MedGemma Technical Report
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Artificial intelligence (AI) has significant potential in healthcare
applications, but its training and deployment faces challenges due to
healthcare's diverse data, complex tasks, and the need to preserve privacy.
Foundation models that perform well on medical tasks and require less
task-specific tuning data are critical to accelerate the development of
healthcare AI applications. We introduce MedGemma, a collection of medical
vision-language foundation models based on Gemma 3 4B and 27B. MedGemma
demonstrates advanced medical understanding and reasoning on images and text,
significantly exceeding the performance of similar-sized generative models and
approaching the performance of task-specific models, while maintaining the
general capabilities of the Gemma 3 base models. For out-of-distribution tasks,
MedGemma achieves 2.6-10% improvement on medical multimodal question answering,
15.5-18.1% improvement on chest X-ray finding classification, and 10.8%
improvement on agentic evaluations compared to the base models. Fine-tuning
MedGemma further improves performance in subdomains, reducing errors in
electronic health record information retrieval by 50% and reaching comparable
performance to existing specialized state-of-the-art methods for pneumothorax
classification and histopathology patch classification. We additionally
introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP.
MedSigLIP powers the visual understanding capabilities of MedGemma and as an
encoder achieves comparable or better performance than specialized medical
image encoders. Taken together, the MedGemma collection provides a strong
foundation of medical image and text capabilities, with potential to
significantly accelerate medical research and development of downstream
applications. The MedGemma collection, including tutorials and model weights,
can be found at https://goo.gle/medgemma.
