Paper

MedGemma Technical Report

Ramé, Alexandre, Yatziv, Liron, Barral, Joelle, Merhej, Ramona, Kirmizibayrak, Can, Jean-bastien Grill, Kohlberger, Timo, Joulin, Armand, Jaroensri, Tiam, Dadashi, Robert, Feinberg, Vlad, Chou, Katherine, Vieillard, Nino, Kamath, Aishwarya, Mahvar, Fereshteh, Chen, Justin, Kazemzadeh, Sahar, Léonard Hussenot, Lu, Yang, Pilgrim, Rory, Liu, Yun, Sanseviero, Omar, Schmidgall, Samuel, Matejovicova, Tatiana, Tiwari, Richa, Singh, Preeti, Philbrick, Kenneth, Lai, Jeremy, Hughes, Cían, Martins, Gus, Chen, Tiffany, Borgeaud, Sebastian, Azizi, Shekoofeh, Bachem, Olivier, Yvinec, Edouard, Bjornsson, Per, Cassidy Hardin, Pathak, Shreya, Baby, Stefanie Anna, Mezerreg, Ines, Hassidim, Avinatan, Chen, Kejia, Asiedu, Mercy, Cotruta, Victor, Warkentin, Tris, Shlens, Jonathon, Kirk, Phoebe, Steiner, David F, Kiraly, Atilla, Rouillard, Louis, Andreev, Alek, Jean-Baptiste Alayrac, Lau, Charles, Yang, Howard, Buchatskaya, Elena, Golden, Daniel, Fleet, David, Ramos, Sabela, Baby, Susanna Maria, Hu, Howard, Riviere, Morgane, Traverse, Madeleine, Cideron, Geoffrey, Shetty, Shravya, Sterling, Bram, Lepikhin, Dmitry, Fayaz Jamil, Xu, Shawn, Se

2026

Overview

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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Artificial intelligence / Classification / Coders / Electronic health records / Error reduction / Health care / Information retrieval / Medical imaging / Medical research / Performance enhancement / Pneumothorax / R&D / Research & development / Task complexity / Vision