MedGemma 1.5 Technical Report
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.
69d5c32db9b236b710ae8152
Andrew Sellergren
69d5c32db9b236b710ae8153
Chufan Gao
69d5c32db9b236b710ae8154
Fereshteh Mahvar
69d5c32db9b236b710ae8155
Timo Kohlberger
69d5c32db9b236b710ae8156
Fayaz Jamil
69d5c32db9b236b710ae8157
Madeleine Traverse
69d5c32db9b236b710ae8158
Alberto Tono
69d5c32db9b236b710ae8159
Bashir Sadjad
69d5c32db9b236b710ae815a
Lin Yang
69d5c32db9b236b710ae815b
Charles Lau
69d5c32db9b236b710ae815c
Liron Yatziv
69d5c32db9b236b710ae815d
Tiffany Chen
69d5c32db9b236b710ae815e
Bram Sterling
69d5c32db9b236b710ae815f
Kenneth Philbrick
69d5c32db9b236b710ae8160
Richa Tiwari
69d5c32db9b236b710ae8161
Yun Liu
69d5c32db9b236b710ae8162
Madhuram Jajoo
69d5c32db9b236b710ae8163
Chandrashekar Sankarapu
69d5c32db9b236b710ae8164
Swapnil Vispute
69d5c32db9b236b710ae8165
Harshad Purandare
69d5c32db9b236b710ae8166
Abhishek Bijay Mishra
69d5c32db9b236b710ae8167
Sam Schmidgall
69d5c32db9b236b710ae8168
Tao Tu
69d5c32db9b236b710ae8169
Anil Palepu
69d5c32db9b236b710ae816a
Chunjong Park
69d5c32db9b236b710ae816b
Tim Strother
69d5c32db9b236b710ae816c
Rahul Thapa
69d5c32db9b236b710ae816d
Yong Cheng
69d5c32db9b236b710ae816e
Preeti Singh
69d5c32db9b236b710ae816f
Kat Black
69d5c32db9b236b710ae8170
Yossi Matias
69d5c32db9b236b710ae8171
Katherine Chou
69d5c32db9b236b710ae8172
Avinatan Hassidim
69d5c32db9b236b710ae8173
Kavi Goel
69d5c32db9b236b710ae8174
Joelle Barral
69d5c32db9b236b710ae8175
Tris Warkentin
69d5c32db9b236b710ae8176
Shravya Shetty
69d5c32db9b236b710ae8177
Dale Webster
69d5c32db9b236b710ae8178
Sunny Virmani
69d5c32db9b236b710ae8179
David F. Steiner
69d5c32db9b236b710ae817a
Can Kirmizibayrak
69d5c32db9b236b710ae817b
Daniel Golden
MedGemma 1.5 Technical Report
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.
MedGemma 1.5 4B enhances medical AI capabilities through expanded multimodal support and improved performance across medical imaging, document understanding, and clinical reasoning tasks.
medical imaging CT/MRI volumes histopathology whole slide images anatomical localization bounding boxes multi-timepoint chest X-ray analysis medical document understanding lab reports electronic health records long-context 3D volume slicing whole-slide pathology sampling 3D MRI condition classification 3D CT condition classification macro F1 Intersection over Union longitudinal chest X-ray analysis MedQA EHRQA lab report information extraction