Marco Lorenzi is a tenured research scientist at the Inria Center of University Côte d’Azur (France), and junior chair holder at the Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur. He is also a visiting Senior Lecturer at the School of Biomedical Engineering & Imaging Sciences at King’s College London. His research focuses on developing statistical learning methods to model heterogeneous and secured data in biomedical applications. He is the founder and scientific responsible for the open-source federated learning platform Fed-BioMed.Dr Zuluaga is an assistant professor in the Data Science department at EURECOM. She holds a junior chair at the 3IA Institute Côte d’Azur and is a visiting Senior Lecturer within the School of Biomedical Engineering & Imaging Sciences at King’s College London.Her current research focuses on the development of machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.

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• PrefaceSection 1- PreliminariesIntroduction to Trustworthy AI for Medical Imaging & Lecture PlanThe fundamentals of AI ethics in Medical ImagingSection 2– Robustness3. Machine Learning Robustness: A Primer4. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging5. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control6. Domain shift, Domain Adaptation and GeneralizationSection 3 - Validation, Transparency and Reproducibility7. Fundamentals on Transparency, Reproducibility and Validation8. Reproducibility in Medical Image Computing9. Collaborative Validation and Performance Assessment in Medical Imaging Applications10. Challenges as a Framework for Trustworthy AISection 4 – Bias and Fairness11. Bias and Fairness12. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging ApplicationsSection 5 - Explainability, Interpretability and Causality13. Fundamentals on Explainable and Interpretable Artificial Intelligence Models14. Causality: Fundamental Principles and Tools15. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations16. Explainable AI for Medical Image Analysis17. Causal Reasoning in Medical ImagingSection 6 - Privacy-preserving ML18. Fundamentals of Privacy-Preserving and Secure Machine Learning19. Differential Privacy in Medical Imaging ApplicationsSection 7 - Collaborative Learning20. Fundamentals on Collaborative Learning21. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses22. Promises and Open Challenges for Translating Federated learning in Hospital EnvironmentsSection 8 - Beyond the Technical Aspects23. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare

Marco Lorenzi is a tenured research scientist at the Inria Center of University Côte d’Azur (France), and junior chair holder at the Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur. He is also a visiting Senior Lecturer at the School of Biomedical Engineering & Imaging Sciences at King’s College London. His research focuses on developing statistical learning methods to model heterogeneous and secured data in biomedical applications. He is the founder and scientific responsible for the open-source federated learning platform Fed-BioMed.Dr Zuluaga is an assistant professor in the Data Science department at EURECOM. She holds a junior chair at the 3IA Institute Côte d’Azur and is a visiting Senior Lecturer within the School of Biomedical Engineering & Imaging Sciences at King’s College London.Her current research focuses on the development of machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.

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• PrefaceSection 1- PreliminariesIntroduction to Trustworthy AI for Medical Imaging & Lecture PlanThe fundamentals of AI ethics in Medical ImagingSection 2– Robustness3. Machine Learning Robustness: A Primer4. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging5. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control6. Domain shift, Domain Adaptation and GeneralizationSection 3 - Validation, Transparency and Reproducibility7. Fundamentals on Transparency, Reproducibility and Validation8. Reproducibility in Medical Image Computing9. Collaborative Validation and Performance Assessment in Medical Imaging Applications10. Challenges as a Framework for Trustworthy AISection 4 – Bias and Fairness11. Bias and Fairness12. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging ApplicationsSection 5 - Explainability, Interpretability and Causality13. Fundamentals on Explainable and Interpretable Artificial Intelligence Models14. Causality: Fundamental Principles and Tools15. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations16. Explainable AI for Medical Image Analysis17. Causal Reasoning in Medical ImagingSection 6 - Privacy-preserving ML18. Fundamentals of Privacy-Preserving and Secure Machine Learning19. Differential Privacy in Medical Imaging ApplicationsSection 7 - Collaborative Learning20. Fundamentals on Collaborative Learning21. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses22. Promises and Open Challenges for Translating Federated learning in Hospital EnvironmentsSection 8 - Beyond the Technical Aspects23. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare

Marco Lorenzi is a tenured research scientist at the Inria Center of University Côte d’Azur (France), and junior chair holder at the Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur. He is also a visiting Senior Lecturer at the School of Biomedical Engineering & Imaging Sciences at King’s College London. His research focuses on developing statistical learning methods to model heterogeneous and secured data in biomedical applications. He is the founder and scientific responsible for the open-source federated learning platform Fed-BioMed.Dr Zuluaga is an assistant professor in the Data Science department at EURECOM. She holds a junior chair at the 3IA Institute Côte d’Azur and is a visiting Senior Lecturer within the School of Biomedical Engineering & Imaging Sciences at King’s College London.Her current research focuses on the development of machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.

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• PrefaceSection 1- PreliminariesIntroduction to Trustworthy AI for Medical Imaging & Lecture PlanThe fundamentals of AI ethics in Medical ImagingSection 2– Robustness3. Machine Learning Robustness: A Primer4. Navigating the Unknown: Out-of-Distribution Detection for Medical Imaging5. From Out-of-Distribution Detection and Uncertainty Quantification to Quality Control6. Domain shift, Domain Adaptation and GeneralizationSection 3 - Validation, Transparency and Reproducibility7. Fundamentals on Transparency, Reproducibility and Validation8. Reproducibility in Medical Image Computing9. Collaborative Validation and Performance Assessment in Medical Imaging Applications10. Challenges as a Framework for Trustworthy AISection 4 – Bias and Fairness11. Bias and Fairness12. Open Challenges on Fairness of Artificial Intelligence in Medical Imaging ApplicationsSection 5 - Explainability, Interpretability and Causality13. Fundamentals on Explainable and Interpretable Artificial Intelligence Models14. Causality: Fundamental Principles and Tools15. Interpretable AI for Medical Image Analysis: Methods, Evaluation and Clinical Considerations16. Explainable AI for Medical Image Analysis17. Causal Reasoning in Medical ImagingSection 6 - Privacy-preserving ML18. Fundamentals of Privacy-Preserving and Secure Machine Learning19. Differential Privacy in Medical Imaging ApplicationsSection 7 - Collaborative Learning20. Fundamentals on Collaborative Learning21. Large-scale Collaborative Studies in Medical Imaging through Meta Analyses22. Promises and Open Challenges for Translating Federated learning in Hospital EnvironmentsSection 8 - Beyond the Technical Aspects23. Stakeholder Engagement: The Path to Trustworthy AI in Healthcare