
Health AI Developer Foundations (HAI-DEF)
Groups models released for use in health AI by Google. Read more about HAI-DEF at https://developers.google.com/health-ai-developer-foundations
- Text Generation • 27B • Updated • 53k • 267
google/medgemma-4b-pt
Image-Text-to-Text • 4B • Updated • 6.74k • 93google/medgemma-4b-it
Image-Text-to-Text • 4B • Updated • 101k • 410google/txgemma-9b-predict
Text Generation • 9B • Updated • 4.47k • 23google/txgemma-9b-chat
Text Generation • 9B • Updated • 2.36k • 37google/txgemma-27b-chat
Text Generation • 27B • Updated • 384 • 52google/txgemma-27b-predict
Text Generation • 27B • Updated • 31.9k • 31google/txgemma-2b-predict
Text Generation • 3B • Updated • 9.91k • 38
google/hear-pytorch
Image Feature Extraction • Updated • 973 • 10Note Health Acoustic Representations accelerates AI development for bioacoustic data e.g., coughs or breath sounds. The model is pre-trained on 300 million 2-second audio clips to produce embeddings that capture dense features relevant for bioacoustic applications.
google/hear
Updated • 125 • 23Note Health Acoustic Representations accelerates AI development for bioacoustic data e.g., coughs or breath sounds. The model is pre-trained on 300 million 2-second audio clips to produce embeddings that capture dense features relevant for bioacoustic applications.
google/derm-foundation
Image Classification • Updated • 642 • 50Note Derm Foundation accelerates AI development for skin image analysis. The model is pre-trained on large amounts of labeled skin images to produce embeddings that capture dense features relevant for dermatology applications.
google/cxr-foundation
Image Classification • Updated • 110 • 81Note CXR Foundation accelerates AI development for chest X-ray image analysis. The model is pre-trained on large amounts of chest X-rays paired with radiology reports. It produces language-aligned embeddings that capture dense features relevant for chest X-ray applications.
google/path-foundation
Image Classification • Updated • 79 • 50Note Path Foundation accelerates AI development for histopathology image analysis. The model uses self-supervised learning on large amounts of digital pathology data to produce embeddings that capture dense features relevant for histopathology applications.