MedGemma
医療テキストと画像解析のための高度なAIモデル
Dr7.ai Medical AIプラットフォーム提供
Google DeepMindの最先端MedGemma AIモデルで次世代のヘルスケアアプリケーションを強化。
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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MedGemmaとは
MedGemma MedGemmaは、医療テキストと画像を理解し処理するために特別に設計された最先端のAIモデルのコレクションです。Google DeepMindによって開発されたMedGemmaは、医療人工知能分野における重要な進歩を表しています。
強力なGemma 3アーキテクチャに基づいて構築されたMedGemmaは、ヘルスケアアプリケーション向けに最適化されており、開発者にMedGemmaを使用した革新的な医療ソリューションを作成するための堅牢なツールを提供します。
Health AI Developer Foundationsの一部として、MedGemmaは高度な医療AI技術へのアクセスを民主化し、世界中の研究者と開発者がMedGemmaを使用してより効果的なヘルスケアアプリケーションを構築できるようにすることを目指しています。
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.
よくある質問
MedGemmaに関するよくある質問
4Bマルチモーダルと27Bテキスト専用MedGemmaモデルの主な違いは何ですか？
4Bマルチモーダルモデルは医療画像とテキストを処理し、27Bモデルはテキスト処理と臨床推論に特化しています。
MedGemmaモデルは即座に臨床使用できますか？
いいえ、MedGemmaモデルは本番環境での展開前に検証と改善が必要です。