谷歌在I/O 2025上推出了MedGemma，这是一个用于多模态医疗文本和图像理解的开源模型套件。基于Gemma 3架构，MedGemma旨在为开发者提供坚实的基础，以创建需要综合分析医疗图像和文本数据的医疗保健应用程序。该模型提供两种配置：4B参数多模态模型和27B参数文本模型，分别用于图像分类、解释和临床文本分析。开发者可以通过Hugging Face访问MedGemma，并可选择本地运行或通过Google Cloud部署。谷歌还提供了Colab笔记本等资源，以支持模型的微调和集成。

🖼️ MedGemma 4B：这是一个拥有40亿参数的多模态模型，能够处理医疗图像和文本。它使用了在去标识化医疗数据集上预训练的SigLIP图像编码器，这些数据集包括胸部X光片、皮肤病学图像、眼科图像和组织病理学切片。

📝 MedGemma 27B：这是一个拥有270亿参数的纯文本模型，专为需要深入理解医疗文本和临床推理的任务而优化。该模型经过专门的指令调整，适用于需要高级文本分析的应用。

🚀 部署与访问：开发者可以通过Hugging Face访问MedGemma模型，并同意Health AI Developer Foundations的使用条款。这些模型可以本地运行用于实验，或通过Google Cloud的Vertex AI部署为可扩展的HTTPS端点，用于生产级应用。

🩺 应用与用例：MedGemma作为多种医疗保健相关应用的基础模型，包括医疗图像分类、医疗图像解释和临床文本分析。例如，4B模型适用于分类放射学扫描和皮肤病学图像，而27B模型擅长理解和总结临床笔记，支持患者分诊和决策支持等任务。

🛠️ 适应与微调：虽然MedGemma提供了强大的基线性能，但鼓励开发者针对其特定用例验证和微调模型。可以使用提示工程、上下文学习和LoRA等参数高效微调方法来增强性能。谷歌提供指导和工具来支持这些适应过程。

At Google I/O 2025, Google introduced MedGemma, an open suite of models designed for multimodal medical text and image comprehension. Built on the Gemma 3 architecture, MedGemma aims to provide developers with a robust foundation for creating healthcare applications that require integrated analysis of medical images and textual data.

Model Variants and Architecture

MedGemma is available in two configurations:

MedGemma 4B: A 4-billion parameter multimodal model capable of processing both medical images and text. It employs a SigLIP image encoder pre-trained on de-identified medical datasets, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. The language model component is trained on diverse medical data to facilitate comprehensive understanding.MedGemma 27B: A 27-billion parameter text-only model optimized for tasks requiring deep medical text comprehension and clinical reasoning. This variant is exclusively instruction-tuned and is designed for applications that demand advanced textual analysis.

Deployment and Accessibility

Developers can access MedGemma models through Hugging Face, subject to agreeing to the Health AI Developer Foundations terms of use. The models can be run locally for experimentation or deployed as scalable HTTPS endpoints via Google Cloud’s Vertex AI for production-grade applications. Google provides resources, including Colab notebooks, to facilitate fine-tuning and integration into various workflows.

Applications and Use Cases

MedGemma serves as a foundational model for several healthcare-related applications:

Medical Image Classification: The 4B model’s pre-training makes it suitable for classifying various medical images, such as radiology scans and dermatological images.Medical Image Interpretation: It can generate reports or answer questions related to medical images, aiding in diagnostic processes.Clinical Text Analysis: The 27B model excels in understanding and summarizing clinical notes, supporting tasks like patient triaging and decision support.

Adaptation and Fine-Tuning

While MedGemma provides strong baseline performance, developers are encouraged to validate and fine-tune the models for their specific use cases. Techniques such as prompt engineering, in-context learning, and parameter-efficient fine-tuning methods like LoRA can be employed to enhance performance. Google offers guidance and tools to support these adaptation processes.

Conclusion

MedGemma represents a significant step in providing accessible, open-source tools for medical AI development. By combining multimodal capabilities with scalability and adaptability, it offers a valuable resource for developers aiming to build applications that integrate medical image and text analysis.

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