[ToC] Course 4

ToC of Course 4/5: Evaluations of AI Applications in Healthcare #

Module 1: AI in Healthcare #

  1. Learning Objectives
  2. Common Definitions
  3. Overview
  4. Why AI is needed in Healthcare
  5. Examples of AI in Healthcare
  6. Growth of AI in Healthcare
  7. Questions Answered by AI
  8. AI Output
  9. Think beyond area under the curve

Module 2: Evaluations of AI in Healthcare #

  1. Learning Objectives
  2. Recap: Framework
  3. Stakeholders
  4. Clinical Utility
  5. Outcome: Action Pairing, An Overview
  6. Lead Time
  7. Type of Action
  8. OAP Examples
  9. Number Needed to Treat
  10. Net Benefits
  11. Decision Curves
  12. Feasibility overview
  13. Implementation Costs
  14. Clinical Evaluation and Uptake
  15. Summary

Module 3: AI Deployment #

  1. Learning Objectives
  2. The Problem
  3. Practical Questions Prior to Deployment
  4. Deployment Pathway
  5. Design and Development
  6. Stakeholder Involvement
  7. Data Type and Sources
  8. Settings
  9. In Silico Evaluation
  10. Net Utility & Work Capacity
  11. Statistical Validity
  12. Care Integration, Silent Mode
  13. Clinical Integration, Considerations
  14. Technical Integration
  15. Deployment Modalities
  16. Continuous Monitoring and Maintenance
  17. Challenges of Deployment
  18. Sepsis Example
  19. Summary

Module 4: Downstream Evaluations of AI in Healthcare: Bias and Fairness #

  1. Learning Objectives
  2. Real World Examples of AI Bias
  3. Introduction - Types of Bias
  4. Historical Bias
  5. Representation Bias
  6. Measurement Bias
  7. Aggregation Bias
  8. Evaluation Bias
  9. Deployment Bias
  10. What is algorithmic Fairness
  11. Anti-classification
  12. Parity Classification
  13. Calibration
  14. Applying Fairness Measures
  15. Lack of Transparency
  16. Minimal Reporting Standards
  17. Opportunities and Challenges
  18. Summary

Module 5: The Regulatory Environment for AI in Healthcare #

  1. Learning Objectives
  2. The Problem
  3. International Definitions Used for Regulatory Purposes
  4. Definition Statement & Risk Framework
  5. Valid Clinical Association
  6. Analytical Evaluation
  7. Clinical Evaluation
  8. General Control
  9. de novo Notifications
  10. Software Modification
  11. TPLC
  12. Locked vs Adapted AI solutions
  13. Examples
  14. Non-Regulated Products
  15. EU Regulations
  16. Chinese Guidelines
  17. OMB Guidelines
  18. Summary

Module 7: AI and Medicine (Optional Content) #

  1. Introduction: Navigating the Intersections of AI and Medicine
  2. Life Cycle of AI
  3. A Deep Dive into Historical and Societal Dimensions
  4. Race-Based Medicine and Race-Aware Approach
  5. Bias Mitigation Strategies
  6. Exploring Potentials and Ethical Quandaries
  7. Dismantling Race-Based Medicine
  8. Deploying AI into Healthcare Settings
  9. Conclusion