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README.md

@@ -1,3 +1,752 @@
----
-license: mit
----
+---
+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
+base_model: google/medgemma-4b-pt
+tags:
+- medical
+- radiology
+- clinical-reasoning
+- dermatology
+- pathology
+- ophthalmology
+- chest-x-ray
+---
+
+# 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)
+variants that are trained for performance on medical text and image
+comprehension. Developers can use MedGemma to accelerate building
+healthcare-based AI applications. MedGemma currently comes in three variants: a
+4B multimodal version and 27B text-only and multimodal versions.
+
+Both MedGemma multimodal versions utilize a
+[SigLIP](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
+$ pip install -U transformers
+```
+
+**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-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": "system",
+        "content": [{"type": "text", "text": "You are an expert radiologist."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this X-ray"},
+            {"type": "image", "image": image}
+        ]
+    }
+]
+
+output = pipe(text=messages, max_new_tokens=200)
+print(output[0]["generated_text"][-1]["content"])
+```
+
+**Run the model directly**
+
+```python
+# pip install accelerate
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from PIL import Image
+import requests
+import torch
+
+model_id = "google/medgemma-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": "system",
+        "content": [{"type": "text", "text": "You are an expert radiologist."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this X-ray"},
+            {"type": "image", "image": image}
+        ]
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=True,
+    return_dict=True, return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+input_len = inputs["input_ids"].shape[-1]
+
+with torch.inference_mode():
+    generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+    generation = generation[0][input_len:]
+
+decoded = processor.decode(generation, skip_special_tokens=True)
+print(decoded)
+```
+
+### Examples
+
+See the following Colab notebooks for examples of how to use MedGemma:
+
+*   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
+*   **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
+*   **Model created**: July 9, 2025
+
+*   **Model version**: 1.0.1
+
+### 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 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.

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