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Blog

We’re thrilled to share that Centaur Labs, in collaboration with our esteemed partners at Brigham and Women’s Hospital (BWH), has been awarded a $750,000 grant from the Massachusetts Life Sciences Center (MLSC) through their Bits to Bytes Program. This grant, part of the Healey-Driscoll Administration’s recent $19.8 million commitment to advancing life sciences innovation, underscores the importance of our joint research in respiratory diagnostics and lung ultrasound technology.
The MLSC’s Bits to Bytes Program, established in 2018, provides support for scientific initiatives that generate and leverage large datasets to tackle vital life sciences challenges. This year, Bits to Bytes has allocated over $3 million across four transformative projects, further strengthening Massachusetts’ role as a global leader in life sciences innovation.
Governor Maura Healey announced this year’s grant recipients at the World Medical Innovation Forum, emphasizing Massachusetts’ dedication to healthcare advancement and research equity. We are deeply grateful to MLSC and the Healey-Driscoll Administration for their commitment to fostering impactful research at the intersection of artificial intelligence and healthcare.
For a demonstration of how Centaur can facilitate your AI model training and evaluation with greater accuracy, scalability, and value, click here: https://centaur.ai/demo
Learn more about this exciting announcement here: MLSC Announcement
Learn more about how Centaur.ai is working with the Brigham and Women's Hospital team to develop multiple AI applications for point-of-care ultrasound.
AI-driven quality control in robotics and manufacturing depends on precisely labeled data. Centaur.ai delivers high-accuracy annotations at scale, combining human expertise with advanced tools to ensure reliable defect detection and production efficiency. Better data means smarter, safer automation.
AI models in climate and energy depend on accurate data, not just algorithms. Centaur.ai delivers expert-validated, edge-aware labels that adapt to shifting seasons, regions, and infrastructure. From tagging satellite imagery to QA-ing emissions outputs, our collective intelligence approach ensures more reliable insights for planet-scale challenges.