What is model drift and why does it matter clinically?

Model drift is the gradual decline in an AI model's accuracy as real-world data, such as patient population or imaging equipment, shifts away from what the model was originally trained on. It matters clinically because a tool that was accurate at go-live is not guaranteed to stay accurate indefinitely, which is why ongoing monitoring, rather than one-time validation, is considered standard practice. See Model Drift.