After evaluating and selecting an AI model, how can we be sure it will continue to work well in our practice? Ongoing monitoring is necessary to uphold the accuracy of any algorithm. This video reviews why it’s important to monitor your AI models and includes steps developers, vendors and radiologists can take to uphold their models.
Importance of Testing and Monitoring of AI Models
By: Woojin Kim, MD
- The importance of post-implementation surveillance.
- The definition and impacts of model decay.
- How to define concept drift.
- How to define data drift.
- Steps for radiologists in evaluating AI algorithms.
Monitoring AI Performance in Clinical Practice
By: Bibb Allen Jr., MD, FACR
- Common causes of model drift and decay.
- How to use Assess-AI and AI-LAB™ for real-world performance monitoring.
- How monitoring AI can help developers improve model performance.
- The benefits of continuous learning.
Assess-AI and AI-PROBE: Monitoring Algorithm Performance in Clinical Practice
In this video, radiologists will learn:
- How Assess-AI works in post-market surveillance.
- An overview of AI-PROBE and how it is used for performance evaluation.
- How AI-PROBE can augment data repositories, such as Assess-AI.