Syllabus: AINS6102 AI for Clinical Decision Support

Catalog Description

Designs clinical decision support systems with guideline modeling, safety, human factors, validation, and monitoring.

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 decision support foundations	What role should AI play in clinician decision-making?	Lab notebook + assignment brief
2	Clinical knowledge and guideline modeling	How do guidelines become computable support?	Lab notebook + assignment brief
3	Diagnostic assistance and triage	How can AI support diagnosis without replacing clinical judgment?	Lab notebook + assignment brief
4	Treatment planning and personalization	How do patient-specific factors shape recommendations?	Lab notebook + assignment brief
5	Human factors and alert fatigue	How do interface choices affect clinical safety?	Lab notebook + assignment brief
6	Validation and clinical safety cases	What evidence is required for trustworthy support?	Lab notebook + assignment brief
7	Regulation, liability, and monitoring	Who is accountable when support systems fail?	Lab notebook + assignment brief
8	Clinical decision support review	What makes a CDS system ready for institutional review?	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.