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Deployed on AWS
Initial release of MedGemma 27B Multimodal (google/medgemma-27b-it) on AWS Marketplace. This version delivers Google's most capable open medical AI model, supporting both medical image comprehension and clinical text reasoning including FHIR-based EHR data, across radiology, dermatology, histopathology, and ophthalmology modalities.
Overview
MedGemma 27B Multimodal (google/medgemma-27b-it) is the largest and most capable variant in Google's MedGemma family - a collection of Gemma 3-based models purpose-trained for medical text and image comprehension.
This listing packages the model as a fully managed Amazon SageMaker real-time endpoint, deployable with a single click. No infrastructure setup, no model packaging, and no HuggingFace dependencies at runtime.
Key capabilities:
- Medical image comprehension: chest X-ray, dermatology, histopathology, ophthalmology, and radiology image analysis
- Clinical text reasoning: medical question answering, differential diagnosis support, and clinical summarization
- FHIR-based EHR understanding: the only MedGemma variant trained on FHIR electronic health record data, enabling structured patient record comprehension
- Test-time scaling: optimized inference-time computation for stronger medical reasoning vs. the 4B variant Use cases supported:
- Clinical decision support tools
- Radiology report generation and review assistance
- Dermatology image triage
- EHR data extraction and summarization
- Medical education and training applications
- Healthcare application prototyping and research Important: This model is intended to assist healthcare developers and researchers. It is not a certified medical device and should not be used as a standalone clinical diagnostic tool. Regulatory compliance is the responsibility of the deploying organization.
This product is governed by the Google Health AI Developer Foundations (HAI-DEF) Terms of Use. By subscribing, you agree to flow down these restrictions to your end users.
Highlights
- Multimodal medical AI in one click: MedGemma 27B Multimodal - Google's most capable open medical model - deployed as a fully managed SageMaker endpoint. Supports radiology, dermatology, histopathology, and ophthalmology image analysis alongside clinical text reasoning. No infrastructure setup required.
- OpenAI-Compatible API via vLLM - Zero SDK Changes for Your App: Served by vLLM with a native /v1/chat/completions endpoint. Integrate using the OpenAI Python SDK, LangChain, or any HTTP client - no custom wrappers needed.
- Deploy in minutes: Zero infrastructure management
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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 | $0.10 |
ml.g7e.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g7e.2xlarge instance type, real-time mode | $0.10 |
ml.g5.24xlarge Inference (Batch) | Model inference on the ml.g5.24xlarge instance type, batch mode | $0.10 |
ml.g6.12xlarge Inference (Real-Time) | Model inference on the ml.g6.12xlarge instance type, real-time mode | $0.10 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $0.10 |
ml.g7e.4xlarge Inference (Real-Time) | Model inference on the ml.g7e.4xlarge instance type, real-time mode | $0.10 |
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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
Deploys Google's MedGemma 27B Multimodal as a managed Amazon SageMaker real-time endpoint. This release includes support for medical image comprehension (radiology, dermatology, histopathology, ophthalmology) and clinical text reasoning, including FHIR-based EHR data understanding. Supports real-time inference and batch transform on ml.g5.12xlarge and above.
Additional details
Inputs
- Summary
Accepts JSON payloads via the /invocations endpoint. Supports two modes: (1) text-only, providing a prompt string under the "inputs" key; (2) multimodal, providing a messages array with interleaved text and base64-encoded image content following the Gemma chat template format. Supported image types: JPEG, PNG. Maximum image size: 1024×1024 pixels. Content-Type must be application/json.
- Limitations for input type
- Maximum prompt length: 8,192 tokens. Images must be base64-encoded and embedded inline; external image URLs are not supported (no network access at inference time). Supported content types: application/json only. Batch transform inputs must use JSON Lines format (.jsonl), one JSON object per line.
- Input MIME type
- application/json
{
"inputs": {
"messages": [
{
"role": "user",
"content": [
{
"type": "image",
"image": "data:image/jpeg;base64,<BASE64_ENCODED_IMAGE>"
},
{
"type": "text",
"text": "Describe the findings in this chest X-ray and identify any abnormalities."
}
]
}
]
},
"parameters": {
"max_new_tokens": 512,
"temperature": 0.3
}
}
{"inputs": {"messages": [{"role": "user", "content": [{"type": "image", "image": "data:image/jpeg;base64,<BASE64_IMAGE_1>"}, {"type": "text", "text": "Describe the findings in this chest X-ray."}]}]}, "parameters": {"max_new_tokens": 512, "temperature": 0.3}}
{"inputs": {"messages": [{"role": "user", "content": [{"type": "image", "image": "data:image/jpeg;base64,<BASE64_IMAGE_2>"}, {"type": "text", "text": "Identify any abnormalities visible in this MRI scan."}]}]}, "parameters": {"max_new_tokens": 512, "temperature": 0.3}}
{"inputs": {"messages": [{"role": "user", "content": [{"type": "image", "image": "data:image/png;base64,<BASE64_IMAGE_3>"}, {"type": "text", "text": "What dermatological condition does this image suggest?"}]}]}, "parameters": {"max_new_tokens": 512, "temperature": 0.3}}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inputs | Text prompt string, or a messages array for multimodal inputs | Max 8,192 tokens | Yes |
max_new_tokens | Maximum number of tokens to generate in the response | 1–2048. Default: 512 | No |
temperature | Controls randomness. Lower values produce more deterministic output | 0.0–1.0. Default: 0.3 | No |
top_p | Nucleus sampling probability threshold | 0.0–1.0. Default: 0.9 | No |
top_k | Limits vocabulary to the top-k most likely tokens at each step | 1–100. Default: 50 | No |
return_full_text | If true, the input prompt is prepended to the generated output | Default: false | No |
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