What is data drift and why does it matter for deployed clinical AI?

Data drift occurs when the statistical properties of input data change over time relative to the data the AI model was trained on, leading to performance degradation. In healthcare, drift can result from changing patient demographics, evolving clinical practices, updated coding standards, or new disease patterns. Continuous monitoring using automated drift detection pipelines is essential, with predefined trigger thresholds for model recalibration or withdrawal.