MedGemma
醫療文本和圖像分析的先進AI模型
使用Google DeepMind的尖端MedGemma AI模型為下一代醫療保健應用提供動力。
MedGemmaインタラクティブデモを試す
医療テキストと画像分析のためのMedGemma 4B ITモデルの力を体験
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What is MedGemma
MedGemma MedGemma is a collection of cutting-edge AI models designed specifically to understand and process medical text and images. Developed by Google DeepMind and announced in May 2025, MedGemma represents a significant advancement in the field of medical artificial intelligence.
Built on the powerful Gemma 3 architecture, MedGemma has been optimized for healthcare applications, providing developers with robust tools to create innovative medical solutions.
As part of the Health AI Developer Foundations, MedGemma aims to democratize access to advanced medical AI technology, enabling researchers and developers worldwide to build more effective healthcare applications.
Recent Development
Launched at Google I/O 2025
Released as part of Google's ongoing efforts to enhance healthcare through technology
Features
Powerful capabilities designed for medical applications
MedGemma Model Variants
4B Multimodal Model
Processes both medical images and text with 4 billion parameters, using a SigLIP image encoder pre-trained on de-identified medical data.
27B Text-Only Model
Optimized for deep medical text comprehension and clinical reasoning with 27 billion parameters.
Key Capabilities
- ✓Medical image classification (radiology, pathology, etc.)
- ✓Medical image interpretation and report generation
- ✓Medical text comprehension and clinical reasoning
- ✓Patient preclinical interviews and triaging
- ✓Clinical decision support and summarization
Performance Comparison
Use Cases for MedGemma
Healthcare Application Development
Build AI-based applications that examine medical images, generate reports, and triage patients.
Medical Research and Innovation
Accelerate research with open access to advanced AI through Hugging Face and Google Cloud.
Clinical Support Roles
Enhance patient interviewing and clinical decision support for improved healthcare efficiency.
How to Use
Implementation guides and adaptation methods
Access MedGemma Models
MedGemma models are accessible on platforms like Hugging Face, subject to the terms of use by the Health AI Developer Foundations.
# Example Python code to load MedGemma model
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/medgemma-4b-it")
model = AutoModelForCausalLM.from_pretrained("google/medgemma-4b-it")Adaptation Methods
Prompt Engineering
Use few-shot examples and break tasks into subtasks to enhance performance.
Fine-Tuning
Optimize using your own medical data with resources like GitHub notebooks.
Agentic Orchestration
Integrate with tools like web search, FHIR generators, and Gemini Live.
Deployment Options
Choose the right deployment method based on your requirements:
Local Deployment
Run models locally for experimentation and development purposes.
Cloud Deployment
Deploy as scalable HTTPS endpoints on Vertex AI through Model Garden for production-grade applications.
Implementation Considerations
Validation Requirements
MedGemma models are not clinical-grade out of the box. Developers must validate performance and make necessary improvements before deploying in production environments.
Terms of Use
The use of MedGemma is governed by the Health AI Developer Foundations terms of use, which developers must review and agree to before accessing models.
常見問題
關於MedGemma的常見問題
4B多模態和27B純文本MedGemma模型的主要區別是什麼？
4B多模態模型處理醫療圖像和文本，而27B模型專注於文本處理和臨床推理。
MedGemma模型可以立即用於臨床嗎？
不，MedGemma模型需要在生產環境中部署前進行驗證和改進。