MedGemma Technical Report

Ronnachai Jaroensri

Abstract:

Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment are challenging due to healthcare’s diverse data, complex spectrum of possible tasks, and the
need to preserve privacy. Foundation models that perform well on various medical tasks and require less
task-specific tuning data are critical to accelerating the development of AI for healthcare applications. In
this technical report, we introduce MedGemma, a new 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% improvements on
medical multimodal question answering, 15.5-18.1% improvements 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 type 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, it achieves performance comparable to or
better 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. More details about the MedGemma
collection, including tutorials and instructions for downloading the model weights, can be found at
https://goo.gle/medgemma.