Appendix J — Course Syllabus Template

This syllabus template is designed for medical schools, residency programs, CME courses, and institutional training. Faculty may adapt the structure, readings, and assignments to their specific context and audience.

Using This Template
  • 12-week format: Standard graduate semester structure
  • 6-week intensive format: Combine weeks as indicated for accelerated programs
  • Modular use: Individual weeks can be extracted for focused workshops
  • Specialty tracks: Choose 2-3 specialty chapters relevant to your program

What this provides:

  • Complete 12-week graduate seminar syllabus using The Physician AI Handbook as primary text
  • Weekly structure: required readings, learning objectives, discussion questions
  • Assessment framework: evaluation critiques, implementation plans, policy briefs
  • 6-week intensive format for CME or accelerated programs
  • Adaptation notes for medical schools, residencies, informatics programs

Key pedagogical approach:

  • Start with failures (MYCIN, Epic sepsis, Watson) before successes
  • Emphasize critical evaluation over AI enthusiasm
  • Include ethics, bias, and liability throughout
  • Balance specialty depth with implementation practicality
  • End with policy engagement and career pathways

Assessment weights: Participation 15%, Evaluation critique 15%, Midterm 20%, Implementation plan 20%, Final brief 30%

Course Overview

Course Title: Clinical AI for Physicians: Evidence-Based Evaluation and Implementation

Level: Graduate seminar (MD, DO), residency elective, or CME program

Prerequisites: Medical degree or advanced clinical training. No prior AI knowledge required.

Primary Text: Tegomoh, B. (2025). The Physician AI Handbook: Peer-Reviewed Evidence for Every Specialty. DOI: 10.5281/zenodo.18251405

Learning Objectives:

By the end of this course, students will be able to:

  1. Explain core AI/ML concepts relevant to clinical practice
  2. Critically evaluate AI tools using peer-reviewed evidence
  3. Apply AI evaluation frameworks to specialty-specific applications
  4. Assess AI systems for bias, safety, and equity considerations
  5. Navigate regulatory and liability issues in clinical AI
  6. Design implementation strategies for AI tools in clinical workflow
  7. Communicate AI limitations and uncertainties to patients and colleagues

12-Week Syllabus

Week 1: History and Context of AI in Medicine

Theme: Why clinical AI keeps failing, and what we can learn from it

Required Reading:

Learning Objectives:

  • Trace AI development in medicine from MYCIN to modern LLMs
  • Explain why MYCIN achieved 90% accuracy but was never deployed
  • Identify patterns in clinical AI failures over 50 years

Discussion Questions:

  1. MYCIN outperformed infectious disease specialists in controlled studies. Why wasn’t it deployed?
  2. What does Google Flu Trends teach us about validation vs. real-world performance?
  3. What’s different about the current AI wave compared to previous cycles?

Week 2: AI Fundamentals for Clinicians

Theme: What physicians actually need to understand about AI

Required Reading:

Learning Objectives:

  • Distinguish supervised, unsupervised, and reinforcement learning
  • Explain why clinical AI faces unique data quality challenges
  • Assess the generalizability limitations of AI trained on specific populations

Discussion Questions:

  1. Why do AI systems trained at one hospital often fail at another?
  2. What clinical data quality issues are most likely to cause AI failures?
  3. How should clinicians interpret vendor accuracy claims?

Week 3: Evaluating AI Systems

Theme: How to assess clinical AI before adoption

Required Reading:

Learning Objectives:

  • Apply TRIPOD and PROBAST frameworks to AI evaluation
  • Identify red flags in vendor validation studies
  • Distinguish internal validation from external validation from prospective deployment

Discussion Questions:

  1. A vendor reports 95% sensitivity in their validation study. What questions should you ask?
  2. How do you evaluate AI when prospective randomized trials don’t exist?
  3. What’s the difference between a model working and a model helping patients?

Assignment: Evaluation critique (1,500 words): Critically evaluate a published clinical AI validation study

Week 4: Ethics, Bias, and Health Equity

Theme: How AI can harm patients and widen disparities

Required Reading:

Learning Objectives:

  • Analyze the Epic sepsis model controversy and its equity implications
  • Identify sources of algorithmic bias in clinical AI
  • Apply ethical frameworks to AI deployment decisions

Discussion Questions:

  1. The Epic sepsis model had lower sensitivity in Black patients. How should health systems respond?
  2. Is it ethical to deploy AI with known demographic performance gaps if it still improves average outcomes?
  3. How should informed consent work for AI-assisted clinical decisions?

Week 5: Privacy, Safety, and Liability

Theme: Legal and regulatory landscape for clinical AI

Required Reading:

Learning Objectives:

  • Explain FDA regulatory pathways for clinical AI (510(k), De Novo, PMA)
  • Analyze liability allocation when AI recommendations cause harm
  • Identify HIPAA implications of cloud-based AI tools

Discussion Questions:

  1. Who is liable when a physician follows an AI recommendation that harms a patient?
  2. How should physicians document AI-assisted decisions?
  3. What’s the regulatory status of ambient AI scribes? What are the risks?

Week 6: Specialty Deep Dive I (Choose Your Track)

Theme: AI applications in specific clinical domains

Faculty Note: Select 1-2 specialty chapters relevant to your program. Radiology is recommended as it has the most mature evidence base.

Suggested Options:

Learning Objectives:

  • Evaluate specialty-specific AI tools against peer-reviewed evidence
  • Identify which AI applications have strong evidence vs. marketing hype
  • Apply general evaluation frameworks to specialty-specific contexts

Discussion Questions:

  1. Which AI applications in your specialty have the strongest evidence? Which are overhyped?
  2. How would AI change clinical workflow in your specialty?
  3. What’s the biggest risk of AI in your specialty?

Midterm: Take-home exam covering Weeks 1-6

Week 7: Specialty Deep Dive II

Theme: Specialties with more complex AI applications

Suggested Options:

Learning Objectives:

  • Analyze AI applications in time-sensitive or high-stakes settings
  • Evaluate the tension between AI speed and clinical judgment
  • Assess AI tools for conditions with subjective diagnostic criteria

Discussion Questions:

  1. Should AI triage recommendations override clinical intuition in the ED?
  2. How should predictive models handle life-or-death decisions in critical care?
  3. Can AI assess mental health without understanding human experience?

Week 8: LLMs in Clinical Practice

Theme: Large language models, chatbots, and documentation AI

Required Reading:

Learning Objectives:

  • Explain how LLMs work and their fundamental limitations
  • Evaluate LLM applications for clinical documentation
  • Identify hallucination risks in medical AI chatbots

Discussion Questions:

  1. When should physicians trust LLM outputs? When should they verify?
  2. How should patients be informed about AI involvement in documentation?
  3. What happens when an LLM hallucinates clinical information?

Week 9: Workflow Integration

Theme: Why technically good AI often fails in practice

Required Reading:

Learning Objectives:

  • Apply human factors principles to AI implementation
  • Identify workflow disruptions that cause AI abandonment
  • Design implementation strategies that increase adoption

Discussion Questions:

  1. Why do clinicians disable AI alerts? How can implementation improve this?
  2. What’s the difference between a useful AI tool and a usable one?
  3. How should AI fit into existing EHR workflows?

Assignment: Implementation plan (2,000 words): Design an implementation strategy for an AI tool in your clinical setting

Week 10: AI Vendor Evaluation

Theme: Assessing vendor claims and negotiating contracts

Required Reading:

Learning Objectives:

  • Apply vendor evaluation frameworks to real AI products
  • Identify red flags in vendor marketing and contract terms
  • Negotiate meaningful validation requirements

Discussion Questions:

  1. A vendor shows impressive demo results. What validation should you require?
  2. How should data use, liability, and performance guarantees be structured in contracts?
  3. What should happen when AI performance degrades after deployment?

Week 11: The Future of Clinical AI

Theme: Emerging technologies and implications

Required Reading:

Learning Objectives:

  • Evaluate emerging AI technologies (multimodal AI, autonomous agents)
  • Analyze how AI might change physician roles over the next decade
  • Assess implications for medical education and training

Discussion Questions:

  1. Will AI make physicians more or less essential? In what ways?
  2. How should medical education change to prepare for AI-augmented practice?
  3. What clinical tasks should never be delegated to AI?

Week 12: Policy, Governance, and Career Implications

Theme: Shaping AI policy and professional development

Required Reading:

Learning Objectives:

  • Analyze current and proposed AI regulations affecting clinical practice
  • Identify career pathways in clinical AI
  • Evaluate global health implications of AI deployment decisions

Discussion Questions:

  1. What AI governance principles should guide clinical deployment?
  2. How can physicians influence AI policy and development?
  3. What responsibilities do wealthy health systems have regarding AI equity?

Final Assignment: Policy brief or implementation proposal (3,000 words)

Assessment Structure

Component Weight Due
Class participation 15% Ongoing
Evaluation critique (Week 3) 15% Week 3
Midterm exam 20% Week 6
Implementation plan (Week 9) 20% Week 9
Final policy brief/proposal 30% Week 12

6-Week Intensive Format

For CME programs or accelerated courses, combine weeks as follows:

Intensive Week Standard Weeks Focus
1 1-2 History, fundamentals, data challenges
2 3-4 Evaluation frameworks, ethics, bias
3 5-6 Safety, liability, specialty deep dive I
4 7-8 Specialty deep dive II, LLMs, documentation
5 9-10 Workflow integration, vendor evaluation
6 11-12 Future technologies, policy, careers

Supplementary Reading Lists

AI Evaluation Frameworks

AI Bias and Equity

FDA Regulatory Framework

Clinical AI Implementation

Adaptation Notes for Instructors

For medical schools (MS3-MS4 elective): Emphasize evaluation frameworks (Week 3), ethics (Week 4), and practical LLM use (Week 8). Reduce policy content.

For residency programs: Focus on specialty-relevant chapters. Add hands-on workshops with real AI tools used at your institution.

For CME/professional development: Compress to 6-week format. Emphasize practical evaluation skills and implementation strategies over theoretical content.

For informatics programs: Expand technical content in Weeks 2, 8, and 11. Add technical evaluation exercises.

License

This syllabus template is released under the same CC BY 4.0 license as The Physician AI Handbook. Faculty may adapt freely with attribution.