# MedGemma

Medical-domain variant for text + image comprehension. Current release is **MedGemma 1.5** (Jan 13, 2026), built on **Gemma 3**. **No Gemma 4 generation.**

## What it is

Gemma 3 fine-tuned on de-identified medical corpora — clinical notes, radiology images, dermatology images, histopathology, etc. The multimodal variants use a SigLIP image encoder trained specifically on medical imagery (not the base SigLIP).

## Sizes

**MedGemma 1.5** (current): **4B multimodal IT only**. Previous 27B variants were in MedGemma 1; 1.5 currently ships 4B only with improvements in medical reasoning, records interpretation, and image interpretation.

**MedGemma 1** (prior): 4B multimodal, 27B text-only, 27B multimodal.

## Model card

- https://developers.google.com/health-ai-developer-foundations/medgemma/model-card
- DeepMind: https://deepmind.google/models/gemma/medgemma/
- Repo: https://github.com/google-health/medgemma
- Tech report: https://arxiv.org/abs/2507.05201

## Intended use

"A starting point that enables more efficient development of downstream healthcare applications involving medical text and images." **Developer tool, not a clinical product.**

### Disclaimer (near-verbatim from model card)

> 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. All outputs require independent verification and clinical correlation.

Terms of use are governed by **Health AI Developer Foundations** — a separate license from base Gemma's. Read it before shipping anything.

## Prompt format

Standard Gemma 3 chat template. Content messages accept `{"type": "image"}` and `{"type": "text"}`.

## Minimum invocation

```python
from transformers import pipeline
from PIL import Image
import requests, torch

pipe = pipeline(
    "image-text-to-text",
    model="google/medgemma-1.5-4b-it",
    torch_dtype=torch.bfloat16,
    device="cuda",
)

img_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
image = Image.open(requests.get(img_url, stream=True).raw)

messages = [{"role": "user", "content": [
    {"type": "image", "image": image},
    {"type": "text", "text": "Describe this chest X-ray. What anatomical structures are visible?"},
]}]

out = pipe(text=messages, max_new_tokens=512)
print(out[0]["generated_text"][-1]["content"])
```

## When to choose it over base Gemma 4

- You're building **healthcare dev tools** (medical image triage assistant, doctor-facing records summarizer, clinician education) and want the SigLIP-medical image encoder.
- You can accept the Health AI Developer Foundations license and embed the disclaimers.
- You need **medical-vocabulary fluency** (SNOMED, ICD, RxNorm) that base Gemma 4 doesn't have at the 4B size.

Use base Gemma 4 otherwise — including for health-adjacent content that isn't clinical (fitness logs, nutrition, sleep data).

## Homelab fit

Zero. Seth is not running medical apps. Noted for completeness only.
