Bringing foundational models to the front lines of TB screening

From point solution to open foundations: HAI-DEF and MedGemma

A custom solution with a single partner cannot address the global TB burden alone. To scale impact, we shifted from isolated deployments to building open, adaptable foundations that allow developers and health ministries to create solutions tailored to local needs.

Through the Health AI Developer Foundations (HAI-DEF), we open-sourced our chest X-ray embeddings and MedSigLIP, a lightweight vision-language model trained to align medical images, such as chest X-rays, with the clinical language used to describe them. Because MedSigLIP understands both images and text, developers can use it to classify or search images with plain-language labels, or fine-tune it for a specific task with only a small set of local examples. This lets local teams build specialized models on modest regional datasets and reach strong performance without massive compute resources.

Building on this, we released MedGemma, a family of open-weight multimodal models (4B and 27B parameter configurations). MedGemma advances from basic classification to multimodal clinical reasoning: it synthesizes radiographic findings with clinical history, answers clinician queries in natural language, and runs locally on-device without internet access. Since its launch, MedGemma has seen more than 10 million downloads worldwide.