# Module 4 Narration

## Opening

Open with the professional setting: a radiology service deciding whether an imaging AI should advance from retrospective review to supervised pilot use. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.

## Middle

Move through the module in four passes:

1. Define **Classification and detection** in the context of AI in Medical Imaging.
2. Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.
3. Compare a baseline with an AI-enabled or more sophisticated alternative.
4. Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.

## Closing

Close by returning to the module artifact: **imaging model evidence packet with preprocessing notes, validation limits, and clinical handoff risks focused on classification and detection: Train or simulate an image classification baseline.**. Students should leave knowing exactly what artifact they are producing and how it will be judged.