Description
Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare.- Provides a comprehensive framework for implementing explainable AI in healthcare, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound- Includes real-world case studies that illustrate practical applications of explainable AI- Offers targeted solutions for diverse stakeholders in the healthcare AI ecosystem
Table of Contents
Section I: Foundations1. Foundations of AI in Healthcare2. Introduction to XAI in Healthcare3. Understanding the Need for Transparency in Clinical AI4. Theoretical Frameworks for XAI in Medicine5. AI Bias and Fairness in Clinical Applications6. Evaluation Frameworks for Healthcare XAISection II: Methods and Technologies7. XAI Techniques for Medical Image Analysis8. Natural Language Processing in Clinical Documentation9. Time Series Analysis for Patient Monitoring10. Integration of Multiple Data ModalitiesSection III: Clinical Applications11. XAI in Diagnostic Support Systems12. Transparent AI for Treatment Planning13. Risk Prediction and Preventive Care14. Drug Discovery and Development15. Performance Metrics and Quality Assurance16. Integration with Clinical WorkflowsSection IV: Ethical and Regulatory Considerations17. Ethics of Transparent AI in Healthcare18. Privacy and Security Considerations19. Regulatory Compliance and Standards20. Patient Trust and AcceptanceSection V: Future Directions21. Emerging Trends and Technologies22. Challenges and Opportunities23. Future Research Directions
