Whitepaper Title Page

The guide to accurate medical data labeling


We interviewed dozens of experts in the medical AI and annotation space, and captured their insights in this guide. 

Looking to enhance accuracy or performance of your medical data labeling pipeline?

Dive deep into the unique challenges, and explore a more accurate and cost-effective alternative, based on collective intelligence.


Download the whitepaper

What's inside?


Why medical data is different

Learn the unique challenges of working with medical data, including the high skill needed for labeling and managing PHI and privacy concerns.


Collecting medical data

Explore ways to acquire medical data including open-source and in-house datasets, or licensing and partnerships.


Data cleaning and enrichment

Understand ways to clean, classify and segment medical data, and when to employ different labeling methods.


Data labeling options

Review data labeling vendor models including in-house experts, hybrid teams and crowdsourced options.


Evaluating accuracy

Learn how to evaluate the accuracy of medical data labels, and understand where traditional methods fall short.


Collective intelligence

Discover a new data labeling method that aggregates multiple opinions to achieve superior accuracy