# AINS6100: AI in Medical Imaging

**Aurnova MSAI track:** Healthcare AI  
**Credits:** 3  
**Format:** 8-week online graduate course

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

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in AI in Medical Imaging;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

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