Sold by: John Snow Labs
Deployed on AWS
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This 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.
Overview
This 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.
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..
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 on Instance Type ml.g5.12xlarge QA: >> Text completion: up to 600 tokens per second; >>Chat completion: up to 600 tokens per second Summarization: >> Text Completion: up to 250 tokens per second; >> Chat Completion: up to 250 tokens per second
- Performance metrics for Batch on Instance Type ml.g5.12xlarge QA: >> Text completion: up to 800 tokens per second Summarization: >>Text Completion: up to 300 tokens per second
- Accuracy: >> Achieves 90.8% average across OpenMed benchmarks; >> Scores 87% on clinical knowledge assessment: >> Reaches 98% on medical genetics understanding: >> Performs at 97.8% for college biology concepts: >> Processes professional medicine with 93.5% accuracy: >> Handles medical MCQs with 92% precision: >> Maintains 89.8% accuracy on Anatomy concepts
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $9.98 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $5.94 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $5.94 |
ml.g4dn.12xlarge Inference (Real-Time) | Model inference on the ml.g4dn.12xlarge instance type, real-time mode | $9.98 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $5.94 |
ml.g4dn.xlarge Inference (Real-Time) | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $5.94 |
Dimensions summary
You pay by the hour for model inference, billed per host hour of usage. Both dimensions run on the same ml.g5.12xlarge GPU instance type. The difference is how you process work. Batch mode handles grouped requests together, suited to processing large volumes at once. Real-time mode serves individual requests as they arrive, suited to interactive or on-demand queries. You choose the mode that fits your workload, and charges accrue only while the instance runs. There is no upfront commitment; costs scale with the number of hours you keep each instance active.
Top-of-mind questions for buyers
You get a single GPU-backed instance sized for medical language model inference. The model runs privately inside your own AWS environment, with no external API calls and no data sent to the vendor. You pay per host hour that instance stays active, regardless of how many documents you process.
Software charges accrue per host hour only while the instance runs. A fully stopped instance stops software metering. Underlying AWS storage or reserved-capacity fees may still apply separately from this listing. To avoid the hourly software charge, shut the instance down when not processing work.
No limit is imposed on the number of characters, words, or documents processed. Both modes bill by host hour, not by volume. Batch mode groups requests for large volumes at once. Real-time mode serves individual requests as they arrive. Cost depends on hours the instance runs, not throughput.
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Model Optimizatiom
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": 0.7
}
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
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-VLM-8B/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-VLM-8B/inputs/batch
Resources
Support
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For any assistance, please reach out to support@johnsnowlabs.com .
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