Increase the Accuracy of your Labeled Medical Data
Examine the unique challenges with medical data labeling, the relative lack of accuracy produced by traditional data labeling methods, and a more accurate and cost-effective alternative based on collective intelligence.
We interviewed dozens of experts in the medical AI and annotation space and captured their insights into what we hope is a descriptive guide for anyone looking to enhance the accuracy and performance of their medical data labeling efforts.
Download the white paper to improve your medical data labeling
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 privacy concerns of PHI
Data Labeling Options
Review data labeling vendor models including in-house experts, hybrid teams and crowdsourced options
Collecting Medical Data
Explore ways to acquire medical data including open-source, in-house and through licensing and partnerships
Evaluating Accuracy
Grasp how to evaluate the accuracy of your medical data labels and understand where traditional methods fall short
Data Cleaning and Enrichment
Understand ways to clean, classify and segment medical data and when to employ each labeling method
Collective Intelligence
Discover a new method for data labeling that offers superior accuracy vs other methods by aggregating multiple opinions
