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DoctorIA-XRAD-R-11B

Name: DoctorIA-XRAD-R-11B
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DoctorIA-XRAD-R-11B

DoctorIA Medical Solutions: Revolutionizing Healthcare with AI-Powered Diagnostics

[Read the Paper] [Demo] [Hugging Face 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:

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
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

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:

pip install -r requirements.txt

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

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.

Development

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

Clone the repository:

bash
1git clone https://github.com/your-username/doctoria.git 2cd doctoria
Install dependencies:

pip install -e '.[dev]'
Run tests:

pytest

FAQ

Check out the Frequently Asked Questions 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},
}

doctoria/DoctorIA-XRAD-R-11B