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Explainable AI in Clinical Practice

Methods, Applications, and Implementation

Paperback Engels 2026 9780443441110
€ 196,59
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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.

Specificaties

ISBN13:9780443441110
Taal:Engels
Bindwijze:Paperback

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Inhoudsopgave

Section I: Foundations<br>1. Foundations of AI in Healthcare<br>2. Introduction to XAI in Healthcare<br>3. Understanding the Need for Transparency in Clinical AI<br>4. Theoretical Frameworks for XAI in Medicine<br>5. AI Bias and Fairness in Clinical Applications<br>6. Evaluation Frameworks for Healthcare XAI<br><br>Section II: Methods and Technologies<br>7. XAI Techniques for Medical Image Analysis<br>8. Natural Language Processing in Clinical Documentation<br>9. Time Series Analysis for Patient Monitoring<br>10. Integration of Multiple Data Modalities<br><br>Section III: Clinical Applications<br>11. XAI in Diagnostic Support Systems<br>12. Transparent AI for Treatment Planning<br>13. Risk Prediction and Preventive Care<br>14. Drug Discovery and Development<br>15. Performance Metrics and Quality Assurance<br>16. Integration with Clinical Workflows<br><br>Section IV: Ethical and Regulatory Considerations<br>17. Ethics of Transparent AI in Healthcare<br>18. Privacy and Security Considerations<br>19. Regulatory Compliance and Standards<br>20. Patient Trust and Acceptance<br><br>Section V: Future Directions<br>21. Emerging Trends and Technologies<br>22. Challenges and Opportunities<br>23. Future Research Directions

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        Explainable AI in Clinical Practice