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In healthcare AI, model development is rarely the greatest challenge. The real issue is ensuring that the underlying data is accurate, measurable, and trustworthy.
HumanX 2026 (April 6–9, Moscone Center) brings together 6,500 enterprise AI leaders who have moved past the hype and are focused on deployment. That's exactly where we belong.
Most teams don't have a labeling problem. They have a quality measurement problem. A single clinician reviewing a CT scan or pathology slide introduces bias, fatigue, and variance that's invisible until it's too late. Ground truth built on one expert opinion isn't ground truth; it's a guess.
Centaur was built to solve this. Our approach, born out of MIT research on collective intelligence, routes every annotation through a competitive network of 50,000+ credentialed medical experts, then combines only the top-performing results. The output isn't just faster (10–20x vs. in-house teams). It's measurably better: AUC scores climb from 0.87 to 0.92. F1 scores jump from 0.6 to 0.83. And every label comes with consensus data to back it up.
That's the difference between data you hope is of sufficient quality and data that you can confidently document for an FDA submission.
Enterprise AI buyers at HumanX, CTOs, Chief AI Officers, and VP Product leaders are no longer asking whether to build healthcare AI. They're asking how to trust it. The answer starts with measured training data, not assumed.
If you’re attending HumanX and working through annotation bottlenecks, FDA-ready dataset requirements, or evaluation challenges for clinical LLMs, there’s a good chance we’re already thinking about the same problems.
We work with teams that have moved beyond experimentation and now face the realities of deployment. Teams that need their data to stand up not just in training environments but also in regulatory and clinical settings.
To meet with us on site, just click here to set up a time. We look forward to the conversation!
Radiology AI requires engineered annotation quality for training and evaluation to avoid dangerous clinical error. Centaur uses collective intelligence to outperform individual annotators and create reliable labels for imaging tasks like stroke detection and tumor classification, producing scientifically trustworthy datasets for LLM evaluation and high stakes medical AI applications.
AI is fast, but accuracy remains the real barrier to safe deployment. This post explains how poor data quality, collapsed expert disagreement, and weak evaluation create false confidence in production AI. It shows how collective intelligence, gold-standard labeling, and human-in-the-loop workflows at Centaur.ai build auditable, high-accuracy datasets for high-stakes applications.
Listen to Co-founder and CEO Erik Duhaime talk about the origins of Centaur Labs and the future of medical data labeling.