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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
Centaur.ai delivers high-quality annotations for neurological datasets where precision determines scientific validity. Through competitive collective intelligence, Centaur produces reproducible labels that strengthen model evaluation and training. NeurIPS attendees working with EEG, EMG, multimodal waveforms, or cognitive modeling should meet with Centaur to see how accuracy is engineered, not assumed.
Centaur Labs’ scaled expert annotation of colonoscopy videos, achieving high throughput and consensus, dramatically enhanced the quality and speed of Satisfai Health’s GI AI development.
Jill Goldsberry joins VistaTalks to break down why healthcare AI succeeds or fails based on the quality of its data. She explains how Centaur.ai blends expert labeling, scalable processes, and compliance-minded workflows to support trustworthy medical AI. This episode highlights the real work behind clinically meaningful machine-learning systems.