Qure.ai has been awarded an $8 million grant from the Gates Foundation to develop AI-powered point-of-care ultrasound (POCUS) tools focused on earlier detection of tuberculosis (TB) and pneumonia, according to a Linkedin post by Prashant Warier, Co-Founder and CEO of Qure.ai.
In his statement, Warier framed the grant as one that would help bridge critical diagnostic gaps in those very areas where access to medical imaging and specialist expertise remains scant, including in first-level clinics with limited infrastructure.
“If AI is going to close health equity gaps, it must work where the burden is highest, in clinics with intermittent electricity, minimal infrastructure, and few specialists,” Warier wrote, describing the initiative as designed for real-world constraints rather than ideal hospital conditions.
What Qure.ai says the grant will support?
Warier said the Gates Foundation grant will fund three major efforts:
1) A large open multimodal database
Qure.ai plans to create what it described as the largest open, multimodal database bringing together multiple diagnostic inputs, including:
- Ultrasound
- Chest X-ray
- Lung sounds
- Laboratory markers
The company said the dataset will be made available to researchers and innovators globally, with the intent of accelerating new work in AI-assisted clinical decision-making, particularly for TB and pneumonia.
2) AI for point-of-care ultrasound workflows
The core product direction is building AI that can work inside portable ultrasound workflows, enabling earlier detection support in settings where traditional imaging access is limited.
3) Decision support for frontline health workers
A key theme of the announcement was shifting diagnostic capability closer to patients, so that health workers at the point of care, not only specialists in large hospitals, can make faster, higher-stakes decisions.
“From two-week delays to 30-second results”
Warier also positioned the project as an extension of what he described as Qure.ai’s work over the last decade to take AI-enabled imaging beyond major urban centers.
We’ve been able to advance the diagnostic timeframe from a two-week delay to a result in 30 seconds, in some cases where a clinician might not even be present, in areas as varied as sub-Saharan Africa, Southeast Asia, and elsewhere,” he said.
Although the new grant tier begins to extend further into the specific domain of ultrasonic workflows, the core objective continues to remain the same, and the concern still exists to reduce the delay between the point of concern and the point of action, above all when it involves the possibility of delay caused by possible infrastructural issues or the absence of specialists.
Why ultrasound and why now?
In Warier’s framing, the promise of point-of-care ultrasound is not just portability, it’s reach. Ultrasound devices can travel where fixed imaging systems cannot, but interpretation often requires training and experience. Qure.ai’s approach, as described in the post, is to embed AI into the workflow so the tool becomes more usable in non-tertiary, resource-limited clinics.
The company says the focus is simple: help frontline providers “see and act sooner.”
Warier said more details are expected in an accompanying press release and thanked the Gates Foundation and partners for supporting the initiative.
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