AI Monitoring After Deployment Needs More Than Test-Set Performance Checks

Radiology AI models require continuous, real-world postdeployment monitoring beyond test sets to detect silent failures and maintain safety, as performance can drift with changing data. A commentary highlights the lack of standardized frameworks for this critical phase.

• This commentary (RadioGraphics) discusses the urgent need for systematic monitoring of AI models after clinical release. Exact quantitative benchmarks for drift detection are not defined in this article.
• Recommends shifting focus from regulatory approval benchmarks to ongoing quality control, comparing it to established practices in laboratory medicine.
• Single-center implementation or pre-deployment test sets may not capture the heterogeneity of real-world imaging equipment, protocols, and patient demographics.

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