commit 3a4b4d8027c42cdff6d6457192b97699465f7722 Author: ModelHub XC Date: Tue Sep 22 06:43:14 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: unsloth/medgemma-4b-it-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..2b7cae7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,76 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb 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diff=lfs merge=lfs -text +medgemma-4b-it-UD-Q6_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +medgemma-4b-it-UD-Q8_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +mmproj-BF16.gguf filter=lfs diff=lfs merge=lfs -text +mmproj-F16.gguf filter=lfs diff=lfs merge=lfs -text +mmproj-F32.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..f869819 --- /dev/null +++ b/README.md @@ -0,0 +1,771 @@ +--- +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-it +tags: +- medical +- unsloth +- radiology +- clinical-reasoning +- dermatology +- pathology +- ophthalmology +- chest-x-ray +--- +
+

+ Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. +

+
+ + + + + + + + + +
+
+ + +# 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. 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