Solutions

XANA-Powered Data Annotation
for Smart Medical Devices.

With Xana Labs, medical device innovators can access high-quality medical data annotations for next generation AI-enabled devices and clinical decision support at an unprecedented speed and scale.

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Use Cases

Accelerating the advancement of medical devices by integrating AI and advanced data annotation capabilities.

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Clinical decision support

AI allows providers to leverage big data at the bedside, supporting decisions throughout the care continuum, and driving improved patient outcomes.

  • tick Image analysis
  • tick Assisted diagnosis
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AI-enabled surgery

Teams are building AI to help make complex, time-sensitive diagnoses, increase the accuracy and efficiency of surgery, and prevent postoperative complications.

  • tick Robotic surgery
  • tick AR/VR surgery simulation
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Patient monitoring

Physicians, caregivers and patients can make care decisions based on both patient-reported symptoms and data streams from next generation medical devices.

  • tick Wearables
  • tick Connected devices
  • tick Disease management software

Why Xana?

XANA accelerates AI development with speedy access to millions of diverse data points. Rooted in quality, it ensures consistent performance, advanced insights, and top-notch privacy across various industries and data types.

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Scale data annotation exponentially

Stop annotating data slowly with in-house teams or outsourcing to teams whose quality you can't control.
Access the Centaur Labs network of thousands of medical doctors, professionals, researchers and students for skilled annotation

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Take control of annotation quality

You define quality. We rigorously measure and manage it.
Incentivize labeler performance with small batch, mobile-first competitions. Labelers are only paid for performance and give 100% of their effort on every case

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Master your dataset

Proactively understand the nuances of your dataset, so your data works with you, not against you.
Leverage case-level insights to inform model development e.g. precision-recall curves, label distribution, labeler agreement

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