This section describes the MedGemma model and how to use it.
Description
MedGemma is a collection of Gemma 3
variants that are trained for performance on medical text and image
comprehension. Developers can use MedGemma to accelerate building
healthcare-based AI applications. MedGemma currently comes in two variants: a 4B
multimodal version and a 27B text-only version.
MedGemma 4B utilizes a SigLIP image encoder
that has been specifically pre-trained on a variety of de-identified medical
data, including chest X-rays, dermatology images, ophthalmology images, and
histopathology slides. Its LLM component is trained on a diverse set of medical
data, including radiology images, histopathology patches, ophthalmology images,
and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and
instruction-tuned (suffix -it) versions. The instruction-tuned version is a
better starting point for most applications. The pre-trained version is
available for those who want to experiment more deeply with the models.
MedGemma 27B has been trained exclusively on medical text and optimized for
inference-time computation. MedGemma 27B is only available as an
instruction-tuned model.
MedGemma variants have been evaluated on a range of clinically relevant
benchmarks to illustrate their baseline performance. These include both open
benchmark datasets and curated datasets. Developers can fine-tune MedGemma
variants for improved performance. Consult the Intended Use section below for
more details.
A full technical report will be available soon.
How to use
Below are some example code snippets to help you quickly get started running the
model locally on GPU. If you want to use the model at scale, we recommend that
you create a production version using Model
Garden.
First, install the Transformers library. Gemma 3 is supported starting from
transformers 4.50.0.
See the following Colab notebooks for examples of how to use MedGemma:
To give the model a quick try, running it locally with weights from Hugging
Face, see Quick start notebook in
Colab.
Note that you will need to use Colab Enterprise to run the 27B model without
quantization.
The MedGemma model is built based on Gemma 3 and
uses the same decoder-only transformer architecture as Gemma 3. To read more
about the architecture, consult the Gemma 3 model
card.
Context length: Supports long context, at least 128K tokens
Key publication: Coming soon
Model created: May 20, 2025
Model version: 1.0.0
Citation
A technical report is coming soon. In the meantime, if you publish using this
model, please cite the Hugging Face model page:
none
1@misc{medgemma-hf,
2 author = {Google},
3 title = {MedGemma Hugging Face}
4 howpublished = {\url{https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4}},
5 year = {2025},
6 note = {Accessed: [Insert Date Accessed, e.g., 2025-05-20]}
7}
Inputs and outputs
Input:
Text string, such as a question or prompt
Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
Total input length of 128K tokens
Output:
Generated text in response to the input, such as an answer to a question,
analysis of image content, or a summary of a document
Total output length of 8192 tokens
Performance and validation
MedGemma was evaluated across a range of different multimodal classification,
report generation, visual question answering, and text-based tasks.
Key performance metrics
Imaging evaluations
The multimodal performance of MedGemma 4B was evaluated across a range of
benchmarks, focusing on radiology, dermatology, histopathology, ophthalmology,
and multimodal clinical reasoning.
MedGemma 4B outperforms the base Gemma 3 4B model across all tested multimodal
health benchmarks.
4-way MCQ per example for skin condition classification.
**Based on "balanced split," described in Yang (2024,
arXiv).
***Based on multiple datasets, presented as 3-9 way MCQ per example for
identification, grading, and subtype for breast, cervical, and prostate cancer.
Chest X-ray report generation
MedGemma chest X-ray (CXR) report generation performance was evaluated on
MIMIC-CXR using the RadGraph
F1 metric. We compare the MedGemma
pre-trained checkpoint with our previous best model for CXR report generation,
PaliGemma 2.
Metric
MedGemma 4B (pre-trained)
PaliGemma 2 3B (tuned for CXR)
PaliGemma 2 10B (tuned for CXR)
Chest X-ray report generation
MIMIC CXR - RadGraph F1
29.5
28.8
29.5
The instruction-tuned versions of MedGemma 4B and Gemma 3 4B achieve lower
scores (0.22 and 0.12, respectively) due to the differences in reporting style
compared to the MIMIC ground truth reports. Further fine-tuning on MIMIC reports
will enable users to achieve improved performance.
Text evaluations
MedGemma 4B and text-only MedGemma 27B were evaluated across a range of
text-only benchmarks for medical knowledge and reasoning.
The MedGemma models outperform their respective base Gemma models across all
tested text-only health benchmarks.
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
Child safety: Evaluation of text-to-text and image-to-text prompts
covering child safety policies, including child sexual abuse and
exploitation.
Content safety: Evaluation of text-to-text and image-to-text prompts
covering safety policies, including harassment, violence and gore, and hate
speech.
Representational harms: Evaluation of text-to-text and image-to-text
prompts covering safety policies, including bias, stereotyping, and harmful
associations or inaccuracies.
General medical harms: Evaluation of text-to-text and image-to-text
prompts covering safety policies, including information quality and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance evaluations"
which are our "arms-length" internal evaluations for responsibility governance
decision making. They are conducted separately from the model development team,
to inform decision making about release. High-level findings are fed back to the
model team, but prompt sets are held out to prevent overfitting and preserve the
results' ability to inform decision making. Notable assurance evaluation results
are reported to our Responsibility & Safety Council as part of release review.
Evaluation results
For all areas of safety testing, we saw safe levels of performance across the
categories of child safety, content safety, and representational harms. All
testing was conducted without safety filters to evaluate the model capabilities
and behaviors. For text-to-text, image-to-text, and audio-to-text, and across
both MedGemma model sizes, the model produced minimal policy violations. A
limitation of our evaluations was that they included primarily English language
prompts.
Data card
Dataset overview
Training
The base Gemma models are pre-trained on a large corpus of text and code data.
MedGemma 4B utilizes a SigLIP image encoder
that has been specifically pre-trained on a variety of de-identified medical
data, including radiology images, histopathology images, ophthalmology images,
and dermatology images. Its LLM component is trained on a diverse set of medical
data, including medical text relevant to radiology images, chest-x rays,
histopathology patches, ophthalmology images and dermatology images.
Evaluation
MedGemma models have been evaluated on a comprehensive set of clinically
relevant benchmarks, including over 22 datasets across 5 different tasks and 6
medical image modalities. These include both open benchmark datasets and curated
datasets, with a focus on expert human evaluations for tasks like CXR report
generation and radiology VQA.
Source
MedGemma utilizes a combination of public and private datasets.
This model was trained on diverse public datasets including MIMIC-CXR (chest
X-rays and reports), Slake-VQA (multimodal medical images and questions),
PAD-UFES-20 (skin lesion images and data), SCIN (dermatology images), TCGA
(cancer genomics data), CAMELYON (lymph node histopathology images), PMC-OA
(biomedical literature with images), and Mendeley Digital Knee X-Ray (knee
X-rays).
Additionally, multiple diverse proprietary datasets were licensed and
incorporated (described next).
Data Ownership and Documentation
Mimic-CXR: MIT Laboratory
for Computational Physiology and Beth Israel Deaconess Medical Center
(BIDMC).
Slake-VQA: The Hong Kong Polytechnic
University (PolyU), with collaborators including West China Hospital of
Sichuan University and Sichuan Academy of Medical Sciences / Sichuan
Provincial People's Hospital.
PAD-UFES-20: Federal
University of Espírito Santo (UFES), Brazil, through its Dermatological and
Surgical Assistance Program (PAD).
SCIN: A collaboration
between Google Health and Stanford Medicine.
TCGA (The Cancer Genome Atlas): A joint
effort of National Cancer Institute and National Human Genome Research
Institute. Data from TCGA are available via the Genomic Data Commons (GDC)
CAMELYON: The data was
collected from Radboud University Medical Center and University Medical
Center Utrecht in the Netherlands.
PMC-OA (PubMed Central Open Access
Subset):
Maintained by the National Library of Medicine (NLM) and National Center for
Biotechnology Information (NCBI), which are part of the NIH.
MedQA: This dataset was created by a
team of researchers led by Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung
Weng, Hanyi Fang, and Peter Szolovits
Mendeley Digital Knee
X-Ray: This dataset is
from Rani Channamma University, and is hosted on Mendeley Data.
AfriMed-QA: This data was developed and led by
multiple collaborating organizations and researchers include key
contributors: Intron Health, SisonkeBiotik, BioRAMP, Georgia Institute of
Technology, and MasakhaneNLP.
VQA-RAD: This dataset was
created by a research team led by Jason J. Lau, Soumya Gayen, Asma Ben
Abacha, and Dina Demner-Fushman and their affiliated institutions (the US
National Library of Medicine and National Institutes of Health)
MedExpQA:
This dataset was created by researchers at the HiTZ Center (Basque Center
for Language Technology and Artificial Intelligence).
MedXpertQA: This
dataset was developed by researchers at Tsinghua University (Beijing, China)
and Shanghai Artificial Intelligence Laboratory (Shanghai, China).
In addition to the public datasets listed above, MedGemma was also trained on
de-identified datasets licensed for research or collected internally at Google
from consented participants.
Radiology dataset 1: De-identified dataset of different CT studies across
body parts from a US-based radiology outpatient diagnostic center network.
Ophthalmology dataset 1: De-identified dataset of fundus images from
diabetic retinopathy screening.
Dermatology dataset 1: De-identified dataset of teledermatology skin
condition images (both clinical and dermatoscopic) from Colombia.
Dermatology dataset 2: De-identified dataset of skin cancer images (both
clinical and dermatoscopic) from Australia.
Dermatology dataset 3: De-identified dataset of non-diseased skin images
from an internal data collection effort.
Pathology dataset 1: De-identified dataset of histopathology H&E whole slide
images created in collaboration with an academic research hospital and
biobank in Europe. Comprises de-identified colon, prostate, and lymph nodes.
Pathology dataset 2: De-identified dataset of lung histopathology H&E and
IHC whole slide images created by a commercial biobank in the United States.
Pathology dataset 3: De-identified dataset of prostate and lymph node H&E
and IHC histopathology whole slide images created by a contract research
organization in the United States.
Pathology dataset 4: De-identified dataset of histopathology, predominantly
H&E whole slide images created in collaboration with a large, tertiary
teaching hospital in the United States. Comprises a diverse set of tissue
and stain types, predominantly H&E.
Data citation
MIMIC-CXR Johnson, A., Pollard, T., Mark, R., Berkowitz, S., & Horng, S.
(2024). MIMIC-CXR Database (version 2.1.0). PhysioNet.
https://physionet.org/content/mimic-cxr/2.1.0/and Johnson, Alistair E. W., Tom J. Pollard, Seth J. Berkowitz, Nathaniel R.
Greenbaum, Matthew P. Lungren, Chih-Ying Deng, Roger G. Mark, and Steven
Horng. 2019. "MIMIC-CXR, a de-Identified Publicly Available Database of
Chest Radiographs with Free-Text Reports." Scientific Data 6 (1): 1–8.
SLAKE Liu, Bo, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu.
2021.SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical
Visual Question Answering." http://arxiv.org/abs/2102.09542.
PAD-UEFS Pacheco, A. G. C., Lima, G. R., Salomao, A., Krohling, B.,
Biral, I. P., de Angelo, G. G., Alves, F. O. G., Ju X. M., & P. R. C.
(2020). PAD-UFES-20: A skin lesion dataset composed of patient data and
clinical images collected from smartphones. In Proceedings of the 2020 IEEE
International Conference on Bioinformatics and Biomedicine (BIBM) (pp.
1551-1558). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313241
SCIN Ward, Abbi, Jimmy Li, Julie Wang, Sriram Lakshminarasimhan, Ashley
Carrick, Bilson Campana, Jay Hartford, et al. 2024. "Creating an Empirical
Dermatology Dataset Through Crowdsourcing With Web Search Advertisements."
JAMA Network Open 7 (11): e2446615–e2446615.
TCGA The results shown here are in whole or part based upon data
generated by the TCGA Research Network: https://www.cancer.gov/tcga.
CAMELYON16 Ehteshami Bejnordi, Babak, Mitko Veta, Paul Johannes van
Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M.
van der Laak, et al. 2017. "Diagnostic Assessment of Deep Learning
Algorithms for Detection of Lymph Node Metastases in Women With Breast
Cancer." JAMA 318 (22): 2199–2210.
MedQA Jin, Di, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang,
and Peter Szolovits. 2020. "What Disease Does This Patient Have? A
Large-Scale Open Domain Question Answering Dataset from Medical Exams."
http://arxiv.org/abs/2009.13081.
AfrimedQA Olatunji, Tobi, Charles Nimo, Abraham Owodunni, Tassallah
Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, et al. 2024.
"AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering
Benchmark Dataset." http://arxiv.org/abs/2411.15640.
VQA-RAD Lau, Jason J., Soumya Gayen, Asma Ben Abacha, and Dina
Demner-Fushman. 2018. "A Dataset of Clinically Generated Visual Questions
and Answers about Radiology Images." Scientific Data 5 (1): 1–10.
MedexpQA Alonso, I., Oronoz, M., & Agerri, R. (2024). MedExpQA:
Multilingual Benchmarking of Large Language Models for Medical Question
Answering. arXiv preprint arXiv:2404.05590. Retrieved from
https://arxiv.org/abs/2404.05590
MedXpertQA Zuo, Yuxin, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu,
Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. 2025. "MedXpertQA:
Benchmarking Expert-Level Medical Reasoning and Understanding."
http://arxiv.org/abs/2501.18362.
De-identification/anonymization:
Google and partnerships utilize datasets that have been rigorously anonymized or
de-identified to ensure the protection of individual research participants and
patient privacy
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
Use and limitations
Intended use
MedGemma is an open multimodal generative AI model intended to be used as a
starting point that enables more efficient development of downstream healthcare
applications involving medical text and images. MedGemma is intended for
developers in the life sciences and healthcare space. Developers are responsible
for training, adapting and making meaningful changes to MedGemma to accomplish
their specific intended use. MedGemma models can be fine-tuned by developers
using their own proprietary data for their specific tasks or solutions.
MedGemma is based on Gemma 3 and has been further trained on medical images and
text. MedGemma enables further development in any medical context (image and
textual), however the model was pre-trained using chest X-ray, pathology,
dermatology, and fundus images. Examples of tasks within MedGemma's training
include visual question answering pertaining to medical images, such as
radiographs, or providing answers to textual medical questions. Full details of
all the tasks MedGemma has been evaluated can be found in an upcoming technical
report.
Benefits
Provides strong baseline medical image and text comprehension for models of
its size.
This strong performance makes it efficient to adapt for downstream
healthcare-based use cases, compared to models of similar size without
medical data pre-training.
This adaptation may involve prompt engineering, grounding, agentic
orchestration or fine-tuning depending on the use case, baseline validation
requirements, and desired performance characteristics.
Limitations
MedGemma is not intended to be used without appropriate validation, adaptation
and/or making meaningful modification by developers for their specific use case.
The outputs generated by MedGemma are not intended to directly inform clinical
diagnosis, patient management decisions, treatment recommendations, or any other
direct clinical practice applications. Performance benchmarks highlight baseline
capabilities on relevant benchmarks, but even for image and text domains that
constitute a substantial portion of training data, inaccurate model output is
possible. All outputs from MedGemma should be considered preliminary and require
independent verification, clinical correlation, and further investigation
through established research and development methodologies.
MedGemma's multimodal capabilities have been primarily evaluated on single-image
tasks. MedGemma has not been evaluated in use cases that involve comprehension
of multiple images.
MedGemma has not been evaluated or optimized for multi-turn applications.
MedGemma's training may make it more sensitive to the specific prompt used than
Gemma 3.
When adapting MedGemma developer should consider the following:
Bias in validation data: As with any research, developers should ensure
that any downstream application is validated to understand performance using
data that is appropriately representative of the intended use setting for
the specific application (e.g., age, sex, gender, condition, imaging device,
etc).
Data contamination concerns: When evaluating the generalization
capabilities of a large model like MedGemma in a medical context, there is a
risk of data contamination, where the model might have inadvertently seen
related medical information during its pre-training, potentially
overestimating its true ability to generalize to novel medical concepts.
Developers should validate MedGemma on datasets not publicly available or
otherwise made available to non-institutional researchers to mitigate this
risk.
