MedGemma 1.5 Technical Report
Authors: name: Andrew Sellergren, name: Chufan Gao, name: Fereshteh Mahvar, name: Timo Kohlberger, name: Fayaz Jamil, name: Madeleine Traverse, name: Alberto Tono, name: Bashir Sadjad, name: Lin Yang, name: Charles Lau, name: Liron Yatziv, name: Tiffany Chen, name: Bram Sterling, name: Kenneth Philbrick, name: Richa Tiwari, name: Yun Liu, name: Madhuram Jajoo, name: Chandrashekar Sankarapu, name: Swapnil Vispute, name: Harshad Purandare, name: Abhishek Bijay Mishra, name: Sam Schmidgall, name: Tao Tu, name: Anil Palepu, name: Chunjong Park, name: Tim Strother, name: Rahul Thapa, name: Yong Cheng, name: Preeti Singh, name: Kat Black, name: Yossi Matias, name: Katherine Chou, name: Avinatan Hassidim, name: Kavi Goel, name: Joelle Barral, name: Tris Warkentin, name: Shravya Shetty, name: Dale Webster, name: Sunny Virmani, name: David F. Steiner, name: Can Kirmizibayrak, name: Daniel Golden

We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imaging (CT/MRI volumes and histopathology whole slide images), anatomical localization via bounding boxes, multi-timepoint chest X-ray analysis, and improved medical document understanding (lab reports, electronic health records). We detail the innovations required to enable these modalities within a single architecture, including new training data, long-context 3D volume slicing, and whole-slide pathology sampling. Compared to MedGemma 1 4B, MedGemma 1.5 4B demonstrates significant gains in these new areas, improving 3D MRI condition classification accuracy by 11% and 3D CT condition classification by 3% (absolute improvements). In whole slide pathology imaging, MedGemma 1.5 4B achieves a 47% macro F1 gain. Additionally, it improves anatomical localization with a 35% increase in Intersection over Union on chest X-rays and achieves a 4% macro accuracy for longitudinal (multi-timepoint) chest x-ray analysis. Beyond its improved multimodal performance over MedGemma 1, MedGemma 1.5 improves on text-based clinical knowledge and reasoning, improving by 5% on MedQA accuracy and 22% on EHRQA accuracy. It also achieves an average of 18% macro F1 on 4 different lab report information extraction datasets (EHR Datasets 2, 3, 4, and Mendeley Clinical Laboratory Test Reports). Taken together, MedGemma 1.5 serves as a robust, open resource for the community, designed as an improved foundation on which developers can create the next generation of medical AI systems. Resources and tutorials for building upon MedGemma 1.5 can be found at https://goo.gle/MedGemma.