Overview
This 8B parameter multimodal medical model delivers advanced clinical reasoning across both text and medical imagery in a highly efficient footprint.
Trained on diverse medical thinking and patient-centered datasets, it understands complex clinical narratives while accurately interpreting X-rays, MRIs, CT scans, pathology slides, charts, diagrams, and structured medical records.
Despite its compact size, the model demonstrates strong diagnostic support capabilities, contextual question answering, structured report summarization, and multimodal evidence synthesis.
With a 32K context window, it can process long clinical documents and longitudinal patient histories. Optimized for retrieval-augmented generation (RAG) workflows, it integrates seamlessly with healthcare databases and imaging systems to provide grounded, data-aware responses across real-world medical environments.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- **Performance metrics for Real Time:** Instance Type: **ml.g5.12xlarge** **QA** * Text completion: up to 1000 tokens per second * Chat completion: up to 1100 tokens per second **Summarization** * Text Completion: up to 600 tokens per second * Chat Completion: up to 600 tokens per second
- **Performance metrics for Batch:** Instance Type: **ml.g5.12xlarge** **QA** * Text completion: up to 900 tokens per second **Summarization** * Text Completion: up to 600 tokens per second
- **Accuracy:** * Achieves 88.2% average across OpenMed benchmarks * Scores 89% on clinical knowledge assessment * Reaches 95% on medical genetics understanding * Performs at 95.2% for college biology concepts * Processes professional medicine with 93% accuracy * Handles medical MCQAs with 89.4% precision * Maintains 88.8% accuracy on Anatomy concepts
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $9.98 |
ml.g5.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.12xlarge instance type, real-time mode | $9.98 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
Optimized version , that understands medical text but also interprets visual information across scans, charts, diagrams, and structured documents.
Additional details
Inputs
- Summary
1. Chat Completion
Example Payload {
"model": "/opt/ml/model",
"messages": [
{"role": "system", "content": "You are a helpful medical assistant."},
{"role": "user", "content": "What should I do if I have a fever and body aches?"}
],
"max_tokens": 1024,
"temperature": 0.6
}For additional parameters:
2. Text Completion
Single Prompt Example {
"model": "/opt/ml/model",
"prompt": "How can I maintain good kidney health?",
"max_tokens": 512,
"temperature": 0.6
}Multiple Prompts Example {
"model": "/opt/ml/model",
"prompt": [
"How can I maintain good kidney health?",
"What are the best practices for kidney care?"
],
"max_tokens": 512,
"temperature": 0.6
}Reference:
3. Image + Text Inference
The model supports both online (direct URL) and offline (base64-encoded) image inputs.
Online Image Example { "model": "/opt/ml/model", "messages": [ {"role": "system", "content": "You are a helpful medical assistant."}, { "role": "user", "content": [ {"type": "text", "text": "What does this medical image show?"}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg "}} ] } ], "max_tokens": 2048, "temperature": 0.1 }
Offline Image Example (Base64)
{ "model": "/opt/ml/model", "messages": [ {"role": "system", "content": "You are a helpful medical assistant."}, { "role": "user", "content": [ {"type": "text", "text": "What does this medical image show?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}} ] } ], "max_tokens": 2048, "temperature": 0.1 }
Reference:
Important Notes:
- Streaming Responses: Add "stream": true to your request payload to enable streaming
- Model Path Requirement: Always set "model": "/opt/ml/model" (SageMaker's fixed model location)
- Input MIME type
- application/json
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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