---

license: other license_name: health-ai-developer-foundations license_link: https://developers.google.com/health-ai-developer-foundations/terms library_name: transformers pipeline_tag: image-text-to-text extragatedheading: Access MedGemma on Hugging Face extragatedprompt: >- To access MedGemma on Hugging Face, you're required to review and agree to Health AI Developer Foundation's terms of use. To do this, please ensure you're logged in to Hugging Face and click below. Requests are processed immediately. extragatedbutton_content: Acknowledge license base_model:

• google/medgemma-27b-text-it

tags:

• medical
• unsloth

- clinical-reasoning - thinking

---

MedGemma model card

Model documentation: MedGemma

Resources:

• Model on Google Cloud Model Garden: MedGemma
• Model on Hugging Face: MedGemma
• GitHub repository (supporting code, Colab notebooks, discussions, and

issues): MedGemma

• Quick start notebook: GitHub
• Fine-tuning notebook: GitHub
• Patient Education Demo built using MedGemma
• Support: See Contact
• License: The use of MedGemma is governed by the Health AI Developer

Foundations terms of use.

Author: Google

Model information

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 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.

$ pip install -U transformers

Run model with the pipeline API

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="google/medgemma-27b-text-it",
    torch_dtype=torch.bfloat16,
    device="cuda",
)

messages = [
    {
        "role": "system",
        "content": "You are a helpful medical assistant."
    },
    {
        "role": "user",
        "content": "How do you differentiate bacterial from viral pneumonia?"
    }
]

output = pipe(text=messages, maxnewtokens=200)
print(output[0]["generated_text"][-1]["content"])

Run the model directly

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "google/medgemma-27b-text-it"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.frompretrained(modelid)

messages = [
    {
        "role": "system",
        "content": "You are a helpful medical assistant."
    },
    {
        "role": "user",
        "content": "How do you differentiate bacterial from viral pneumonia?"
    }
]

inputs = tokenizer.applychattemplate(
    messages,
    addgenerationprompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

inputlen = inputs["inputids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(inputs, maxnewtokens=200, do_sample=False)
    generation = generation[0][input_len:]

decoded = tokenizer.decode(generation, skipspecialtokens=True)
print(decoded)

Examples

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.

• For an example of fine-tuning the model, see the Fine-tuning notebook in

Colab.

Model architecture overview

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.

Technical specifications

• Model type: Decoder-only Transformer architecture, see the Gemma 3

technical report

• Modalities: 4B: Text, vision; 27B: Text only
• Attention mechanism: Utilizes grouped-query attention (GQA)
• 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:

@misc{medgemma-hf,
    author = {Google},
    title = {MedGemma Hugging Face}
    howpublished = {\url{https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4}},
    year = {2025},
    note = {Accessed: [Insert Date Accessed, e.g., 2025-05-20]}
}

Inputs and outputs

Input:

• Text string, such as a question or prompt
• 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

#### 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.

Metric	MedGemma 27B	Gemma 3 27B	MedGemma 4B	Gemma 3 4B
MedQA (4-op)	89.8 (best-of-5) 87.7 (0-shot)	74.9	64.4	50.7
MedMCQA	74.2	62.6	55.7	45.4
PubMedQA	76.8	73.4	73.4	68.4
MMLU Med (text only)	87.0	83.3	70.0	67.2
MedXpertQA (text only)	26.7	15.7	14.2	11.6
AfriMed-QA	84.0	72.0	52.0	48.0

For all MedGemma 27B results, test-time scaling is used to improve performance.

Ethics and safety evaluation

#### Evaluation approach

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.

• Johnson, A.E.W., Pollard, T.J., Berkowitz, S.J. et al. MIMIC-CXR, a

de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6, 317 (2019).

• Available on Physionet Goldberger, A., Amaral, L., Glass, L., Hausdorff, J.,

Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). [PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation \[Online\]. 101 (23), pp. E215–e220.](https://pubmed.ncbi.nlm.nih.gov/10851218/)

• Bo Liu, Li-Ming Zhan, etc. SLAKE: A Semantically-Labeled Knowledge-Enhanced

Dataset for Medical Visual Question Answering.

• PAD-UFES-20: A skin lesion dataset composed of patient data and clinical

images collected from smartphones

• The Cancer Genome Atlas Program (TCGA)
• Babak Ehteshami Bejnordi, etc.: Diagnostic Assessment of Deep Learning

Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

• MedQA: https://arxiv.org/abs/2009.13081
• Mendeley Digital Knee X-Ray: Gornale, Shivanand; Patravali, Pooja (2020),

"Digital Knee X-ray Images", Mendeley Data, V1, doi: 10.17632/t9ndx37v5h.1

• AfriMed-QA: https://arxiv.org/abs/2411.15640
• VQA-RAD: Lau, J., Gayen, S., Ben Abacha, A. et al. A dataset of clinically

generated visual questions and answers about radiology images. Sci Data 5, 180251 (2018). https://doi.org/10.1038/sdata.2018.251

• MedExpQA: Multilingual benchmarking of Large Language Models for

Medical Question Answering

• MedXpertQA: arXiv:2501.18362v2

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

Implementation information

Details about the model internals.

Software

Training was done using JAX.

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.