Powering the next generation of healthcare applications with Google DeepMind's cutting-edge MedGemma AI models for medical understanding.
Experience the power of MedGemma 4B IT model for medical text and image analysis
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.
Launched at Google I/O 2025
Released as part of Google's ongoing efforts to enhance healthcare through technology
Powerful capabilities designed for medical applications
Processes both medical images and text with 4 billion parameters, using a SigLIP image encoder pre-trained on de-identified medical data.
Optimized for deep medical text comprehension and clinical reasoning with 27 billion parameters.
Build AI-based applications that examine medical images, generate reports, and triage patients.
Accelerate research with open access to advanced AI through Hugging Face and Google Cloud.
Enhance patient interviewing and clinical decision support for improved healthcare efficiency.
Implementation guides and adaptation methods
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")
Use few-shot examples and break tasks into subtasks to enhance performance.
Optimize using your own medical data with resources like GitHub notebooks.
Integrate with tools like web search, FHIR generators, and Gemini Live.
Choose the right deployment method based on your requirements:
Run models locally for experimentation and development purposes.
Deploy as scalable HTTPS endpoints on Vertex AI through Model Garden for production-grade applications.
MedGemma models are not clinical-grade out of the box. Developers must validate performance and make necessary improvements before deploying in production environments.
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.
Common questions about MedGemma
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.
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.
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.
The 4B multimodal model is pre-trained on diverse medical images including chest X-rays, dermatology images, ophthalmology images, and histopathology slides, making it adaptable for various medical imaging 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.
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.
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.
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.
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.
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.