MedGemma

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 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,
dermatology images, and medical text.

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, with a focus on expert human
evaluations for tasks. Developers can fine tune MedGemma variants for improved
performance. Please read more about our work in our manuscript [link coming] and
consult our Intended Use Statement for more details.

Get started

• Read our
  developer documentation
  to see the full range of next steps available, including learning more about
  the model through its
  model card.

• Explore this repository, which contains notebooks for using
  the model.

• Visit the model on
  Hugging Face or
  Model Garden.

Contributing

We are open to bug reports, pull requests (PR), and other contributions. See
CONTRIBUTING and
community guidelines
for details.

License

While the model is licensed under the
Health AI Developer Foundations License,
everything in this repository is licensed under the Apache 2.0 license, see
LICENSE.