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Blog
Centaur.ai has teamed up with Consensus, an emerging AI-powered scientific search engine, to accelerate AI development with high-quality, expert annotations. Faced with the challenge of extracting precise information from scientific literature, Consensus turned to Centaur.ai’s collective labeling platform, which combines robust software with a vetted network of scientists and doctors.
In just two weeks, Centaur.ai delivered over 100,000 unique expert labels, meeting Consensus’s needs for scale and accuracy. “Because of the unique Centaur.ai data labeling solution that not only provides the software to complete labeling tasks but also provides a network of experts… it was a no-brainer to work together,” said Consensus CEO Eric Olson. Centaur.ai managed the full lifecycle, from sourcing annotators to quality assurance, allowing Consensus to focus on refining its models. That rapid success laid the foundation for an ongoing collaboration to ensure their AI continues delivering the most accurate scientific insights.
Erik Duhaime, co-founder and CEO of Centaur.ai, reaffirmed the company’s commitment to advancing AI through expert-driven data: “We’re excited to partner with teams on the cutting edge of healthcare and AI… We’re thrilled to support them both as they move to general availability.” Moving forward, both teams aim to leverage this synergy to offer users instant access to rigorously vetted research findings, underpinned by scalable, collective expert intelligence.
Emphasized the importance of data curation practices in reducing bias in medical AI, promoting diverse datasets, expert collaboration, and fairness metrics for more equitable outcomes.
A new partnership between Protégé and Centaur Labs aims to accelerate AI development, driving innovation in healthcare and research technology.
Radiology AI improves acquisition, processing, interpretation, reporting, and long-term monitoring, but performance depends entirely on high-quality annotations. Centaur.ai delivers expert-reviewed, rigorously validated radiology labels at scale, enabling reliable LLM training and evaluation for clinical imaging. Strong data is the foundation of trustworthy radiology AI.