Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications.The book will help readers acquire the basic notions of AI trustworthiness and understand its concrete application in medical imaging, identify pain points and solutions to enhance trustworthiness in medical imaging applications, understand current limitations and perspectives of trustworthy AI in medical imaging, and identify novel research directions.Although the problem of trustworthiness in AI is actively researched in different disciplines, the adoption and implementation of trustworthy AI principles in real-world scenarios is still at its infancy. This is particularly true in medical imaging where guidelines and standards for trustworthiness are critical for the successful deployment in clinical practice. After setting out the technical and clinical challenges of AI trustworthiness, the book gives a concise overview of the basic concepts before presenting state-of-the-art methods for solving these challenges.
- Introduces the key concepts of trustworthiness in AI.
- Presents state-of-the-art methodologies for trustworthy AI in medical imaging.
- Outlines major initiatives focusing on real-world deployment of trustworthy principles in medical imaging applications.
- Presents outstanding questions still to be solved and discusses future research directions.
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ISBN-13
978-0-443-23760-7 (9780443237607)
Schweitzer Klassifikation
PrefaceSection 1- Preliminaries
- Introduction to Trustworthy AI for Medical Imaging & Lecture Plan
- The fundamentals of AI ethics in Medical ImagingSection 2- Robustness
3. 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
