed to enhance radiological diagnostics, improve healthcare access, and reduce disparities in underserved areas of Morocco. Leveraging state-of-the-art machine learning techniques, DoctorIA provides accurate, efficient, and scalable diagnostic support for medical professionals.

---

## Overview

DoctorIA is built to assist healthcare providers with:
- **Automated Medical Image Analysis**: Accurate interpretation of X-rays, MRIs, and other medical imaging technologies.
- **Clinical Reasoning Support**: Advanced reasoning capabilities to assist in diagnosis, treatment planning, and risk assessment.
- **Healthcare Accessibility Initiatives**: Bridging gaps in healthcare access by offering scalable solutions to underserved populations.

Our mission is to empower healthcare professionals and patients alike by providing cutting-edge AI-driven diagnostic tools.

---

## Key Features

- **AI-Driven Diagnostic Tools**: Supports clinical reasoning and treatment planning by providing insights derived from advanced AI algorithms.
- **Radiology Assistance**: Assists radiologists with preliminary analysis of medical images.
- **Patient Education**: Provides clear explanations of medical procedures and technologies.
- **Multilingual Support**: Available in Arabic, French, English, and Spanish to cater to diverse populations.
- **Scalable Deployment**: Optimized for deployment in resource-constrained environments, ensuring accessibility even in underserved areas.

---

## Models

We release two versions of the DoctorIA model:

1. **DoctorIA-ClinicalReasoning**  
   - **Purpose**: Clinical reasoning and diagnostic support.  
   - **Tasks**: Symptom-to-diagnosis mapping, treatment planning, and evidence-based recommendations.  
   - **Quantization**: Available in 4-bit precision for reduced memory usage.  
   - **Hugging Face Repository**: [DoctorIA-ClinicalReasoning](https://huggingface.co/your-username/doctoria-clinical-reasoning)  

2. **DoctorIA-MedicalImageAnalysis**  
   - **Purpose**: Automated analysis of medical images (X-rays, MRIs, etc.).  
   - **Tasks**: Disease detection, lesion segmentation, and abnormality classification.  
   - **Quantization**: Available in 4-bit precision for reduced memory usage.  
   - **Hugging Face Repository**: [DoctorIA-MedicalImageAnalysis](https://huggingface.co/your-username/doctoria-medical-image-analysis)  

All models are released under the **Apache 2.0 License**.

---

## Organisation of the Repository

The repository is structured as follows:

- **`clinical_reasoning/`**: Contains the code and resources for the clinical reasoning model.
- **`medical_image_analysis/`**: Contains the code and resources for the medical image analysis model.
- **`examples/`**: Example scripts for inference, fine-tuning, and integration.
- **`datasets/`**: Links to datasets used for training and evaluation.
- **`notebooks/`**: Jupyter notebooks for experimentation and visualization.
- **`docs/`**: Additional documentation and tutorials.

---

## Requirements

To use DoctorIA, you will need:

- Python 3.8 or higher (Python 3.10 recommended).
- PyTorch (`torch`) and Transformers (`transformers`) libraries.
- GPU with at least 16GB of memory (for full-precision models).

Install dependencies using:

```bash
pip install -r requirements.txt
```

For quantized models (4-bit precision), ensure you have the `bitsandbytes` library installed:

```bash
pip install bitsandbytes
```

---

## Usage

### 1. Clinical Reasoning Model

Load the model and tokenizer:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "your-username/doctoria-clinical-reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example input
inputs = tokenizer("The patient presents with fever, cough, and shortness of breath.", return_tensors="pt")
outputs = model(**inputs)
predicted_diagnosis = outputs.logits.argmax().item()
print(f"Predicted Diagnosis: {predicted_diagnosis}")
```

### 2. Medical Image Analysis Model

Load the model and feature extractor:

```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
import requests

model_name = "your-username/doctoria-medical-image-analysis"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Example input
url = "https://example.com/chest-xray.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax().item()
print(f"Predicted Class: {predicted_class}")
```

---

## Benchmarks

DoctorIA has been evaluated on several benchmarks to ensure its performance and reliability:

- **Clinical Reasoning**: Achieved **X% accuracy** on the DR.BENCH benchmark for clinical diagnostic reasoning.
- **Medical Image Analysis**: Achieved **Y% sensitivity** and **Z% specificity** on the CheXpert benchmark for chest X-ray analysis.

For more details, refer to our [paper](https://example.com/paper).

---

## Development

If you wish to contribute to DoctorIA or modify it for your needs:

1. Clone the repository:
   ```bash
   git clone https://github.com/your-username/doctoria.git
   cd doctoria
   ```

2. Install dependencies:
   ```bash
   pip install -e '.[dev]'
   ```

3. Run tests:
   ```bash
   pytest
   ```

---

## FAQ

Check out the [Frequently Asked Questions](FAQ.md) section before opening an issue.

---

## License

The codebase is released under the **Apache 2.0 License**.  
The model weights are released under the **CC-BY 4.0 License**.

---

## Citation

If you use DoctorIA in your research or projects, please cite our work:

```bibtex
@techreport{doctoria2025,
      title={DoctorIA: Enhancing Radiological Diagnostics with AI},
      author={Jad Tounsi El Azzoiani and Team DoctorIA},
      year={2025},
      eprint={XXXX.XXXXX},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://example.com/paper},
}
```