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