Syllabus: AINS6102 AI for Clinical Decision Support#
Course Identity#
Field |
Value |
|---|---|
Course |
AINS6102 AI for Clinical Decision Support |
Program |
Aurnova Master of Science in Artificial Intelligence |
Track |
Healthcare AI |
Credits |
3 graduate credits |
Format |
8-week online graduate course with weekly instructor interaction |
Primary environment |
Google Colab for first-run labs; GitHub Codespaces for repository-based or instructor-guided work |
Catalog Description#
Designs clinical decision support systems with guideline modeling, safety, human factors, validation, and monitoring.
Student Audience and Prerequisites#
Completion of the MSAI core foundations or permission of the instructor is recommended. No undergraduate computer science major is assumed; technical work remains scaffolded around interpretation, evidence, and professional judgment. The course is designed for graduate students preparing to lead, evaluate, manage, or apply AI work in professional settings. Students are not expected to be computer science majors. Technical work is scaffolded so students can run a notebook, observe output, change one controlled variable, and explain the evidence in professional language.
Course-Level Learning Outcomes#
By the end of this course, students will be able to:
CO1: Analyze professional problems in AI for Clinical Decision Support and formulate AI use cases with explicit stakeholders, decision boundaries, data assumptions, and success criteria.
CO2: Execute or interpret reproducible notebook-based investigations that demonstrate core AI for Clinical Decision Support methods using guided Python/Colab workflows.
CO3: Evaluate model, workflow, or governance evidence for accuracy, validity, uncertainty, bias, security, privacy, and operational limitations appropriate to Healthcare AI contexts.
CO4: Produce professional artifacts for AI for Clinical Decision Support, including briefs, model cards, risk registers, evaluation memos, implementation plans, or executive recommendations.
CO5: Apply responsible AI, academic integrity, data stewardship, accessibility, and human-oversight expectations to course work and proposed deployments.
CO6: Communicate AI findings, limitations, tradeoffs, and next-step recommendations to technical and nontechnical stakeholders using clear graduate-level evidence.
Credit-Hour and Contact-Hour Rationale#
This 3-credit accelerated graduate course expects approximately 135 total student learning hours across the 8-week term. The course design allocates approximately 45 hours to instructor-led lecture, narrated content, guided lab demonstration, discussion, feedback, and synchronous or asynchronous interaction. Approximately 90 additional hours are allocated to reading, notebook practice, applied assignments, revision, and the final portfolio artifact. Each module contains enough slide and narration material for a 90-minute class session, plus labs and assignments that extend learning beyond direct instruction.
Weekly Schedule and Major Deliverables#
Week |
Module Topic |
Essential Question |
Primary Deliverable |
|---|---|---|---|
1 |
Clinical decision support foundations |
What role should AI play in clinician decision-making? |
Module 1 Assignment: Clinical decision support foundations |
2 |
Clinical knowledge and guideline modeling |
How do guidelines become computable support? |
Module 2 Assignment: Clinical knowledge and guideline modeling |
3 |
Diagnostic assistance and triage |
How can AI support diagnosis without replacing clinical judgment? |
Module 3 Assignment: Diagnostic assistance and triage |
4 |
Treatment planning and personalization |
How do patient-specific factors shape recommendations? |
Module 4 Assignment: Treatment planning and personalization |
5 |
Human factors and alert fatigue |
How do interface choices affect clinical safety? |
Module 5 Assignment: Human factors and alert fatigue |
6 |
Validation and clinical safety cases |
What evidence is required for trustworthy support? |
Module 6 Assignment: Validation and clinical safety cases |
7 |
Regulation, liability, and monitoring |
Who is accountable when support systems fail? |
Module 7 Assignment: Regulation, liability, and monitoring |
8 |
Clinical decision support review |
What makes a CDS system ready for institutional review? |
Module 8 Assignment: Clinical decision support review |
Assessment Plan#
Component |
Weight |
Evidence |
|---|---|---|
Module applied assignments |
35% |
Eight short professional artifacts scored with module rubrics. |
Notebook labs and reflections |
20% |
Guided Colab/Codespaces labs with before-after interpretation and limitation statements. |
Mid-course synthesis brief |
15% |
Integrative memo or design artifact covering Modules 1-4. |
Final applied portfolio artifact |
20% |
Cumulative artifact demonstrating evidence, risk reasoning, and stakeholder communication. |
Participation and professional engagement |
10% |
Discussion, peer review, instructor check-ins, and revision responsiveness. |
Passing performance requires both cumulative course performance of 70% or higher and submission of the final applied portfolio artifact. Graduate-level performance requires accurate vocabulary, evidence-based reasoning, responsible limitation statements, and professional communication. Students may revise selected artifacts when the instructor determines that revision supports learning outcomes and academic integrity.
Grading Scale#
Grade |
Range |
Interpretation |
|---|---|---|
A |
93-100 |
Excellent graduate performance; evidence is rigorous, well communicated, and professionally defensible. |
A- |
90-92 |
Strong graduate performance with minor gaps in depth, precision, or integration. |
B+ |
87-89 |
Good performance; work is complete and evidence-based but not consistently advanced. |
B |
83-86 |
Satisfactory graduate performance; meets core expectations with some limitations. |
B- |
80-82 |
Marginal graduate performance; recurring gaps require attention. |
C |
70-79 |
Minimal passing performance; significant weaknesses in evidence, accuracy, or communication. |
F |
Below 70 |
Does not demonstrate required course outcomes. |
Required Readings, Standards, and Professional Resources#
The instructor may update editions, links, or equivalent institutional resources before the term begins. Readings combine scholarly foundations, professional standards, and applied guidance.
Before the course opens, the instructor or program designee verifies library access, link currency, accessible formats, and any approved substitutions. Populi identifies the authoritative assigned edition or link for the live offering; students should use that version rather than an older saved copy.
Module |
Topic |
Required Reading or Standard |
Applied Professional Resource |
|---|---|---|---|
1 |
Clinical decision support foundations |
FDA guidance on Clinical Decision Support Software. |
HL7 FHIR overview and implementation concepts. |
2 |
Clinical knowledge and guideline modeling |
HL7 FHIR overview and implementation concepts. |
AHRQ clinical decision support implementation resources. |
3 |
Diagnostic assistance and triage |
AHRQ clinical decision support implementation resources. |
Clinical prediction model reporting guidance such as TRIPOD-AI. |
4 |
Treatment planning and personalization |
Clinical prediction model reporting guidance such as TRIPOD-AI. |
Human factors and alert fatigue readings selected by instructor. |
5 |
Human factors and alert fatigue |
Human factors and alert fatigue readings selected by instructor. |
WHO ethics and governance of AI for health. |
6 |
Validation and clinical safety cases |
WHO ethics and governance of AI for health. |
FDA Good Machine Learning Practice principles. |
7 |
Regulation, liability, and monitoring |
FDA Good Machine Learning Practice principles. |
NIST AI RMF 1.0, monitoring and governance sections. |
8 |
Clinical decision support review |
NIST AI RMF 1.0, monitoring and governance sections. |
FDA guidance on Clinical Decision Support Software. |
Learning Technology and Beginner Support#
Students should start notebook labs in Google Colab unless the instructor specifies Codespaces. Colab lowers setup burden and supports students who are new to Python. Students should first run the notebook unchanged, then change one small value and explain what changed. Codespaces is used when an activity requires the full repository, multiple files, tests, or GitHub workflow evidence. The course assesses interpretation, evidence, limitations, and professional judgment; it does not assume that every student enters as a programmer.
Before launch, the instructor or program designee tests Colab and Codespaces access, equivalent activity paths, assistive-technology concerns, and the support escalation route. Populi provides the current accessibility, accommodation, and technical-support contacts for the live offering.
Academic Integrity and Permitted AI Assistance#
Students must submit their own analysis, decisions, explanations, and reflections. Use of generative AI tools is permitted for brainstorming, debugging, summarizing public documentation, and improving prose when allowed by the instructor, but students must disclose material AI assistance and remain responsible for factual accuracy, citations, code behavior, and final claims. Fabricated citations, hidden AI-generated submissions, unauthorized collaboration, or submission of work the student cannot explain may be treated as academic misconduct.
Accessibility and Accommodations#
Aurnova is expected to provide reasonable accommodations consistent with institutional policy and applicable law. Students who need accommodations should contact the designated institutional office and notify the instructor as early as possible. Course materials should be provided in accessible formats when feasible, including readable HTML pages, downloadable notebooks, text-based instructions, and alternatives for activities that require specific hardware or external accounts.
Data Privacy, Safety, and Responsible Use#
Unless explicitly approved by the instructor and institution, students must not upload confidential, regulated, proprietary, patient, student, financial, security-sensitive, or personally identifiable information into Colab, Codespaces, public repositories, or third-party AI tools. Labs use synthetic or public proxy data. Students must document data assumptions, privacy risks, and operational limits before recommending real-world use.
Late Work, Participation, and Instructor Interaction#
Students are expected to engage weekly with readings, lecture material, labs, and applied deliverables. Late work may receive reduced credit unless prior arrangements are approved or institutional policy applies. The instructor should provide regular substantive interaction through announcements, discussion, feedback, office hours or appointments, assignment feedback, and timely grading. Participation is evaluated by evidence of preparation, constructive discussion, peer feedback, and revision effort rather than by technical bravado.
Accreditation Evidence Location#
Aurnova maintains program-outcome mappings, assessment records, and continuous-improvement evidence separately from the public learner site. Those records are not learner coursework or a substitute for the syllabus, rubric, or Populi gradebook.