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


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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

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Data Labeling Options

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

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Collecting Medical Data

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

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Evaluating Accuracy

Grasp how to evaluate the accuracy of your medical data labels and understand where traditional methods fall short

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Data Cleaning and Enrichment

Understand ways to clean, classify and segment medical data and when to employ each labeling method

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Collective Intelligence

Discover a new method for data labeling that offers superior accuracy vs other methods by aggregating multiple opinions