Sold by: John Snow LabsÂ
Deployed on AWS
Medical LLM reasoning model optimized for clinical reasoning, designed to elaborate the thought processes, considering multiple hypotheses, evaluating evidence systematically, and explaining conclusions transparently.
Overview
The LLM Reasoning Model 32 B marks a transformative leap in AI-driven clinical support by focusing on clinical reasoning over mere knowledge recall. Unlike traditional models that serve primarily as reference tools, this advanced model functions as a cognitive assistant, designed to aid healthcare professionals in making intricate diagnostic and treatment decisions. It meticulously processes patient symptoms, test results, and medical histories, employing structured reasoning patterns to recommend subsequent actions aligned with clinical guidelines. Key benefits include: - Transparent Decision Pathways: Provides clear and comprehensible explanations of how conclusions are reached, enhancing trust and reliability. - Consideration of Alternatives: Evaluates multiple hypotheses to ensure thorough analysis and diagnostic accuracy. - Uncertainty Acknowledgment: Recognizes and communicates the inherent uncertainties in medical diagnosis, which is crucial for risk management and decision-making. - Medical Knowledge Integration: Seamlessly incorporates vast medical knowledge, ensuring that all recommendations are up-to-date and evidence-based. - Structured Reasoning Patterns: Uses established clinical reasoning frameworks to simulate the thought processes of seasoned clinicians.
The Medical LLM Reasoner 32B outperforms other leading models across most categories, with particularly strong performance in clinical knowledge, professional medicine, and medical education domains.
Our benchmarking shows that the 32B model achieves 95-97% of the reasoning performance of larger models while generating tokens at approximately half the computational cost.
This model represents a significant step forward in equipping healthcare professionals with a tool that supports complex decision-making with precision and depth, mirroring a clinician approach to patient care.
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
- **Key Metrics on Medical Knowledge and Reasoning Tasks** **Clinical Knowledge Application** - Clinical Knowledge benchmark: 87.04% - Professional Medicine: 90.07% - Medical Genetics: 93.0% - Outstanding clinical comprehension (94.40%) , exceeding comparable to top-tier models - Superior medical reasoning (90%) comparable to top-tier models
- **Model Size benefits** - Cost Efficiency - Response Latency - Deployment Flexibility
- **Performance metrics for Real Time:** Instance Type: ml.p4d.24xlarge **QA** * Text completion: up to 1370 tokens per second * Chat completion: up to 1450 tokens per second **Summarization** * Text Completion: up to 360 tokens per second * Chat Completion: up to 400 tokens per second **Performance metrics for Batch:** Instance Type: ml.g5.48xlarge **QA** * Text completion: up to 295 tokens per second **Summarization** * Text Completion: up to 55 tokens per second
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $19.96 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $19.96 |
ml.g5.48xlarge Inference (Real-Time) | Model inference on the ml.g5.48xlarge instance type, real-time mode | $19.96 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $19.96 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $19.96 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $19.96 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $19.96 |
Vendor refund policy
No refunds are possible.
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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
Clinical Knowledge benchmark: 87.04% Professional Medicine: 90.07% Medical Genetics: 93.0% Outstanding clinical comprehension (93.75%), exceeding Med-PaLM-2's 88.3% Superior medical reasoning (90%)
Additional details
Inputs
- Summary
Input Format
- 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:
- 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:
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-Reasoning-LLM-32B/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-Reasoning-LLM-32B/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
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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