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.
1. Chapter 2. Introduction to explainable artificial intelligence in healthcare - p. 23 2. Chapter 3. A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer - p. 45 3. Chapter 4. Elucidable Artificial Intelligence in Medical Practice: Enhancing Transparency, Accountability, and Responsible Artificial Intelligence use - p. 69 4. Chapter 5. Explainable artificial intelligence for breast cancer diagnosis: a SHAP-driven approach - p. 91 5. Chapter 6. Explainable artificial intelligence techniques for image analysis - p. 111 6. Chapter 7. Explainable artificial intelligence in a diagnostic support system - p. 131 7. Chapter 8. Explainable artificial intelligence techniques for medical image analysis - p. 147 8. Chapter 9. Integration of multiple data modalities - p. 167 9. Chapter 10. Theoretical frameworks for explainable AI in medicine - p. 187 10. Chapter 11. Patient trust and acceptance of artificial intelligence in healthcare - p. 211 11. Chapter 12. Enhancing clinical transparency through exPlainable artificial intelligence in surface electromyograph–based lower limb activity recognition - p. 235 12. Chapter 13. Enhancing diagnostic accuracy: the role of explainable artificial intelligence in clinical decision support - p. 255 13. Chapter 14. Artificial intelligence bias and fairness in clinical applications - p. 271 14. Chapter 15. Explainable artificial intelligence for medicine prediction: interpretable deep learning and similarity models for therapeutic use analysis from chemical compositions - p. 301 15. Chapter 16. Early diagnosis of heart disease using machine learning and explainable artificial intelligence: a robust framework for improved healthcare outcomes - p. 327 16. Chapter 17. Risk prediction and preventive care using explainable artificial intelligence in clinical practice - p. 343 17. Chapter 18. Transparent artificial intelligence for treatment planning - p. 373 18. Chapter 19. A novel approach and solution based on natural language processing technique for diagnostic analysis and clinical documentation - p. 393 19. Chapter 20. Privacy and security challenges of explainable artificial intelligence in healthcare - p. 411 20. Chapter 21. Ethics of transparent artificial intelligence in health care - p. 427 21. Chapter 22. Emerging trends and technologies - p. 447 22. Chapter 23. The future of explainable AI in clinical decision-making: challenges and research directions - p. 477 23. Index - p. 493
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.
1. Chapter 2. Introduction to explainable artificial intelligence in healthcare - p. 23 2. Chapter 3. A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer - p. 45 3. Chapter 4. Elucidable Artificial Intelligence in Medical Practice: Enhancing Transparency, Accountability, and Responsible Artificial Intelligence use - p. 69 4. Chapter 5. Explainable artificial intelligence for breast cancer diagnosis: a SHAP-driven approach - p. 91 5. Chapter 6. Explainable artificial intelligence techniques for image analysis - p. 111 6. Chapter 7. Explainable artificial intelligence in a diagnostic support system - p. 131 7. Chapter 8. Explainable artificial intelligence techniques for medical image analysis - p. 147 8. Chapter 9. Integration of multiple data modalities - p. 167 9. Chapter 10. Theoretical frameworks for explainable AI in medicine - p. 187 10. Chapter 11. Patient trust and acceptance of artificial intelligence in healthcare - p. 211 11. Chapter 12. Enhancing clinical transparency through exPlainable artificial intelligence in surface electromyograph–based lower limb activity recognition - p. 235 12. Chapter 13. Enhancing diagnostic accuracy: the role of explainable artificial intelligence in clinical decision support - p. 255 13. Chapter 14. Artificial intelligence bias and fairness in clinical applications - p. 271 14. Chapter 15. Explainable artificial intelligence for medicine prediction: interpretable deep learning and similarity models for therapeutic use analysis from chemical compositions - p. 301 15. Chapter 16. Early diagnosis of heart disease using machine learning and explainable artificial intelligence: a robust framework for improved healthcare outcomes - p. 327 16. Chapter 17. Risk prediction and preventive care using explainable artificial intelligence in clinical practice - p. 343 17. Chapter 18. Transparent artificial intelligence for treatment planning - p. 373 18. Chapter 19. A novel approach and solution based on natural language processing technique for diagnostic analysis and clinical documentation - p. 393 19. Chapter 20. Privacy and security challenges of explainable artificial intelligence in healthcare - p. 411 20. Chapter 21. Ethics of transparent artificial intelligence in health care - p. 427 21. Chapter 22. Emerging trends and technologies - p. 447 22. Chapter 23. The future of explainable AI in clinical decision-making: challenges and research directions - p. 477 23. Index - p. 493
