Bringing foundational models to the front lines of TB screening

Getting access to timely healthcare often comes down to an accident of geography: where you live, what resources your local clinic possesses, and whether a specialist is within reach.

Nowhere is this disparity more acute than in the fight against tuberculosis (TB). Despite being preventable and curable, TB claims more than 1.23 million lives annually. A primary bottleneck is radiology: For those in high-burden settings, from people who live in remote rural areas to miners working deep underground, radiologists are scarce, and imaging infrastructure is often days away. As a result, millions of active cases go undetected, perpetuating transmission.

Over the past several years, our team across Google Research and Google for Health has worked to change that trajectory: moving from an AI-based tool that builds on our existing work in medical imaging to identify people with presumptive TB for follow-up testing to prospective clinical trials, regulatory clearance with local partners, and ultimately open foundation models like MedGemma that empower public health systems worldwide. This week at IDWeek 2026 in Washington, D.C., we are sharing how we’re bringing health AI research to reality, and how foundational models can democratize TB screening at global scale.

Proving AI-based tools can read chest radiographs

Our work began with a fundamental question: Can AI-based tools interpret chest radiographs (CXRs) with the accuracy of an expert radiologist for TB screening?

Chest radiography is the frontline imaging modality for pulmonary screening, but detecting subtle signs of TB like apical infiltrates (fluid in the top portion of the lung) and cavitary lesions (tissue damage) requires specialized expertise that is rarely available in rural clinics. In 2019, we published a benchmark in Radiology, demonstrating that deep learning algorithms could detect active pulmonary TB at performance levels matching experienced radiologists.