Managing Model Drift in Healthcare Machine Learning

Healthcare machine learning systems face a major challenge after deployment: model drift.

Drift occurs when real-world data diverges from the training dataset.

Common causes include:

• demographic changes
• evolving disease prevalence
• updated clinical practices

Managing healthcare ML systems requires:

• drift detection pipelines
• monitoring dashboards
• retraining strategies
• governance frameworks

Healthcare AI is not static software.

It is a dynamic decision support system requiring continuous lifecycle management.

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