MedGemma model card

Model documentation: MedGemma

Resources:

• Model on Google Cloud Model Garden: MedGemma

• Model on Hugging Face: MedGemma

• GitHub repository (supporting code, Colab notebooks, discussions, and issues): MedGemma

• Quick start notebook: GitHub

• Fine-tuning notebook: GitHub

• Patient Education Demo built using MedGemma

• Support: See Contact

• License: The use of MedGemma is governed by the Health AI Developer Foundations terms of use.

Author: Google

Model information

This section describes the MedGemma model and how to use it.

Description

MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version.

MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images.

MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.

MedGemma 27B has been trained exclusively on medical text and optimized for inference-time computation. MedGemma 27B is only available as an instruction-tuned model.

MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These include both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended Use section below for more details.

A full technical report will be available soon.

How to use

Below are some example code snippets to help you quickly get started running the model locally on GPU. If you want to use the model at scale, we recommend that you create a production version using Model Garden.