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MedGemma model card
Model documentation: MedGemma
Resources:
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 three variants: a 4B multimodal version and 27B text-only and multimodal versions.
Both MedGemma multimodal versions utilize 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. Their LLM components are trained on a diverse set of medical data, including medical text, medical question-answer pairs, FHIR-based electronic health record data (27B multimodal only), 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 multimodal has pre-training on medical image, medical record and medical record comprehension tasks. MedGemma 27B text-only has been trained exclusively on medical text. Both models have been optimized for inference-time computation on medical reasoning. This means it has slightly higher performance on some text benchmarks than MedGemma 27B multimodal. Users who want to work with a single model for both medical text, medical record and medical image tasks are better suited for MedGemma 27B multimodal. Those that only need text use-cases may be better served with the text-only variant. Both MedGemma 27B variants are only available in instruction-tuned versions.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These evaluations are based on 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.
MedGemma is optimized for medical applications that involve a text generation component. For medical image-based applications that do not involve text generation, such as data-efficient classification, zero-shot classification, or content-based or semantic image retrieval, the MedSigLIP image encoder is recommended. MedSigLIP is based on the same image encoder that powers MedGemma.
Please consult the MedGemma Technical Report for more details.
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 modelscope import pipeline
from PIL import Image
import requests
import torch
pipe = pipeline(
“image-text-to-text”,
model=“google/medgemma-27b-it”,
torch_dtype=torch.bfloat16,
device=“cuda”,
)
messages = [
{
“role”: “system”,
“content”: [{“type”: “text”, “text”: “You are a helpful medical assistant.”}]
},
{
“role”: “user”,
“content”: [{“type”: “text”, “text”: “How do you differentiate bacterial from viral pneumonia?”}]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0][“generated_text”][-1][“content”])
# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
image_url = “https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png”
image = Image.open(requests.get(image_url, headers={“User-Agent”: “example”}, stream=True).raw)
messages = [
{
“role”: “system”,
“content”: [{“type”: “text”, “text”: “You are an expert radiologist.”}]
},
{
“role”: “user”,
“content”: [
{“type”: “text”, “text”: “Describe this X-ray”},
{“type”: “image”, “image”: image}
]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0][“generated_text”][-1][“content”])
Run the model directly
# pip install accelerate
from modelscope import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import requests
import torch
model_id = “google/medgemma-27b-it”
model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map=“auto”,
)
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
“role”: “system”,
“content”: [{“type”: “text”, “text”: “You are a helpful medical assistant.”}]
},
{
“role”: “user”,
“content”: [{“type”: “text”, “text”: “How do you differentiate bacterial from viral pneumonia?”}]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors=“pt”
).to(model.device, dtype=torch.bfloat16)
input_len = inputs[“input_ids”].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
image_url = “https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png”
image = Image.open(requests.get(image_url, headers={“User-Agent”: “example”}, stream=True).raw)
messages = [
{
“role”: “system”,
“content”: [{“type”: “text”, “text”: “You are an expert radiologist.”}]
},
{
“role”: “user”,
“content”: [
{“type”: “text”, “text”: “Describe this X-ray”},
{“type”: “image”, “image”: image}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors=“pt”
).to(model.device, dtype=torch.bfloat16)
input_len = inputs[“input_ids”].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
Examples
See the following Colab notebooks for examples of how to use MedGemma:
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
Citation
When using this model, please cite: Sellergren et al. “MedGemma Technical Report.“ *arXiv preprint arXiv:2507.05201* (2025).
@article{sellergren2025medgemma,
title={MedGemma Technical Report},
author={Sellergren, Andrew and Kazemzadeh, Sahar and Jaroensri, Tiam and Kiraly, Atilla and Traverse, Madeleine and Kohlberger, Timo and Xu, Shawn and Jamil, Fayaz and Hughes, Cían and Lau, Charles and others},
journal={arXiv preprint arXiv:2507.05201},
year={2025}
}
Inputs and outputs
Input:
Output:
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 and 27B multimodal 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.
| Task and metric | Gemma 3 4B | MedGemma 4B | Gemma 3 27B | MedGemma 27B multimodal |
|---|---|---|---|---|
| Medical image classification | ||||
| MIMIC CXR** - macro F1 for top 5 conditions | 81.2 | 88.9 | 71.7 | 90.0 |
| CheXpert CXR - macro F1 for top 5 conditions | 32.6 | 48.1 | 26.2 | 49.9 |
| CXR14 - macro F1 for 3 conditions | 32.0 | 50.1 | 31.4 | 45.3 |
| PathMCQA* (histopathology, internal**) - Accuracy | 37.1 | 69.8 | 42.2 | 71.6 |
| US-DermMCQA* - Accuracy | 52.5 | 71.8 | 66.9 | 71.7 |
| EyePACS* (fundus, internal) - Accuracy | 14.4 | 64.9 | 20.3 | 75.3 |
| Visual question answering | ||||
| SLAKE (radiology) - Tokenized F1 | 40.2 | 72.3 | 42.5 | 70.0 |
| VQA-RAD*** (radiology) - Tokenized F1 | 33.6 | 49.9 | 42.7 | 46.7 |
| Knowledge and reasoning | ||||
| MedXpertQA (text + multimodal questions) - Accuracy | 16.4 | 18.8 | 22.0 | 26.8 |
*Internal datasets. US-DermMCQA is described in Liu (2020, Nature medicine), presented as a 4-way MCQ per example for skin condition classification. PathMCQA is based on multiple datasets, presented as 3-9 way MCQ per example for identification, grading, and subtype for breast, cervical, and prostate cancer. EyePACS is a dataset of fundus images with classification labels based on 5-level diabetic retinopathy severity (None, Mild, Moderate, Severe, Proliferative). More details in the MedGemma Technical Report.
**Based on radiologist adjudicated labels, described in Yang (2024, arXiv) Section A.1.1.
***Based on “balanced split,” described in Yang (2024, arXiv).
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) | MedGemma 4B (tuned for CXR) | MedGemma 27B multimodal (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 | 30.3 | 27.0 | 28.8 | 29.5 |
*Not released
The instruction-tuned versions of MedGemma 4B and MedGemma 27B achieve lower scores (21.9 and 21.3, respectively) due to the differences in reporting style compared to the MIMIC ground truth reports. Further fine-tuning on MIMIC reports enables users to achieve improved performance, as shown by the improved performance of the MedGemma 4B model that was tuned for CXR.
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 | Gemma 3 4B | MedGemma 4B | Gemma 3 27B | MedGemma 27B text-only | MedGemma 27B multimodal |
|---|---|---|---|---|---|
| MedQA (4-op) | 50.7 | 64.4 | 74.9 | 89.8 (best-of-5) 87.7 (0-shot) | 87.0 (best-of-5) 85.3 (0-shot) |
| MedMCQA | 45.4 | 55.7 | 62.6 | 74.2 | 70.2 |
| PubMedQA | 68.4 | 73.4 | 73.4 | 76.8 | 77.2 |
| MMLU Med | 67.2 | 70.0 | 83.3 | 87.0 | 86.2 |
| MedXpertQA (text only) | 11.6 | 14.2 | 15.7 | 25.7 | 23.7 |
| AfriMed-QA (25 question test set) | 48.0 | 52.0 | 72.0 | 84.0 | 72.0 |
For all MedGemma 27B results, test-time scaling is used to improve performance.
Medical record evaluations
All models were evaluated on a question answer dataset from synthetic FHIR data to answer questions about patient records. MedGemma 27B multimodal’s FHIR-specific training gives it significant improvement over other MedGemma and Gemma models.
| Metric | Gemma 3 4B | MedGemma 4B | Gemma 3 27B | MedGemma 27B text-only | MedGemma 27B multimodal |
|---|---|---|---|---|---|
| EHRQA | 70.9 | 67.6 | 84.2 | 86.3 | 90.5 |
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:
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 multimodal variants utilize 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. Their LLM component is trained on a diverse set of medical data, including medical text, medical question-answer pairs, FHIR-based electronic health record data (27B multimodal only), radiology images, 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 6 different tasks and 4 medical image modalities. These benchmarks include both open and internal datasets.
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), ChestImaGenome: Set of bounding boxes linking image findings with anatomical regions for MIMIC-CXR (MedGemma 27B multimodal only), SLAKE (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
In addition to the public datasets listed above, MedGemma was also trained on de-identified, licensed datasets or datasets collected internally at Google from consented participants.
Data citation
De-identification/anonymization:
Google and its partners 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 the MedGemma Technical Report.
Benefits
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:
