Preface Section 1- Preliminaries Introduction to Trustworthy AI for Medical Imaging & Lecture Plan The fundamentals of AI ethics in Medical Imaging Section 2- Robustness 3. Machine Learning Robustness: A Primer 4. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging 5. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control 6. Domain shift, Domain Adaptation and Generalization Section 3 - Validation, Transparency and Reproducibility 7. Fundamentals on Transparency, Reproducibility and Validation 8. Reproducibility in Medical Image Computing 9. Collaborative Validation and Performance Assessment in Medical Imaging Applications 10.

Challenges as a Framework for Trustworthy AI Section 4 - Bias and Fairness 11. Bias and Fairness 12. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications Section 5 - Explainability, Interpretability and Causality 13. Fundamentals on Explainable and Interpretable Artificial Intelligence Models 14. Causality: Fundamental Principles and Tools 15. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations 16. Explainable AI for Medical Image Analysis 17. Causal Reasoning in Medical Imaging Section 6 - Privacy-preserving ML 18.

Fundamentals of Privacy-Preserving and Secure Machine Learning 19. Differential Privacy in Medical Imaging Applications Section 7 - Collaborative Learning 20. Fundamentals on Collaborative Learning 21. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses 22. Promises and Open Challenges for Translating Federated learning in Hospital Environments Section 8 - Beyond the Technical Aspects 23. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare.