Sold by: John Snow LabsÂ
Deployed on AWS
Free Trial
Use for tasks like medical summarization or open-book question answering with context of up to 40K tokens.
Overview
Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming detailed clinical notes, patient encounters, and various medical reports into concise, digestible summaries. The summarization feature boosts efficiency while preserving critical details, supporting optimal patient care. It introduces a dedicated reasoning mode that can follow multi-step clinical logic and justify its answers.
Its question-answering capability ensures accurate, context-specific responses to both open and closed medical queries, further enhancing decision-making.
For physicians, this tool offers a quick grasp of a patient medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient journey, condition, and treatment protocols swiftly.
Optimized for Retrieval-Augmented Generation (RAG), the model can be used in combination with healthcare databases, EHR, and scientific literature repositories (like PubMed) to enhance response quality.
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 620 tokens per second * Chat completion: up to 645 tokens per second **Summarization** * Text Completion: up to 88 tokens per second * Chat Completion: up to 130 tokens per second
- **Performance metrics for Batch:** Instance Type: ml.g5.12xlarge **QA** * Text completion: up to 500 tokens per second **Summarization** * Text Completion: up to 115 tokens per second
- **Accuracy:** - Achieves 81.42% average, competing with GPT-4 (82.85%) - Outstanding clinical comprehension (93.40%), exceeding Med-PaLM-2's 88.3% - Superior medical reasoning (90%) comparable to top-tier models - Outperforms Meditron-70B despite being 5x smaller - State-of-the-art performance in medical tasks while maintaining deployment efficiency
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 |
Vendor refund policy
No refunds are possible.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Dedicated reasoning mode that can follow multi-step clinical logic and justify its answers.
Additional details
Inputs
- Summary
Input Format
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 see: ChatCompletionRequest OpenAI's Chat APIÂ
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 }
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
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-LLM-Small/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-LLM-Small/inputs/batch
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.
Similar products
Access 2,000+ state-of-the-art models by John Snow Labs for understanding clinical and biomedical text or visual documents, using a pay-as-you-go license.
Small and medium-sized businesses (SMBs) need an accessible and affordable way to use large language models (LLMs) without compromising data confidentiality. SnapSoft's application of Private GenAI Acceleration Program (PGAP) enables SMBs to build custom GenAI agents, compare the performance of different LLMs on AWS Bedrock, and evaluate the quality of private data sources. SMBs can build agents in their personal workspace without worrying about infrastructure, configuration, or scripting knowledge.
Chat with a Medical Generative AI that explains its answers, cites references, updates medical knowledge daily, and lets you add and choose knowledge bases. The Medical Chatbot is designed to provide real-time, evidence based information, and streamlines the research processes which offers relevant insights which are deeply rooted in the vast area of medical literature.
Use for chat, RAG, medical summarization, open-book question answering with context of up to 32K tokens.
Cutting-edge English - Spanish medical translation model.
BioT is the infrastructure for medical device clouds - the flexible, open, and secure foundation needed to build your cloud right and deliver more impactful care from the start.
With our new medical record summarization activity powered by Anthropic's Claude Opus model, UiPath can provide a clinician-level summarization of a medical record organized in easy-to-understand segments. Users can summarize everything from demographics to lab results to surgical histories while having complete control over the type of information summarized.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.