# Syllabus: AINS6100 AI in Medical Imaging

## Catalog Description

Applies AI to clinical imaging workflows, labels, preprocessing, classification, segmentation, validation, and regulation.

## Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

## Weekly Schedule

| Week | Topic | Essential Question | Deliverable |
|------|-------|--------------------|-------------|
| 1 | Clinical imaging workflows | Where can AI help without disrupting clinical accountability? | Lab notebook + assignment brief |
| 2 | Image data, labels, and annotation | How do labeling practices shape medical model validity? | Lab notebook + assignment brief |
| 3 | Preprocessing and augmentation | Which transformations preserve clinical meaning? | Lab notebook + assignment brief |
| 4 | Classification and detection | How do imaging models identify findings? | Lab notebook + assignment brief |
| 5 | Segmentation and measurement | How do models support localization and quantification? | Lab notebook + assignment brief |
| 6 | Validation, bias, and safety | What evidence is needed before clinical use? | Lab notebook + assignment brief |
| 7 | Regulatory and operational integration | How does an imaging AI enter practice responsibly? | Lab notebook + assignment brief |
| 8 | Medical imaging AI case review | What would make the system clinically credible? | Lab notebook + assignment brief |

## Assessment

| Component | Weight |
|-----------|--------|
| Weekly labs and notebooks | 30% |
| Applied assignments | 35% |
| Participation and technical critique | 15% |
| Final synthesis portfolio | 20% |

## Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.