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README.md

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+---
+license: other
+license_name: health-ai-developer-foundations
+license_link: https://developers.google.com/health-ai-developer-foundations/terms
+library_name: transformers
+pipeline_tag: image-text-to-text
+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
+tags:
+- medical
+- radiology
+- clinical-reasoning
+- dermatology
+- pathology
+- ophthalmology
+- chest-x-ray
+---
+# MedGemma 1.5 model card
+
+Note: This card describes MedGemma 1.5, which is only available as a 4B
+multimodal instruction-tuned variant. For information on MedGemma 1 variants,
+refer to the [MedGemma 1 model
+card](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card-v1).
+
+**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)
+*   Models on Hugging Face: [Collection](https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4)
+*   Concept applications built using MedGemma: [Collection](https://huggingface.co/collections/google/medgemma-concept-apps-686ea036adb6d51416b0928a)
+*   [GitHub repository](https://github.com/google-health/medgemma)
+*   [Tutorial notebooks](https://github.com/google-health/medgemma/blob/main/notebooks)
+
+*   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).
+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:
+
+
+
+*   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).
+
+*   [Support](https://developers.google.com/health-ai-developer-foundations/medgemma/get-started.md#contact)
+    channels
+
+**Author:** Google
+
+## Model information
+
+This section describes the specifications and recommended use of the MedGemma
+model.
+
+### 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 1.5 4B is an updated version of the MedGemma 1 4B model.
+
+MedGemma 1.5 4B expands support for several new medical imaging and data
+processing applications, including:
+
+*   **High-dimensional medical imaging:** Interpretation of three-dimensional
+    volume representations of Computed Tomography (CT) and Magnetic Resonance
+    Imaging (MRI).
+*   **Whole-slide histopathology imaging (WSI):** Simultaneous interpretation of
+    multiple patches from a whole slide histopathology image as input.
+*   **Longitudinal medical imaging:** Interpretation of chest X-rays in the
+    context of prior images (e.g., comparing current versus historical scans).
+*   **Anatomical localization:** Bounding box–based localization of anatomical
+    features and findings in chest X-rays.
+*   **Medical document understanding:** Extraction of structured data, such as
+    values and units, from unstructured medical lab reports.
+*   **Electronic Health Record (EHR) understanding:** Interpretation of
+    text-based EHR data.
+
+In addition to these new features, MedGemma 1.5 4B delivers improved accuracy on
+medical text reasoning and modest improvement on standard 2D image
+interpretation compared to MedGemma 1 4B.
+
+MedGemma 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 chest X-rays, dermatology images, ophthalmology images, and
+histopathology slides. The LLM component is trained on a diverse set of medical
+data, including medical text, medical question-answer pairs, FHIR-based
+electronic health record data, 2D and 3D radiology images, histopathology
+images, ophthalmology images, dermatology images, and lab reports for document
+understanding.
+
+MedGemma 1.5 4B has been evaluated on a range of clinically relevant benchmarks
+to illustrate its baseline performance. These evaluations are based on both open
+benchmark datasets and internally curated datasets. Developers are expected to
+fine-tune MedGemma for improved performance on their use case. Consult the
+[Intended use section](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card.md#intended_use)
+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 1 and MedGemma 1.5.
+
+### How to use
+
+The following are some example code snippets to help you quickly get started
+running the model locally on GPU.
+
+Note: If you need to use the model at scale, we recommend creating a production
+version using [Model
+Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma).
+Model Garden provides various deployment options and tutorial notebooks,
+including specialized server-side image processing options for efficiently
+handling large medical images: Whole Slide Digital Pathology (WSI) or volumetric
+scans (CT/MRI) stored in [Cloud DICOM
+Store](https://docs.cloud.google.com/healthcare-api/docs/concepts/dicom) or
+[Google Cloud Storage (GCS)](https://cloud.google.com/storage).
+
+First, install the Transformers library. Gemma 3 is supported starting from
+transformers 4.50.0.
+
+```sh
+$ pip install -U transformers
+```
+
+Next, use either the pipeline wrapper or the transformer API directly to send a
+chest X-ray image and a question to the model.
+
+Note that CT, MRI and whole-slide histopathology images require some
+pre-processing; see the
+[CT](https://github.com/google-health/medgemma/blob/main/notebooks/high_dimensional_ct_hugging_face.ipynb)
+and
+[WSI](https://github.com/google-health/medgemma/blob/main/notebooks/high_dimensional_pathology_hugging_face.ipynb)
+notebook for examples.
+
+**Run model with the pipeline API**
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+import torch
+
+pipe = pipeline(
+    "image-text-to-text",
+    model="google/medgemma-1.5-4b-it",
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+)
+
+# 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": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "Describe this X-ray"}
+        ]
+    }
+]
+
+output = pipe(text=messages, max_new_tokens=2000)
+print(output[0]["generated_text"][-1]["content"])
+```
+
+**Run the model directly**
+
+```python
+# Make sure to install the accelerate library first via `pip install accelerate`
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from PIL import Image
+import requests
+import torch
+
+model_id = "google/medgemma-1.5-4b-it"
+
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+# 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": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "Describe this X-ray"}
+        ]
+    }
+]
+
+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=2000, do_sample=False)
+    generation = generation[0][input_len:]
+
+decoded = processor.decode(generation, skip_special_tokens=True)
+print(decoded)
+```
+
+### Examples
+
+Refer to the growing collection of [tutorial
+notebooks](https://github.com/google-health/medgemma/blob/main/notebooks) to see
+how to use or fine-tune MedGemma.
+
+### 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 (multimodal)
+*   **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](https://arxiv.org/abs/2507.05201)
+*   **Model created**: **4B multimodal**: Jan 13, 2026
+*   **Model version**: **4B multimodal**: 1.5.0
+
+### 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
+*   Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
+*   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 evaluations
+
+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 1.5 4B was evaluated across a range of
+benchmarks, focusing on radiology (2D, longitudinal 2D, and 3D), dermatology,
+histopathology, ophthalmology, document understanding, and multimodal clinical
+reasoning. See Data card for details of individual datasets.
+
+We also list the previous results for MedGemma 1 4B and 27B (multimodal models
+only), as well as for Gemma 3 4B for comparison.
+
+| Task / Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
+| :---- | :---- | :---- | :---- | :---- | :---- |
+| **3D radiology image classification** |  |  |  |  |  |
+| CT Dataset 1\*(7 conditions/abnormalities) | Macro accuracy | 54.5 | 58.2 | 61.1 | 57.8 |
+| CT-RATE (validation, 18 conditions/abnormalities ) | Macro F1 |  | 23.5 | 27.0 |  |
+|  | Macro precision |  | 34.5 | 34.2 |  |
+|  | Macro recall |  | 34.1 | 42.0 |  |
+| MRI Dataset 1\*(10  conditions/abnormalities) | Macro accuracy | 51.1 | 51.3 | 64.7 | 57.4 |
+| **2D image classification** |  |  |  |  |  |
+| MIMIC CXR\*\* | Macro F1 (top 5 conditions) | 81.2 | 88.9 | 89.5 | 90.0 |
+| CheXpert CXR | Macro F1 (top 5 conditions) | 32.6 | 48.1 | 48.2 | 49.9 |
+| CXR14 | Macro F1 (3 conditions) | 32.0 | 50.1 | 48.4 | 45.3 |
+| PathMCQA\* (histopathology) | Accuracy | 37.1 | 69.8 | 70.0 | 71.6 |
+| WSI-Path\* (whole-slide histopathology) | ROUGE | 2.3 | 2.2 | 49.4 | 4.1 |
+| US-DermMCQA\* | Accuracy | 52.5 | 71.8 | 73.5 | 71.7 |
+| EyePACS\* (fundus) | Accuracy | 14.4 | 64.9 | 76.8 | 75.3 |
+| **Disease Progression Classification (Longitudinal)** |  |  |  |  |  |
+| MS-CXR-T | Macro Accuracy | 59.0 | 61.11 | 65.7 | 50.1 |
+| **Visual question answering** |  |  |  |  |  |
+| SLAKE (radiology) | Tokenized F1 | 40.2 | 72.3 | 59.7\*\*\*\* | 70.3 |
+|  | Accuracy (on closed subset) | 62.0 | 87.6 | 82.8 | 85.9 |
+| VQA-RAD\*\*\* (radiology)   | Tokenized F1  | 33.6 | 49.9 | 48.1 | 46.7 |
+|  | Accuracy (on closed subset) | 42.1 | 69.1 | 70.2 | 67.1 |
+| **Region of interest detection** |  |  |  |  |  |
+| Chest ImaGenome: Anatomy bounding box detection | Intersection over union | 5.7 | 3.1 | 38.0 | 16.0 |
+| **Multimodal medical knowledge and reasoning** |  |  |  |  |  |
+| MedXpertQA (text \+ multimodal questions) | Accuracy | 16.4 | 18.8 | 20.9 | 26.8 |
+
+\* Internal datasets. CT Dataset 1 and MRI Dataset 1 are described below \-- for
+evaluation, perfectly balanced samples were drawn per condition. US-DermMCQA is
+described in [Liu et al. (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. WSI-Path is a
+dataset of deidentified H\&E WSIs and associated final diagnosis text from
+original pathology reports, comprising single WSI examples and previously
+described in [Ahmed et al. (2024, arXiv)](https://arxiv.org/pdf/2406.19578).
+EyePACS is a dataset of fundus images with classification labels based on
+5-level diabetic retinopathy severity (None, Mild, Moderate, Severe,
+Proliferative). A subset of these datasets are described in more detail 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).
+
+\*\*\*\* While MedGemma 1.5 4B exhibits strong radiology interpretation
+capabilities, it was less optimized for the SLAKE Q\&A format compared to
+MedGemma 1 4B. Fine-tuning on SLAKE may improve results.
+
+#### 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 MedGemma 1.5 4B against
+a fine-tuned version of MedGemma 1 4B, and the MedGemma 1 27B base model.
+
+| Task / Dataset | Metric | MedGemma 1 4B (tuned for CXR) | MedGemma 1.5 4B | MedGemma 1 27B |
+| :---- | :---- | :---- | :---- | :---- |
+| **Chest X-ray report generation** |  |  |  |  |
+| MIMIC CXR \- RadGraph F1 |  | 30.3 | 27.2 | 27.0 |
+
+#### Text evaluations
+
+MedGemma 1.5 4B was evaluated across a range of text-only benchmarks for medical
+knowledge and reasoning. Existing results for MedGemma 1 variants and Gemma 3
+are shown for comparison.
+
+| Dataset | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
+| :---- | :---- | :---- | :---- | :---- |
+| MedQA (4-op) | 50.7 | 64.4 | 69.1 | 85.3 |
+| MedMCQA | 45.4 | 55.7 | 59.8 | 70.2 |
+| PubMedQA | 68.4 | 73.4 | 68.2 | 77.2 |
+| MMLU Med | 67.2 | 70.0 | 69.6 | 86.2 |
+| MedXpertQA (text only) | 11.6 | 14.2 | 16.4 | 23.7 |
+| AfriMed-QA (25 question test set) | 48.0 | 52.0 | 56.0 | 72.0 |
+
+#### Medical record evaluations
+
+EHR understanding and interpretation was evaluated for synthetic longitudinal
+text-based EHR data and real-world de-identified discharge summaries via
+question-answering benchmark datasets for MedGemma 1.5 4B, MedGemma 1 variants,
+and Gemma 3 4B.
+
+| Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
+| :---- | :---- | :---- | :---- | :---- | :---- |
+| EHRQA\* | Accuracy | 70.9 | 67.6 | 89.6 | 90.5 |
+| EHRNoteQA | Accuracy | 78.0 | 79.4 | 80.4 | 90.7 |
+
+\* Internal dataset
+
+#### Document understanding evaluations
+
+Evaluation of converting unstructured medical lab reports documents
+(PDFs/images) into structured JSON data.
+
+| Task / Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B  |
+| :---- | :---- | :---- | :---- | :---- | :---- |
+|  **PDF-to-JSON Lab Test Data Conversion** |  |  |  |  |  |
+| EHR Dataset 2\* (raw PDF to JSON) | Macro F1 (average over per document  F1 scores)  | 84.0 | 78.0 | 91.0 | 76.0 |
+|  | Micro F1 (F1 across all extracted data fields) | 81.0 | 75.0 | 88.0 | 70.0 |
+| EHR Dataset 3\* (raw PDF to JSON) | Macro F1 | 61.0 | 50.0 | 71.0 | 66.0 |
+|  | Micro F1 | 61.0 | 51.0 | 70.0 | 69.0 |
+| Mendeley Clinical Laboratory Test Reports (PNG image to JSON) | Macro F1 | 83.0 | 85.0 | 85.0 | 69.0 |
+|  | Micro F1 | 78.0 | 81.0 | 83.0 | 68.0 |
+| EHR Dataset 4\* | Macro F1 | 41.0 | 25.0 | 64.0 |  |
+|  | Micro F1 | 41.0 | 33.0 | 67.0 |  |
+
+\* Internal datasets.
+
+### 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
+    potentially harmful responses 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
+and 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 compared
+to previous Gemma models. All testing was conducted without safety filters to
+evaluate the model capabilities and behaviors. For both text-to-text and
+image-to-text 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 across multiple datasets, tasks and 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 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).
+*   [MS-CXR-T](https://physionet.org/content/ms-cxr-t/1.0.0/): Microsoft
+    Research Health Futures, Microsoft Research.
+*   [ChestX-ray14](https://pmc.ncbi.nlm.nih.gov/articles/PMC6476887/): National
+    Institutes of Health \- Clinical Center.
+*   [SLAKE](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.
+*   [MedMCQA](https://arxiv.org/abs/2203.14371): This dataset was created by
+    Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu from Saama AI
+    Research, Chennai, India
+*   [PubMedQA](https://arxiv.org/abs/1909.06146): This dataset was created by
+    Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, Xinghua Lu from
+    the University of Pittsburg, Carnegie Mellon University and Google.
+*   [LiveQA](https://trec.nist.gov/pubs/trec26/papers/Overview-QA.pdf): This
+    dataset was created by Ben Abacha Asma, Eugene Agichtein Yuval Pinter and
+    Dina Demner-Fushman from the U.S. National Library of Medicine, Emory
+    University and Georgia Institute of Technology.
+*   [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.
+*   [ISIC](https://www.isic-archive.com/): International Skin Imaging
+    Collaboration is a joint effort involving clinicians, researchers, and
+    engineers from various institutions worldwide.
+*   [Mendeley Clinical Laboratory Test
+    Reports](https://data.mendeley.com/datasets/bygfmk4rx9/2): This dataset is
+    hosted on Mendeley and includes 260 clinical laboratory test reports issued
+    by 24 laboratories in Egypt.
+*   [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE): Istanbul
+    Medipol University Mega Hospital and University of Zurich / ETH Zurich.
+
+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.
+
+*   **CT dataset 1:** De-identified dataset of different axial CT studies across
+    body parts (head, chest, abdomen) from a US-based radiology outpatient
+    diagnostic center network.
+*   **MRI dataset 1:** De-identified dataset of different axial multi-parametric
+    MRI studies across body parts (head, abdomen, knee) 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.
+*   **Dermatology dataset 4**: De-identified dataset featuring multiple images
+    and longitudinal visits and records from Japan.
+*   **Dermatology dataset 5**: Dermatology dataset featuring unlabeled images.
+*   **Dermatology dataset 6**: De-identified cases from adult patients with data
+    representing Fitzpatrick 5 or 6 skin types
+*   **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.
+*   **EHR dataset 2**: De-identified Lab Reports across different departments in
+    Pathology such as Biochemistry, Clinical Pathology, Hematology, Microbiology
+    and Serology
+*   **EHR dataset 3**: De-identified Lab Reports across different departments in
+    Pathology such as Biochemistry, Clinical Pathology, Hematology, Microbiology
+    and Serology from at least 25 different labs
+*   **EHR dataset 4**: Synthetic dataset of laboratory reports
+*   **EHR dataset 5**: Synthetic dataset of approximately 60,000 health-relevant
+    user queries
+
+### 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.
+*   **MS-CXR-T:** Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M.,
+    Coelho de Castro, D., Boecking, B., Sharma, H., Bouzid, K., Schwaighofer,
+    A., Wetscherek, M. T., Richardson, H., Naumann, T., Alvarez Valle, J., &
+    Oktay, O. (2023). MS-CXR-T: Learning to Exploit Temporal Structure for
+    Biomedical Vision-Language Processing (version 1.0.0). PhysioNet.
+    [https://doi.org/10.13026/pg10-j984](https://doi.org/10.13026/pg10-j984).
+*   **ChestX-ray14:** Wang, Xiaosong, Yifan Peng, Le Lu, Zhiyong Lu,
+    Mohammadhadi Bagheri, and Ronald M. Summers. "Chestx-ray8: Hospital-scale
+    chest x-ray database and benchmarks on weakly-supervised classification and
+    localization of common thorax diseases." In *Proceedings of the IEEE
+    conference on computer vision and pattern recognition*, pp. 2097-2106.
+    2017\.
+*   **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-UFES-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.
+*   **CAMELYON17:** Bandi, Peter, et al. "From detection of individual
+    metastases to classification of lymph node status at the patient level: the
+    camelyon17 challenge." *IEEE transactions on medical imaging* 38.2 (2018):
+    550-560.
+*   **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).
+*   **MedMCQA:** Pal, Ankit, Logesh Kumar Umapathi, and Malaikannan
+    Sankarasubbu. "Medmcqa: A large-scale multi-subject multi-choice dataset for
+    medical domain question answering." *Conference on health, inference, and
+    learning. PMLR,* 2022\.
+*   **PubMedQA:** Jin, Qiao, et al. "Pubmedqa: A dataset for biomedical research
+    question answering." *Proceedings of the 2019 conference on empirical
+    methods in natural language processing and the 9th international joint
+    conference on natural language processing (EMNLP-IJCNLP).* 2019\.
+*   **LiveQA:** Abacha, Asma Ben, et al. "Overview of the medical question
+    answering task at TREC 2017 LiveQA." *TREC.* 2017\.
+*   **AfriMed-QA:** 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).
+*   **HealthSearchQA:** Singhal, Karan, Shekoofeh Azizi, Tao Tu, S. Sara
+    Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales et al. "Large language
+    models encode clinical knowledge." *Nature* 620, no. 7972 (2023): 172-180.
+*   **ISIC**: Gutman, David; Codella, Noel C. F.; Celebi, Emre; Helba, Brian;
+    Marchetti, Michael; Mishra, Nabin; Halpern, Allan. "Skin Lesion Analysis
+    toward Melanoma Detection: A Challenge at the International Symposium on
+    Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging
+    Collaboration (ISIC)". eprint [arXiv:1605.01397.
+    2016](https://arxiv.org/abs/1605.01397)
+*   **Mendeley Clinical Laboratory Test Reports:** Abdelmaksoud, Esraa;
+    Gadallah, Ahmed; Asad, Ahmed (2022), “Clinical Laboratory Test Reports”,
+    Mendeley Data, V2, doi: 10.17632/bygfmk4rx9.2
+*   **CheXpert**: Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S.,
+    Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J.,
+    Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B.,
+    Langlotz, C. P., Patel, B. N., Lungren, M. P., & Ng, A. Y. (2019). CheXpert:
+    A Large Chest Radiograph Dataset with Uncertainty Labels and Expert
+    Comparison. arXiv:1901.07031
+*   **CT-RATE:** Hamamci, I. E., Er, S., Almas, F., Simsek, A. G., Esirgun, S.
+    N., Dogan, I., Dasdelen, M. F., Wittmann, B., Menze, B., et al. (2024).
+    CT-RATE Dataset. Hugging Face.
+    [https://huggingface.co/datasets/ibrahimhamamci/CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE)
+    and Hamamci, Ibrahim Ethem, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek,
+    Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann,
+    et al. 2024\. "Developing Generalist Foundation Models from a Multimodal
+    Dataset for 3D Computed Tomography." *arXiv preprint arXiv:2403.17834*.
+    [https://arxiv.org/abs/2403.17834](https://arxiv.org/abs/2403.17834)
+*   **EHRNoteQA**: Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha,
+    Hangyul Yoon, Kwanghyun Kim, Jeewon Yang, Seunghyun Won, Edward Choi. (2024)
+    “EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using
+    Discharge Summaries.” arXiv:2402.16040
+
+### 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 medical contexts (image and
+textual); however, the model has been trained using chest x-ray, histopathology,
+dermatology, fundus images, CT, MR, medical text/documents and electronic health
+records (EHR) data. Examples of tasks within MedGemma’s training include visual
+question answering pertaining to medical images, such as radiographs, document
+understanding, or providing answers to textual medical questions.
+
+### 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. 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
+
+#### MedGemma 4B IT
+
+*   Jan 13, 2026: Release of MedGemma 1.5 with improved medical reasoning,
+    medical records interpretation and medical image interpretation
+*   Jan 23, 2026: Updated generation config to use greedy decoding by default.
+    Sampling can still be allowed by users to achieve previous functionality.
+    Please see https://huggingface.co/docs/transformers/en/generation_strategies
+    for details.
+