India-based health AI company Qure.ai has received a multimillion-dollar grant from the Gates Foundation to develop AI-enabled point-of-care ultrasound tools and an open global lung-health dataset. The initiative targets earlier detection of tuberculosis and pneumonia in under-resourced regions, where delayed diagnosis remains a leading cause of preventable deaths.
For global health systems struggling with workforce shortages and delayed diagnostics, early detection of lung disease remains one of the hardest gaps to close. That challenge is now at the center of a new initiative by Qure.ai, which on January 22 said it has secured a major grant from the Gates Foundation to expand AI-driven diagnostics for tuberculosis and pneumonia.
The funding will support the development of AI-powered point-of-care ultrasound algorithms designed to work in low-resource clinical settings, alongside the creation of a large open-source, multi-modal lung health database. Qure.ai said the effort is aimed at accelerating earlier diagnosis of two of the world’s deadliest but treatable infectious diseases, particularly in low- and middle-income countries.
Tuberculosis alone causes an estimated 1.23 million deaths each year, while pneumonia kills around 2 million people annually, including roughly 700,000 children under the age of five. Both conditions are curable when detected early, yet access to trained radiologists, advanced imaging, and timely lab testing remains limited in many regions where disease burden is highest.
At the core of the grant is a plan to assemble a non-identifiable, open-access dataset aligned with World Health Organization lung-health diagnostic pathways. According to Qure.ai, the database will combine chest X-rays, thoracic ultrasound, high-resolution CT scans, cough and lung sound recordings, clinical histories, and laboratory or biological markers. The goal is to enable researchers and developers globally to build, validate, and refine new AI models for lung disease detection.
For Qure.ai, the move builds on more than a decade of deploying AI-enabled imaging tools in remote and underserved settings. The company says its chest X-ray AI systems have already been used in some of the most challenging environments, from rural sub-Saharan Africa to high-altitude regions in Asia, reducing tuberculosis diagnosis timelines from weeks to as little as one or two days in some cases, even without an on-site clinician.
Founder and CEO Prashant Warier said the grant allows the company to extend that experience beyond X-ray into ultrasound, a modality that is cheaper, portable, and more suitable for frontline care. “With this grant, we are excited to leverage our expertise further to scale and reach more people,” he said in a statement.
Point-of-care ultrasound has long been viewed as a promising diagnostic tool in low-resource environments, but its effectiveness depends heavily on operator skill. Qure.ai’s approach is to use AI algorithms as a decision-support layer, helping health workers identify patterns associated with TB and pneumonia even with limited training.
From a broader ecosystem perspective, the grant reflects a growing shift among global health funders toward open data and interoperable AI infrastructure, rather than isolated proprietary tools. By making the dataset open-source, Qure.ai is positioning the project as a shared foundation for innovation across academia, startups, and public health agencies.
Internally, the company is framing the initiative as part of a wider focus on pediatric and population-level lung health. “A child dies of pneumonia every 43 seconds,” said Dr. Justy Antony Chiramal, project lead and clinical director for global health innovation at Qure.ai, underscoring the urgency of improving diagnostics and access to care.
Qure.ai
Qure.ai currently operates in more than 105 countries across over 4,800 clinical sites, with AI products spanning tuberculosis, lung cancer, stroke, and other neurocritical conditions. The company maintains regional offices in New York, London, Dubai, and Mumbai, and was named a TIME100 Most Influential Company in 2025.
For policymakers and health system operators, the outcome of this project will be closely watched. If AI-enabled ultrasound can reliably extend early lung disease detection to clinics with minimal infrastructure, it could reshape how infectious diseases are screened and managed at the front lines of care.
