MedGemma
نماذج الذكاء الاصطناعي المتقدمة لتحليل النصوص والصور الطبية
مدعوم بمنصة Dr7.ai للذكاء الاصطناعي الطبي
تشغيل الجيل القادم من تطبيقات الرعاية الصحية بنماذج الذكاء الاصطناعي MedGemma المتطورة من Google DeepMind للفهم الطبي.
جرب العرض التفاعلي لـ MedGemma
اختبر قوة نموذج MedGemma 4B IT لتحليل النصوص والصور الطبية المتقدم
جرب نماذج أخرى
عرض الكلعرض MedGemma
اختبر المساعد الطبي بالذكاء الاصطناعي
هل تريد ميزات أقوى للتشخيص الطبي بالذكاء الاصطناعي؟
قم بالترقية إلى إصدار Pro لفتح نماذج ذكاء اصطناعي متقدمة، ومحادثات غير محدودة، وتحليل احترافي للصور الطبية، وميزات أقوى
ما هو 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.
أحدث التطورات
Launched at Google I/O 2025
Released as part of Google's ongoing efforts to enhance healthcare through technology
الميزات
قدرات قوية مصممة للتطبيقات الطبية
إصدارات نموذج MedGemma
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
مقارنة الأداء
حالات استخدام 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
الوصول إلى نماذج MedGemma
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")طرق التكييف
هندسة الموجّهات
استخدم أمثلة قليلة (few-shot) وقسّم المهام إلى مهام فرعية لتحسين الأداء.
الضبط الدقيق
حسّن النموذج باستخدام بياناتك الطبية الخاصة مع موارد مثل دفاتر GitHub.
التنسيق القائم على الوكلاء
Integrate with tools like web search, FHIR generators, and Gemini Live.
خيارات النشر
اختر طريقة النشر المناسبة وفقًا لمتطلباتك:
النشر المحلي
شغّل النماذج محليًا لأغراض التجربة والتطوير.
النشر السحابي
Deploy as scalable HTTPS endpoints on Vertex AI through Model Garden for production-grade applications.
اعتبارات التنفيذ
متطلبات التحقق
MedGemma models are not clinical-grade out of the box. Developers must validate performance and make necessary improvements before deploying in production environments.
شروط الاستخدام
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.
هل لديك صورة لعَرَض ظاهر؟
النماذج الموصوفة في هذه الصفحة أدوات بحثية للتصوير الطبي السريري. أما إذا كانت لديك صورة عادية — طفح جلدي أو شامة أو ظفر أو جرح — فإن AI Doctor Scan يقرؤها ويعطيك مجانًا مستوى الاستعجال وملخصًا بلغة بسيطة خلال نحو 30 ثانية. لا يقدّم تشخيصًا، ولا يحتاج إلى حساب.
جرّب AI Doctor Scan مجانًاFAQ
Common questions about MedGemma
What are the key differences between the 4B multimodal and 27B text-only MedGemma models?
The 4B multimodal model processes both medical images and text with 4 billion parameters, using a SigLIP image encoder. The 27B text-only model focuses exclusively on text processing with 27 billion parameters, optimized for deeper medical text comprehension and clinical reasoning.
Are MedGemma models ready for clinical use out of the box?
No, MedGemma models are not considered clinical-grade out of the box. Developers must validate their performance and make necessary improvements before deploying in production environments, especially for applications involving patient care.
How can I access MedGemma models for my development work?
MedGemma models are accessible on platforms like Hugging Face and Google Cloud, subject to the terms of use by the Health AI Developer Foundations. You can run them locally for experimentation or deploy them via Google Cloud for production-grade applications.
ما أنواع الصور الطبية التي يمكن لنموذج 4B متعدد الوسائط معالجتها؟
دُرِّب نموذج 4B متعدد الوسائط مسبقًا على صور طبية متنوعة، منها صور الأشعة السينية للصدر، وصور الأمراض الجلدية، وصور طب العيون، وشرائح علم الأنسجة المرضية، مما يجعله قابلًا للتكييف مع مهام تصوير طبي متعددة.
What adaptation methods can improve MedGemma's performance for specific tasks?
Developers can use prompt engineering (few-shot examples), fine-tuning with their own medical data, and agentic orchestration with tools like web search, FHIR generators, and Gemini Live to enhance performance for specific use cases.
When was MedGemma released and by whom?
MedGemma was officially launched around May 20-22, 2025, during Google I/O 2025 by Google DeepMind, as part of their ongoing efforts to enhance healthcare through technology.
How does MedGemma compare to similar models of its size?
According to its model card on Google Developers, MedGemma's baseline performance is strong compared to similar-sized models. It has been evaluated on clinically relevant benchmarks, including open datasets and curated datasets, with a focus on expert human evaluations for tasks.
Are there any resources available for fine-tuning MedGemma?
Yes, resources including notebooks on GitHub are available to facilitate fine-tuning, such as a fine-tuning example using LoRA available at Google's MedGemma GitHub repository.
What are the hardware requirements for running MedGemma models?
The hardware requirements depend on the model variant. According to posts from Google AI, MedGemma models are designed to be efficient, with the ability to run fine-tuning and inference on a single GPU, making them more accessible than some larger models.
Does MedGemma support multilingual medical terminology?
Based on community discussions, there are questions about MedGemma's performance with non-English medical terminology, such as Japanese medical terms. This suggests that multilingual support may vary and could be an area for future improvement or fine-tuning.