# AINS6102: AI for Clinical Decision Support

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

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

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