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LangExtract is an open-source Python library that uses large language models (LLMs) to reliably and programmatically extract structured information from unstructured text. It's designed to solve the challenges of manually processing large volumes of documents like clinical notes or legal reports. We showcase Radiology use cases using Gemini. Read More

In May of this year, we expanded the HAI-DEF collection with MedGemma, a collection of generative models based on Gemma 3 that are designed to accelerate healthcare and lifesciences AI development. Today, we're proud to announce two new models in this collection. Read More

We invited doctors, developers and researchers to come togeher in Google Paris and prototype new medical solutions using our open models. Read More

The weekend before its official debut at Google I/O, we provided a select group of developers in San Francisco with early access to MedGemma, challenging them to solve real-world problems in healthcare. Read More

At Google I/O, we announced MedGemma, Google's most capable open model for multimodal medical text and image comprehension. It is a collection of Gemma 3 variants (4B multimodal and 27B text-only), fine-tuned for medical tasks. Read More

TxGemma is Google's first open LLM for predicting properties of therapeutics. It is a collection of Gemma 2 variants (2B, 9B, and 27B), fine-tuned for therapeutic tasks using data from the Therapeutic Data Commons. It includes TxGemma-Predict for direct task outputs and TxGemma-Chat for conversational applications. Read More

Our Health Acoustic Representations (HeAR) model which we published last August is now openly available as an open model in the HAI-DEF collection. Read More

We challenged researchers, developers, clinicians, and entrepreneurs to use HAI-DEF models in solving real world problems. Read More

We are open-sourcing our foundation models under flexible terms of use, enabling developers and researchers to use our pretrained models at no cost, customize them, and use for product research & development. The models are available in Google Model Garden and Hugging Face. While they are not exclusive to Google Cloud, you'll find them production-ready when deployed using Google Model Garden. Read More

We've created Computed Tomography (CT) Foundation, a new AI tool that takes 3D CT scans and encodes them into small, information-rich numerical embeddings. This breakthrough allows researchers to build powerful new models for CT studies with significantly less data and computational resources than ever before. We've made this models available to the researchers as experimental APIs. Read More

We've developed Health Acoustic Representations (HeAR), an AI model that learns to extract health insights from bioacoustic sounds like coughs. We show that using this embedding model new models for a wide range of diseases can be developed with less data and computational resources. We've made the model available to researchers as an experimental API. Read More

We've developed new deep learning models in Dermatology and Pathology that can encode medical images into compressed numerical vectors called embeddings. These embeddings capture an image's key features, allowing new models for various medical tasks to be developed with far less data and compute than traditional methods. We've made these models available to researchers as experimental APIs. Read More

In this research we explore Med-Gemini, a family of models based on Gemini that have been fine-tuned for medical tasks. Read More

In this research we explore integrating various sources like images, lab results, and patient notes to build a more comprehensive and assistive medical AI. Read More

We've developed a new machine learning method to create pre-trained neural networks that convert chest X-Ray (CXR) images into data-rich vectors (embeddings). We show that with this technique model researchers can train powerful new models using far less data and computational power than traditional methods. Read More