---
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access MedGemma on Hugging Face
extra_gated_prompt: >-
  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.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-pt
tags:
  - medical
  - clinical-reasoning
  - thinking
---

# <span style="color: #7FFF7F;">medgemma-27b-text-it GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`f5cd27b7`](https://github.com/ggerganov/llama.cpp/commit/f5cd27b71da3ac375a04a41643d14fc779a8057b).




## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>

Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

### **Benchmark Context**
All tests conducted on **Llama-3-8B-Instruct** using:
- Standard perplexity evaluation pipeline
- 2048-token context window
- Same prompt set across all quantizations

### **Method**
- **Dynamic Precision Allocation**:  
  - First/Last 25% of layers → IQ4_XS (selected layers)  
  - Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)  
- **Critical Component Protection**:  
  - Embeddings/output layers use Q5_K  
  - Reduces error propagation by 38% vs standard 1-2bit  

### **Quantization Performance Comparison (Llama-3-8B)**

| Quantization | Standard PPL | DynamicGate PPL | Δ PPL   | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
| IQ2_XXS      | 11.30        | 9.84             | -12.9%  | 2.5G     | 2.6G    | +0.1G  | 234s      | 246s     |
| IQ2_XS       | 11.72        | 11.63            | -0.8%   | 2.7G     | 2.8G    | +0.1G  | 242s      | 246s     |
| IQ2_S        | 14.31        | 9.02             | -36.9%  | 2.7G     | 2.9G    | +0.2G  | 238s      | 244s     |
| IQ1_M        | 27.46        | 15.41            | -43.9%  | 2.2G     | 2.5G    | +0.3G  | 206s      | 212s     |
| IQ1_S        | 53.07        | 32.00            | -39.7%  | 2.1G     | 2.4G    | +0.3G  | 184s      | 209s     |

**Key**:
- PPL = Perplexity (lower is better)
- Δ PPL = Percentage change from standard to DynamicGate
- Speed = Inference time (CPU avx2, 2048 token context)
- Size differences reflect mixed quantization overhead

**Key Improvements:**
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization

**Tradeoffs:**
- All variants have modest size increases (0.1-0.3GB)
- Inference speeds remain comparable (<5% difference)


### **When to Use These Models**
📌 **Fitting models into GPU VRAM**

✔ **Memory-constrained deployments**

✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated 
 
✔ **Research** into ultra-low-bit quantization



## **Choosing the Right Model Format**  

Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.  

### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**  
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.  
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.  
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).  
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.  

📌 **Use BF16 if:**  
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).  
✔ You want **higher precision** while saving memory.  
✔ You plan to **requantize** the model into another format.  

📌 **Avoid BF16 if:**  
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).  
❌ You need compatibility with older devices that lack BF16 optimization.  

---

### **F16 (Float 16) – More widely supported than BF16**  
- A 16-bit floating-point **high precision** but with less of range of values than BF16. 
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).  
- Slightly lower numerical precision than BF16 but generally sufficient for inference.  

📌 **Use F16 if:**  
✔ Your hardware supports **FP16** but **not BF16**.  
✔ You need a **balance between speed, memory usage, and accuracy**.  
✔ You are running on a **GPU** or another device optimized for FP16 computations.  

📌 **Avoid F16 if:**  
❌ Your device lacks **native FP16 support** (it may run slower than expected).  
❌ You have memory limitations.  

---

### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**  
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.  
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.  
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.  

📌 **Use Quantized Models if:**  
✔ You are running inference on a **CPU** and need an optimized model.  
✔ Your device has **low VRAM** and cannot load full-precision models.  
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.  

📌 **Avoid Quantized Models if:**  
❌ You need **maximum accuracy** (full-precision models are better for this).  
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).  

---

### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**  
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.  

- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.  
  - **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.  
  - **Trade-off**: Lower accuracy compared to higher-bit quantizations.  

- **IQ3_S**: Small block size for **maximum memory efficiency**.  
  - **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.  

- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.  
  - **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.  

- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.  
  - **Use case**: Best for **low-memory devices** where **Q6_K** is too large.  

- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.  
  - **Use case**: Best for **ARM-based devices** or **low-memory environments**.  

---

### **Summary Table: Model Format Selection**  

| Model Format  | Precision  | Memory Usage  | Device Requirements  | Best Use Case  |  
|--------------|------------|---------------|----------------------|---------------|  
| **BF16**     | Highest    | High          | BF16-supported GPU/CPUs  | High-speed inference with reduced memory |  
| **F16**      | High       | High          | FP16-supported devices | GPU inference when BF16 isn't available |  
| **Q4_K**     | Medium Low | Low           | CPU or Low-VRAM devices | Best for memory-constrained environments |  
| **Q6_K**     | Medium     | Moderate      | CPU with more memory | Better accuracy while still being quantized |  
| **Q8_0**     | High       | Moderate      | CPU or GPU with enough VRAM | Best accuracy among quantized models |  
| **IQ3_XS**   | Very Low   | Very Low      | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |  
| **Q4_0**     | Low        | Low           | ARM or low-memory devices | llama.cpp can optimize for ARM devices |  

---

## **Included Files & Details**  

### `medgemma-27b-text-it-bf16.gguf`  
- Model weights preserved in **BF16**.  
- Use this if you want to **requantize** the model into a different format.  
- Best if your device supports **BF16 acceleration**.  

### `medgemma-27b-text-it-f16.gguf`  
- Model weights stored in **F16**.  
- Use if your device supports **FP16**, especially if BF16 is not available.  

### `medgemma-27b-text-it-bf16-q8_0.gguf`  
- **Output & embeddings** remain in **BF16**.  
- All other layers quantized to **Q8_0**.  
- Use if your device supports **BF16** and you want a quantized version.  

### `medgemma-27b-text-it-f16-q8_0.gguf`  
- **Output & embeddings** remain in **F16**.  
- All other layers quantized to **Q8_0**.    

### `medgemma-27b-text-it-q4_k.gguf`  
- **Output & embeddings** quantized to **Q8_0**.  
- All other layers quantized to **Q4_K**.  
- Good for **CPU inference** with limited memory.  

### `medgemma-27b-text-it-q4_k_s.gguf`  
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.  
- Best for **very low-memory setups**.  

### `medgemma-27b-text-it-q6_k.gguf`  
- **Output & embeddings** quantized to **Q8_0**.  
- All other layers quantized to **Q6_K** .  

### `medgemma-27b-text-it-q8_0.gguf`  
- Fully **Q8** quantized model for better accuracy.  
- Requires **more memory** but offers higher precision.  

### `medgemma-27b-text-it-iq3_xs.gguf`  
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.  
- Best for **ultra-low-memory devices**.  

### `medgemma-27b-text-it-iq3_m.gguf`  
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.  
- Suitable for **low-memory devices**.  

### `medgemma-27b-text-it-q4_0.gguf`  
- Pure **Q4_0** quantization, optimized for **ARM devices**.  
- Best for **low-memory environments**.
- Prefer IQ4_NL for better accuracy.

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
❤ **Please click "Like" if you find this useful!**  
Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:  
👉 [Quantum Network Monitor](https://readyforquantum.com/dashboard/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  

💬 **How to test**:  
 Choose an **AI assistant type**:  
   - `TurboLLM` (GPT-4o-mini)  
   - `HugLLM` (Hugginface Open-source)  
   - `TestLLM` (Experimental CPU-only)  

### **What I’m Testing**  
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
- **How small can a model go** while still handling:  
  - Automated **Nmap scans**  
  - **Quantum-readiness checks**  
  - **Network Monitoring tasks**  

🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads):  
- ✅ **Zero-configuration setup**  
- ⏳ 30s load time (slow inference but **no API costs**)  
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  

### **Other Assistants**  
🟢 **TurboLLM** – Uses **gpt-4o-mini** for: 
- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)  
  

🔵 **HugLLM** – Latest Open-source models:  
- 🌐 Runs on Hugging Face Inference API  

### 💡 **Example commands to you could test**:  
1. `"Give me info on my websites SSL certificate"`  
2. `"Check if my server is using quantum safe encyption for communication"`  
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!

### Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊




# 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)
*   [Patient Education Demo built using MedGemma](https://huggingface.co/spaces/google/rad_explain)
*   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)
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](https://cloud.google.com/model-garden).

First, install the Transformers library. Gemma 3 is supported starting from
transformers 4.50.0.

```sh
$ pip install -U transformers
```

**Run model with the `pipeline` API**

```python
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, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
```

**Run the model directly**

```python
# 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.from_pretrained(model_id)

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

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

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 = tokenizer.decode(generation, skip_special_tokens=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](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 run the 27B model without
    quantization.

*   For an example of fine-tuning the 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).

### 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)
*   **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:

```none
@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](https://arxiv.org/abs/2501.19393) 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](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.

#### 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](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)
*   [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).

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.

*   **Mendeley Digital Knee X-Ray** Gornale, Shivanand; Patravali, Pooja (2020),
    "Digital Knee X-ray Images", Mendeley Data, V1, doi: 10.17632/t9ndx37v5h.1

*   **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

## 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 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.