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.