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Model: unsloth/medgemma-4b-it-GGUF Source: Original Platform
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---
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license: other
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license_name: health-ai-developer-foundations
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license_link: https://developers.google.com/health-ai-developer-foundations/terms
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library_name: transformers
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pipeline_tag: image-text-to-text
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extra_gated_heading: Access MedGemma on Hugging Face
|
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extra_gated_prompt: >-
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To access MedGemma on Hugging Face, you're required to review and
|
||||
agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
|
||||
To do this, please ensure you're logged in to Hugging Face and click below.
|
||||
Requests are processed immediately.
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extra_gated_button_content: Acknowledge license
|
||||
base_model:
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- google/medgemma-4b-it
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tags:
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- medical
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- unsloth
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- radiology
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- clinical-reasoning
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- dermatology
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- pathology
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- ophthalmology
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- chest-x-ray
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---
|
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<div>
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<p style="margin-top: 0;margin-bottom: 0;">
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<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
|
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</p>
|
||||
<div style="display: flex; gap: 5px; align-items: center; ">
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||||
<a href="https://github.com/unslothai/unsloth/">
|
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
|
||||
</a>
|
||||
<a href="https://discord.gg/unsloth">
|
||||
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
|
||||
</a>
|
||||
<a href="https://docs.unsloth.ai/">
|
||||
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
|
||||
</a>
|
||||
</div>
|
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</div>
|
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|
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# MedGemma model card
|
||||
|
||||
**Model documentation:** [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma)
|
||||
|
||||
**Resources:**
|
||||
|
||||
* Model on Google Cloud Model Garden: [MedGemma](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma)
|
||||
* Model on Hugging Face: [MedGemma](https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4)
|
||||
* GitHub repository (supporting code, Colab notebooks, discussions, and
|
||||
issues): [MedGemma](https://github.com/google-health/medgemma)
|
||||
* Quick start notebook: [GitHub](https://github.com/google-health/medgemma/blob/main/notebooks/quick_start_with_hugging_face.ipynb)
|
||||
* Fine-tuning notebook: [GitHub](https://github.com/google-health/medgemma/blob/main/notebooks/fine_tune_with_hugging_face.ipynb)
|
||||
* Concept applications built using MedGemma: [Collection](https://huggingface.co/collections/google/medgemma-concept-apps-686ea036adb6d51416b0928a)
|
||||
* Support: See [Contact](https://developers.google.com/health-ai-developer-foundations/medgemma/get-started.md#contact)
|
||||
* License: The use of MedGemma is governed by the [Health AI Developer
|
||||
Foundations terms of
|
||||
use](https://developers.google.com/health-ai-developer-foundations/terms).
|
||||
|
||||
**Author:** Google
|
||||
|
||||
## Model information
|
||||
|
||||
This section describes the MedGemma model and how to use it.
|
||||
|
||||
### Description
|
||||
|
||||
MedGemma is a collection of [Gemma 3](https://ai.google.dev/gemma/docs/core)
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||||
variants that are trained for performance on medical text and image
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||||
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](https://arxiv.org/abs/2303.15343) 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](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card#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](https://developers.google.com/health-ai-developer-foundations/medsiglip/model-card)
|
||||
is recommended. MedSigLIP is based on the same image encoder that powers
|
||||
MedGemma.
|
||||
|
||||
Please consult the [MedGemma Technical Report](https://arxiv.org/abs/2507.05201)
|
||||
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](https://cloud.google.com/model-garden).
|
||||
|
||||
First, install the Transformers library. Gemma 3 is supported starting from
|
||||
transformers 4.50.0.
|
||||
|
||||
```sh
|
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$ pip install -U transformers
|
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```
|
||||
|
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**Run model with the `pipeline` API**
|
||||
|
||||
```python
|
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from transformers import pipeline
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from PIL import Image
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import requests
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import torch
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|
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pipe = pipeline(
|
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"image-text-to-text",
|
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model="google/medgemma-4b-it",
|
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torch_dtype=torch.bfloat16,
|
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device="cuda",
|
||||
)
|
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|
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
|
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
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|
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messages = [
|
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{
|
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"role": "system",
|
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"content": [{"type": "text", "text": "You are an expert radiologist."}]
|
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},
|
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{
|
||||
"role": "user",
|
||||
"content": [
|
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{"type": "text", "text": "Describe this X-ray"},
|
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{"type": "image", "image": image}
|
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]
|
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}
|
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]
|
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|
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output = pipe(text=messages, max_new_tokens=200)
|
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print(output[0]["generated_text"][-1]["content"])
|
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```
|
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|
||||
**Run the model directly**
|
||||
|
||||
```python
|
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# pip install accelerate
|
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from transformers import AutoProcessor, AutoModelForImageTextToText
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from PIL import Image
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import requests
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import torch
|
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|
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model_id = "google/medgemma-4b-it"
|
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|
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model = AutoModelForImageTextToText.from_pretrained(
|
||||
model_id,
|
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torch_dtype=torch.bfloat16,
|
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device_map="auto",
|
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)
|
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processor = AutoProcessor.from_pretrained(model_id)
|
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|
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
|
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
|
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|
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messages = [
|
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{
|
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"role": "system",
|
||||
"content": [{"type": "text", "text": "You are an expert radiologist."}]
|
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},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Describe this X-ray"},
|
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{"type": "image", "image": image}
|
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]
|
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}
|
||||
]
|
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|
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inputs = processor.apply_chat_template(
|
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messages, add_generation_prompt=True, tokenize=True,
|
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return_dict=True, return_tensors="pt"
|
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).to(model.device, dtype=torch.bfloat16)
|
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|
||||
input_len = inputs["input_ids"].shape[-1]
|
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|
||||
with torch.inference_mode():
|
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generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
|
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generation = generation[0][input_len:]
|
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|
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decoded = processor.decode(generation, skip_special_tokens=True)
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print(decoded)
|
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```
|
||||
|
||||
### 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](https://colab.research.google.com/github/google-health/medgemma/blob/main/notebooks/quick_start_with_hugging_face.ipynb).
|
||||
Note that you will need to use Colab Enterprise to obtain adequate GPU
|
||||
resources to run either 27B model without quantization.
|
||||
|
||||
* For an example of fine-tuning the 4B model, see the [Fine-tuning notebook in
|
||||
Colab](https://colab.research.google.com/github/google-health/medgemma/blob/main/notebooks/fine_tune_with_hugging_face.ipynb).
|
||||
The 27B models can be fine tuned in a similar manner but will require more
|
||||
time and compute resources than the 4B model.
|
||||
|
||||
### Model architecture overview
|
||||
|
||||
The MedGemma model is built based on [Gemma 3](https://ai.google.dev/gemma/) and
|
||||
uses the same decoder-only transformer architecture as Gemma 3\. To read more
|
||||
about the architecture, consult the Gemma 3 [model
|
||||
card](https://ai.google.dev/gemma/docs/core/model_card_3).
|
||||
|
||||
### Technical specifications
|
||||
|
||||
* **Model type**: Decoder-only Transformer architecture, see the [Gemma 3
|
||||
Technical
|
||||
Report](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
|
||||
* **Input Modalities**: Text, vision
|
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* **Output Modality:** Text only
|
||||
* **Attention mechanism**: Grouped-query attention (GQA)
|
||||
* **Context length**: Supports long context, at least 128K tokens
|
||||
* **Key publication**: https://arxiv.org/abs/2507.05201
|
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* **Model created**: July 9, 2025
|
||||
|
||||
* **Model version**: 1.0.1
|
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|
||||
### Citation
|
||||
|
||||
When using this model, please cite: Sellergren et al. "MedGemma Technical
|
||||
Report." *arXiv preprint arXiv:2507.05201* (2025).
|
||||
|
||||
```none
|
||||
@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**:
|
||||
|
||||
* Text string, such as a question or prompt
|
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* Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
|
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* 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 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 |
|
||||
| :---- | :---- | :---- |
|
||||
| **Medical image classification** | | |
|
||||
| MIMIC CXR\*\* \- macro F1 for top 5 conditions | 81.2 | 88.9 |
|
||||
| CheXpert CXR \- macro F1 for top 5 conditions | 32.6 | 48.1 |
|
||||
| CXR14 \- macro F1 for 3 conditions | 32.0 | 50.1 |
|
||||
| PathMCQA\* (histopathology, internal\*\*) \- Accuracy | 37.1 | 69.8 |
|
||||
| US-DermMCQA\* \- Accuracy | 52.5 | 71.8 |
|
||||
| EyePACS\* (fundus, internal) \- Accuracy | 14.4 | 64.9 |
|
||||
| **Visual question answering** | | |
|
||||
| SLAKE (radiology) \- Tokenized F1 | 40.2 | 72.3 |
|
||||
| VQA-RAD\*\*\* (radiology) \- Tokenized F1 | 33.6 | 49.9 |
|
||||
| **Knowledge and reasoning** | | | | |
|
||||
| MedXpertQA (text \+ multimodal questions) \- Accuracy | 16.4 | 18.8 |
|
||||
|
||||
*Internal datasets. US-DermMCQA is described in [Liu (2020, Nature
|
||||
medicine)](https://www.nature.com/articles/s41591-020-0842-3), 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](https://arxiv.org/abs/2507.05201).
|
||||
|
||||
**Based on radiologist adjudicated labels, described in [Yang (2024,
|
||||
arXiv)](https://arxiv.org/pdf/2405.03162) Section A.1.1.
|
||||
|
||||
***Based on "balanced split," described in [Yang (2024,
|
||||
arXiv)](https://arxiv.org/pdf/2405.03162).
|
||||
|
||||
#### Chest X-ray report generation
|
||||
|
||||
MedGemma chest X-ray (CXR) report generation performance was evaluated on
|
||||
[MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) using the [RadGraph
|
||||
F1 metric](https://arxiv.org/abs/2106.14463). We compare the MedGemma
|
||||
pre-trained checkpoint with our previous best model for CXR report generation,
|
||||
[PaliGemma 2](https://arxiv.org/abs/2412.03555).
|
||||
|
||||
| Metric | MedGemma 4B (pre-trained) | MedGemma 4B (tuned for CXR)| PaliGemma 2 3B (tuned for CXR) | PaliGemma 2 10B (tuned for CXR) |
|
||||
| :---- | :---- | :---- | :---- | :---- |
|
||||
| MIMIC CXR \- RadGraph F1 | 29.5 | 30.3 |28.8 | 29.5 |
|
||||
|
||||
|
||||
|
||||
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 |
|
||||
| :---- | :---- | :---- |
|
||||
| MedQA (4-op) | 50.7 | 64.4 |
|
||||
| MedMCQA | 45.4 | 55.7 |
|
||||
| PubMedQA | 68.4 | 73.4 |
|
||||
| MMLU Med | 67.2 | 70.0 |
|
||||
| MedXpertQA (text only) | 11.6 | 14.2 |
|
||||
| AfriMed-QA (25 question test set) | 48.0 | 52.0 |
|
||||
|
||||
For all MedGemma 27B results, [test-time
|
||||
scaling](https://arxiv.org/abs/2501.19393) 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 |
|
||||
| :---- | :---- | :---- |
|
||||
| EHRQA | 70.9 | 67.6 |
|
||||
|
||||
|
||||
### 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](https://arxiv.org/abs/2303.15343) 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.
|
||||
|
||||
### 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 multimodal variants utilize a
|
||||
[SigLIP](https://arxiv.org/abs/2303.15343) 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
|
||||
|
||||
* [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/): MIT Laboratory
|
||||
for Computational Physiology and Beth Israel Deaconess Medical Center
|
||||
(BIDMC).
|
||||
* [Slake-VQA](https://www.med-vqa.com/slake/): 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](https://pmc.ncbi.nlm.nih.gov/articles/PMC7479321/): Federal
|
||||
University of Espírito Santo (UFES), Brazil, through its Dermatological and
|
||||
Surgical Assistance Program (PAD).
|
||||
* [SCIN](https://github.com/google-research-datasets/scin): A collaboration
|
||||
between Google Health and Stanford Medicine.
|
||||
* [TCGA](https://portal.gdc.cancer.gov/) (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](https://camelyon17.grand-challenge.org/Data/): The data was
|
||||
collected from Radboud University Medical Center and University Medical
|
||||
Center Utrecht in the Netherlands.
|
||||
* [PMC-OA (PubMed Central Open Access
|
||||
Subset)](https://catalog.data.gov/dataset/pubmed-central-open-access-subset-pmc-oa):
|
||||
Maintained by the National Library of Medicine (NLM) and National Center for
|
||||
Biotechnology Information (NCBI), which are part of the NIH.
|
||||
* [MedQA](https://arxiv.org/pdf/2009.13081): 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](https://data.mendeley.com/datasets/t9ndx37v5h/1): This dataset is
|
||||
from Rani Channamma University, and is hosted on Mendeley Data.
|
||||
* [AfriMed-QA](https://afrimedqa.com/): 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](https://www.nature.com/articles/sdata2018251): 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)
|
||||
* [Chest ImaGenome](https://physionet.org/content/chest-imagenome/1.0.0/): IBM
|
||||
Research.
|
||||
* [MedExpQA](https://www.sciencedirect.com/science/article/pii/S0933365724001805):
|
||||
This dataset was created by researchers at the HiTZ Center (Basque Center
|
||||
for Language Technology and Artificial Intelligence).
|
||||
* [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA): This
|
||||
dataset was developed by researchers at Tsinghua University (Beijing, China)
|
||||
and Shanghai Artificial Intelligence Laboratory (Shanghai, China).
|
||||
* [HealthSearchQA](https://huggingface.co/datasets/katielink/healthsearchqa):
|
||||
This dataset consists of consisting of 3,173 commonly searched consumer
|
||||
questions
|
||||
|
||||
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.
|
||||
|
||||
* **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 (EyePACS):** 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 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.
|
||||
* **EHR dataset 1:** Question/answer dataset drawn from synthetic FHIR records
|
||||
created by [Synthea.](https://synthetichealth.github.io/synthea/) The test
|
||||
set includes 19 unique patients with 200 questions per patient divided into
|
||||
10 different categories.
|
||||
|
||||
### 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/](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](http://arxiv.org/abs/2102.09542).
|
||||
|
||||
* **PAD-UEFS-20:** Pacheco, Andre GC, et al. "PAD-UFES-20: A skin lesion
|
||||
dataset composed of patient data and clinical images collected from
|
||||
smartphones." *Data in brief* 32 (2020): 106221\.
|
||||
|
||||
* **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](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.
|
||||
|
||||
* **Mendeley Digital Knee X-Ray:** Gornale, Shivanand; Patravali, Pooja
|
||||
(2020), "Digital Knee X-ray Images", Mendeley Data, V1, doi:
|
||||
10.17632/t9ndx37v5h.1
|
||||
|
||||
* **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.
|
||||
|
||||
* **Chest ImaGenome:** Wu, J., Agu, N., Lourentzou, I., Sharma, A., Paguio,
|
||||
J., Yao, J. S., Dee, E. C., Mitchell, W., Kashyap, S., Giovannini, A., Celi,
|
||||
L. A., Syeda-Mahmood, T., & Moradi, M. (2021). Chest ImaGenome Dataset
|
||||
(version 1.0.0). PhysioNet. RRID:SCR\_007345.
|
||||
[https://doi.org/10.13026/wv01-y230](https://doi.org/10.13026/wv01-y230)
|
||||
|
||||
* **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](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](http://arxiv.org/abs/2411.15640).
|
||||
|
||||
* **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](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](http://arxiv.org/abs/2501.18362).
|
||||
|
||||
### 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](https://github.com/jax-ml/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](https://arxiv.org/abs/2507.05201).
|
||||
|
||||
### 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.
|
||||
|
||||
|
||||
### Release notes
|
||||
|
||||
* May 20, 2025: Initial Release
|
||||
* July 9, 2025 Bug Fix: Fixed the subtle degradation in the multimodal
|
||||
performance. The issue was due to a missing end-of-image token in the model
|
||||
vocabulary, impacting combined text-and-image tasks. This fix reinstates and
|
||||
correctly maps that token, ensuring text-only tasks remain unaffected while
|
||||
restoring multimodal performance.
|
||||
62
config.json
Normal file
62
config.json
Normal file
@@ -0,0 +1,62 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Gemma3ForConditionalGeneration"
|
||||
],
|
||||
"boi_token_index": 255999,
|
||||
"bos_token_id": 2,
|
||||
"eoi_token_index": 256000,
|
||||
"eos_token_id": 106,
|
||||
"image_token_index": 262144,
|
||||
"initializer_range": 0.02,
|
||||
"mm_tokens_per_image": 256,
|
||||
"model_type": "gemma3",
|
||||
"pad_token_id": 0,
|
||||
"text_config": {
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"attn_logit_softcapping": null,
|
||||
"cache_implementation": "hybrid",
|
||||
"final_logit_softcapping": null,
|
||||
"head_dim": 256,
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 10240,
|
||||
"max_position_embeddings": 131072,
|
||||
"model_type": "gemma3_text",
|
||||
"num_attention_heads": 8,
|
||||
"num_hidden_layers": 34,
|
||||
"num_key_value_heads": 4,
|
||||
"query_pre_attn_scalar": 256,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_local_base_freq": 10000,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"rope_type": "linear"
|
||||
},
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": 1024,
|
||||
"sliding_window_pattern": 6,
|
||||
"torch_dtype": "bfloat16",
|
||||
"use_cache": true,
|
||||
"vocab_size": 262208
|
||||
},
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.3",
|
||||
"unsloth_fixed": true,
|
||||
"vision_config": {
|
||||
"attention_dropout": 0.0,
|
||||
"hidden_act": "gelu_pytorch_tanh",
|
||||
"hidden_size": 1152,
|
||||
"image_size": 896,
|
||||
"intermediate_size": 4304,
|
||||
"layer_norm_eps": 1e-06,
|
||||
"model_type": "siglip_vision_model",
|
||||
"num_attention_heads": 16,
|
||||
"num_channels": 3,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14,
|
||||
"torch_dtype": "bfloat16",
|
||||
"vision_use_head": false
|
||||
}
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
|
||||
BIN
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|
||||
Reference in New Issue
Block a user