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“The nice thing about AI is that it never gets tired. So as we think about AI replacing radiologists, let's focus on AI replacing the tedious, humdrum, boring stuff that needs to be done very well. That's where AI has the greatest opportunity."
- Dr. Brad Erickson, MD, PhD, Mayo Clinic at fireside chat at RSNA

Paige, the global leader in end-to-end digital pathology solutions and clinical AI applications, was able to improve their model's F1 score from .6 to .83 and complete annotation 10 x faster by working with Centaur Labs to annotate their pathology slide dataset.

Our new Research product enables researchers to use our platform and labeling network for free, and our new APIs allow model developers to integrate our data annotation capabilities into their ML pipelines.

At RSNA we announced our Radiology AI Safety initiative in partnership with deepc and Segmed. Together we’ll make it easier to both identify opportunities to improve models and get the data to quickly retrain them.
From new research and regulatory approvals, to new datasets, here are some of our favorite updates in AI in healthcare.
Whether you're -
...Centaur Labs can help accelerate and improve your data annotation process.
Share the resources you're using, the research you're reading and publishing, and the roles you're hiring for and we’ll share with the community next month.
Until next month,
Erik and the Centaur Labs team
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.
Radiology AI models are only as strong as their annotations. Centaur.ai engineers quality through collective intelligence, combining expert crowds, benchmarking, and performance-based incentives to produce validated data for model training and evaluation. Visit our RSNA booth to see how we make radiology AI accuracy inevitable at scale.
Drones and satellites reveal emissions that once went unseen. But the true value lies in expert annotation that turns raw images into intelligence. High-quality data annotation is essential for training and evaluating AI models, ensuring accurate detection, compliance, and trust in a future where proof is the standard.