
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 • Updated • 15.6k • 205
google/medgemma-4b-pt
Image-Text-to-Text • Updated • 4.24k • 80google/medgemma-4b-it
Image-Text-to-Text • Updated • 32.8k • 311google/txgemma-9b-predict
Text Generation • Updated • 1.93k • 22google/txgemma-9b-chat
Text Generation • Updated • 9.7k • 36google/txgemma-27b-chat
Text Generation • Updated • 1.31k • 49google/txgemma-27b-predict
Text Generation • Updated • 17.2k • 28google/txgemma-2b-predict
Text Generation • Updated • 9.79k • 36
google/hear-pytorch
Image Feature Extraction • Updated • 182 • 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 • 136 • 22Note 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 • 663 • 47Note 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 • 85 • 73Note 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 • 149 • 47Note 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.