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Aidoc Staff

Exploring Data Drift's Impact on AI

aiOS, Data Management, Deep Learning, System Integration
Table of Contents

Data drift refers to changes in the distribution of features an AI model receives in production, potentially causing a decline in the model’s performance. In imaging AI, for example, that could culminate in a less reliable algorithm at flagging suspected pathologies on a given image. Data drift can corrupt data, break processes and cause a host of other problems for modern data architectures. 

Data drift is a significant challenge for clinical AI algorithms. It can happen for several reasons, including the introduction of new protocols, the replacement of old machines or the evolving best practices for image acquisition.

With data draft, users may experience frustration when AI algorithms fail to detect relevant findings due to changes in protocols and metadata. They would see the brain in the images, maybe even the trace of a hemorrhage and wonder: Why didn’t AI analyze this case?

Amongst the popular integration options of the first wave of AI, the graphic below shows the difference between Point Solutions, Marketplaces and Platforms when confronted with issues related to data drift.

Intracranial Hemorrhage (ICH) Protocols at One Location Over 2.5 Years

A retrospective analysis comparing an advanced AI orchestration method (AIO) with a traditional rule-based metadata orchestration (MBO) was completed. As more protocols and human elements changed workflows overtime, our whitepaper highlights the decline in weekly ICH patient capture at this location.

Key highlights

  • AIO identified 66,581 ICH scans, while MBO identified 61,902 scans, resulting in a 7.0% decrease for ICH
  • THe mean weekly decrease was 0.1% with a standard deviation of 4.2%
  • The maximum weekly decrease observed was 17.5% for ICH

This segment of our whitepaper, The Clinical AI Scorecard: How Different Integration Approaches Handle Deployment Challenges, gives a brief overview of the challenges of data drift with some of the modern AI implementation strategies. 

Click here to download Pt. I of the whitepaper, learn more about the ICH analysis mentioned above and explore  the benefits of an AI platform for implementation and deployment.

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