Table of Contents:
  • Preface
  • Section 1. Foundations of Hallucination in AI Systems
  • Chapter 1. Hallucination in AI Systems: Understanding and Framework
  • Chapter 2. Conceptual and Philosophical Perspectives
  • Section 2. From Fluency to Fidelity: A Conceptual Framework and Toolkit for Evaluating and Detecting Hallucinations in Generative AI
  • Chapter 3. Trust in AI Systems
  • Chapter 4. The Phenomenology of Trust: An Existential Prerequisite for Hallucination-Aware AI
  • Chapter 5. Towards Truthful and Responsible AI Systems
  • Section 3. Detection, Mitigation, and Model Design
  • Chapter 6. Human -in-the-Loop Frameworks for AI Mitigation and Accountability
  • Chapter 7. Method Distinction for Hallucination Detection
  • Section 4. Domain-Specific Applications in Healthcare
  • Chapter 8. Fact-Filtering Frameworks: Integrating Verification Pipelines for Hallucination-Resistant Large Language Models
  • Chapter 9. Generative Artificial Intelligence in Healthcare
  • Chapter 10. Integrated Healthcare Management With AI and Real-Time Optimization Multi-Criteria Decision Analysis
  • Chapter 11. Beyond Automation: A Framework for Augmenting Clinical Expertise With Generative AI
  • Section 5. Sectoral Impact and Research Trends
  • Chapter 12. Mitigating Hallucinations in AI: Ensuring Trustworthy and Aligned Systems in Legal, Educational, and Governmental Sectors
  • Compilation of References
  • About the Contributors
  • Index.