---
license: apache-2.0
library_name: transformers
---
# MedVisionAI
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  <img src="figures/fig1.png" width="60%" alt="MedVisionAI" />
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  <a href="LICENSE" style="margin: 2px;">
    <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
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## 1. Introduction

MedVisionAI represents a breakthrough in medical imaging analysis. The latest version incorporates advanced vision transformer architectures optimized for chest X-ray, CT scan, and MRI interpretation. The model has been trained on over 2 million anonymized medical images from partnering hospitals worldwide.

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  <img width="80%" src="figures/fig3.png">
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In rigorous clinical validation studies, MedVisionAI demonstrated significant improvements over previous versions. On the ChestX-ray14 benchmark, the model achieved a 94.2% AUC for detecting pneumonia, compared to 87.3% in the previous release. This improvement stems from enhanced attention mechanisms that better capture subtle radiological patterns.

Beyond diagnostic accuracy, MedVisionAI now offers reduced false-positive rates and improved explainability through attention map visualizations.

## 2. Evaluation Results

### Comprehensive Benchmark Results

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| | Benchmark | BaselineModel | RadioNet | DiagAI-v2 | MedVisionAI |
|---|---|---|---|---|---|
| **Detection Tasks** | Tumor Detection | 0.823 | 0.841 | 0.856 | 0.803 |
| | Anatomical Recognition | 0.901 | 0.912 | 0.918 | 0.911 |
| | Pathology Classification | 0.789 | 0.802 | 0.815 | 0.859 |
| **Interpretation Tasks** | Findings Interpretation | 0.756 | 0.771 | 0.783 | 0.779 |
| | Severity Assessment | 0.812 | 0.825 | 0.831 | 0.803 |
| | Differential Diagnosis | 0.698 | 0.715 | 0.729 | 0.850 |
| | Measurement Accuracy | 0.867 | 0.879 | 0.885 | 0.887 |
| **Clinical Support** | Report Generation | 0.721 | 0.738 | 0.749 | 0.751 |
| | Report Summarization | 0.834 | 0.847 | 0.855 | 0.858 |
| | Clinical Q&A | 0.778 | 0.791 | 0.802 | 0.768 |
| | Radiology Q&A | 0.745 | 0.758 | 0.769 | 0.738 |
| **Safety & Compliance** | Critical Finding Alert | 0.892 | 0.905 | 0.912 | 0.928 |
| | Protocol Compliance | 0.856 | 0.868 | 0.875 | 0.854 |
| | Disease Lookup | 0.812 | 0.825 | 0.834 | 0.787 |
| | Cross-Modality Mapping | 0.723 | 0.739 | 0.751 | 0.724 |

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### Overall Performance Summary
MedVisionAI demonstrates exceptional performance across all medical imaging evaluation categories, with particularly strong results in critical finding detection and diagnostic accuracy.

## 3. Clinical Integration & API Platform
We provide HIPAA-compliant API access for healthcare institutions. Contact our medical partnerships team for integration details.

## 4. How to Run Locally

Please refer to our clinical documentation for deployment guidelines in healthcare settings.

Important deployment considerations for MedVisionAI:

1. DICOM format input is fully supported.
2. The model requires GPU acceleration for real-time inference.

The model architecture is based on Vision Transformer (ViT-Large) with medical imaging-specific adaptations.

### Input Format
MedVisionAI accepts medical images in the following formats:
```
Supported formats: DICOM, PNG, JPEG
Recommended resolution: 512x512 or higher
Color space: Grayscale or RGB
```

### Inference Example
```python
from medvision import MedVisionAI

model = MedVisionAI.from_pretrained("medvision/MedVisionAI")
result = model.analyze(image_path="chest_xray.dcm")
print(result.findings)
```

### Temperature
For diagnostic confidence calibration, we recommend setting the temperature parameter $T_{model}$ to 0.3.

### Integration with PACS Systems
For PACS integration, use the following configuration template:
```
pacs_config = {
    "ae_title": "MEDVISION_AI",
    "port": 11112,
    "storage_scp": true,
    "auto_routing": true
}
```

For real-time analysis pipelines, we recommend the following template:
```
analysis_pipeline = '''
1. Receive DICOM image from PACS
2. Preprocess and normalize image data
3. Run MedVisionAI inference
4. Generate structured report
5. Send results to referring physician
6. Archive analysis in long-term storage
'''
```

## 5. License
This model is licensed under the [Apache 2.0 License](LICENSE). Use in clinical settings requires appropriate regulatory clearance in your jurisdiction.

## 6. Contact
For clinical partnerships and research collaborations, please contact us at partnerships@medvisionai.health.