<ul><li>Post-deployment monitoring in clinical AI is underdeveloped, with only 9% of FDA-registered AI-based healthcare tools having a surveillance plan.</li><li>Existing monitoring approaches for AI in healthcare are manual, sporadic, and reactive, lacking efficiency for dynamic clinical model environments.</li><li>The paper proposes statistically valid and label-efficient testing frameworks for post-deployment monitoring to ensure reliability and safety in real-world deployment of AI-based healthcare tools.</li><li>By grounding monitoring in statistical rigor, the paper advocates for reproducibility and scientifically sound maintenance of the reliability of clinical AI systems, offering new research directions for the technical community.</li></ul>

Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health

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