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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
Centaur.AI’ latest study tackles human bias in crowdsourced AI training data using cognitive-inspired data engineering. By applying recalibration techniques, they improved medical image classification accuracy significantly. This approach enhances AI reliability in healthcare and beyond, reducing bias and improving efficiency in machine learning model training.
AI is transforming claims processing in financial services and insurance. By pairing automation with expert data annotation, companies reduce errors, accelerate resolutions, and ensure compliance. Centaur.ai delivers precise labeling for forms, emails, and documents, enabling AI models to handle repetitive tasks while humans focus on nuanced decisions.
The AI industry’s rapid data economy highlights that model performance depends on high-quality human annotation, not volume alone. The Verge shows a surge of data vendors chasing market share. Centaur.ai differentiates itself by embedding domain expertise and rigorous evaluation frameworks into its annotation process, delivering data that drives reliable, real world model performance.