Retraining AI Models with Clean Data

Use when: Initial training artifacts mislead AI interpretation.

How it works:

  1. Identify misleading shortcuts the AI may use, such as UI artifacts mistaken for diagnostic cues.
  2. Replace artifact-heavy training methods with clean, unmarked data to eliminate reliance on false indicators.
  3. Utilize manual annotation directly on training samples to ensure AI learns genuine patterns.

AI training image showing an MRI scan with red circles and a square over lesions — illustrating how the model learned to detect the annotation shapes rather than the medical abnormalities.

Tip: Accept potential increase in manual workload short-term for more accurate long-term model training.

Tool (optional): Azure Vision AI can be an effective platform for balancing compute requirements and managing training data.

→ See also: Behavioral UX Strategies for Sustainable AI Adoption

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