Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine.
Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Chapter 1. Foundations of AI in healthcare - p. 1 8. Chapter 2. Sustainable health record system using artificial intelligence with blockchain technology: Sustainable health record system using artificial intelligence with blockchain technology - p. 17 9. Chapter 3. Unleashing the hidden potential of metaverse in healthcare: A bibliometric analysis and future research agenda - p. 37 10. Chapter 4. Interpretability in clinical text sentiment analysis: A comparative study of LIME, SHAP, and Grad‑CAM for large language models - p. 59 11. Chapter 5. Enhancing clinical documentation through explainable AI-driven natural language processing (NLP): improving transparency, accuracy, and compliance in medical record-keeping - p. 77 12. Chapter 6. AI/ML-based surgery illustration of smart AI-driven treatment planning: transparency and discovering new innovations in the modern medical field - p. 89 13. Chapter 7. An integrated framework for dengue fever prediction using CNN with SHAP - p. 105 14. Chapter 8. Atrial fibrillation (AF) classification using rectangular pulse and cascade hybrid multilayer perceptron (CHMLP) neural network - p. 119 15. Chapter 9. Cascade hybrid multilayer perceptron (CHMLP) neural network for ECG signal pattern recognition applications - p. 133 16. Chapter 10. Explainable AI-based federated learning for uncovering and reducing bias in medical images - p. 151 17. Chapter 11. Emerging trends and technologies in explainable AI for clinical practice - p. 171 18. Chapter 12. Artificial intelligence in digital therapeutic systems: Personalization in mental health care and patient monitoring - p. 187 19. Chapter 13. Explainable AI for malaria classification: Enhancing transparency and trust in clinical diagnostics - p. 207 20. Chapter 14. Transparency in AI-driven healthcare: The role of XAI in enhancing fairness and mitigating bias in clinical practice - p. 219 21. Chapter 15. The transparent heart: XAI in cardiology - p. 231 22. Chapter 16. IoT enabled smart healthcare for multiple sclerosis: trends, challenges, and future directions - p. 241 23. Chapter 17. Self-guided medication system using hybrid model of graph neural networks with LIME - p. 259 24. Chapter 18. AI bias and fairness in clinical applications - p. 273 25. Chapter 19. Emerging trends in artificial intelligence in drug design and development: Revolutionizing clinical practices - p. 289 26. Chapter 20. Emerging trends and technologies in explainable AI (XAI) for clinical practice - p. 307 27. Chapter 21. Ethics of transparent AI in physiotherapy - p. 323 28. Chapter 22. Future research opportunities toward using XAI in healthcare - p. 335 29. Index - p. 355
Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine.
Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Chapter 1. Foundations of AI in healthcare - p. 1 8. Chapter 2. Sustainable health record system using artificial intelligence with blockchain technology: Sustainable health record system using artificial intelligence with blockchain technology - p. 17 9. Chapter 3. Unleashing the hidden potential of metaverse in healthcare: A bibliometric analysis and future research agenda - p. 37 10. Chapter 4. Interpretability in clinical text sentiment analysis: A comparative study of LIME, SHAP, and Grad‑CAM for large language models - p. 59 11. Chapter 5. Enhancing clinical documentation through explainable AI-driven natural language processing (NLP): improving transparency, accuracy, and compliance in medical record-keeping - p. 77 12. Chapter 6. AI/ML-based surgery illustration of smart AI-driven treatment planning: transparency and discovering new innovations in the modern medical field - p. 89 13. Chapter 7. An integrated framework for dengue fever prediction using CNN with SHAP - p. 105 14. Chapter 8. Atrial fibrillation (AF) classification using rectangular pulse and cascade hybrid multilayer perceptron (CHMLP) neural network - p. 119 15. Chapter 9. Cascade hybrid multilayer perceptron (CHMLP) neural network for ECG signal pattern recognition applications - p. 133 16. Chapter 10. Explainable AI-based federated learning for uncovering and reducing bias in medical images - p. 151 17. Chapter 11. Emerging trends and technologies in explainable AI for clinical practice - p. 171 18. Chapter 12. Artificial intelligence in digital therapeutic systems: Personalization in mental health care and patient monitoring - p. 187 19. Chapter 13. Explainable AI for malaria classification: Enhancing transparency and trust in clinical diagnostics - p. 207 20. Chapter 14. Transparency in AI-driven healthcare: The role of XAI in enhancing fairness and mitigating bias in clinical practice - p. 219 21. Chapter 15. The transparent heart: XAI in cardiology - p. 231 22. Chapter 16. IoT enabled smart healthcare for multiple sclerosis: trends, challenges, and future directions - p. 241 23. Chapter 17. Self-guided medication system using hybrid model of graph neural networks with LIME - p. 259 24. Chapter 18. AI bias and fairness in clinical applications - p. 273 25. Chapter 19. Emerging trends in artificial intelligence in drug design and development: Revolutionizing clinical practices - p. 289 26. Chapter 20. Emerging trends and technologies in explainable AI (XAI) for clinical practice - p. 307 27. Chapter 21. Ethics of transparent AI in physiotherapy - p. 323 28. Chapter 22. Future research opportunities toward using XAI in healthcare - p. 335 29. Index - p. 355
