• 

  1: Foundations of AI and Data Literacy

  1.1: Understand AI and machine learning basics

  1.2: Know data sources and limitations

  1.3: Assess clinical AI tools

• 

  2: Regulation and Quality Oversight

  2.1: Know FDA AI regulations

  2.2: Understand model validation

  2.3: Monitor AI performance

• 

  3: Bias, Equity and Ethical Use

  3.1: Recognize bias in AI systems

  3.2: Promote fairness & equity

  3.3: Evaluate ethical concerns

• 

  4: Privacy, Security and Professionalism

  4.1: Apply HIPAA & data safety

  4.2: Use AI responsibly

  4.3: Uphold professional standards

• 

  5: Clinical Application
  and Decision Support

  5.1: Integrates AI with clinical reasoning

  5.2: Identify proper use cases

  5.3: Maintain clinical judgment

• 

  6: Communicate AI - Patient-Centered Use

  6.1: Engage patients in open discussion when AI informs care decisions

  6.2: Address patient questions and concerns

  6.3: Document AI use appropriately

• 

  7: Human-AI Teaming and Judgment

  7.1: Interpret AI results wisely

  7.2: Avoid automation bias

  7.3: Decide when to override

  7.4: Critically review and validate AI-generated documentation

• 

  8: Lifelong Learning and Adaptability

  8.1: Keep up with AI advances

  8.2: Read AI research

  8.3: Adapt to new practices

---

Advanced Skills (For Interested Students)

• Al research projects
• Data curation and metrics
• Model evaluation skills

Approved March 2026