Generative Artificial Intelligence and Ethics for Healthcare conducts a deep dive into the potential issues and challenges associated with Generative AI applications. The book begins with foundational concepts of generative AI and then explores ethical theories, including specific case studies in healthcare, and concludes with discussions on policy and future implications. Written for healthcare professionals, policymakers, academics and AI developers, by authors who have a thorough understanding of AI and machine learning in the healthcare.
1. Chapter 1 Generative AI in healthcare: Introduction, concept, applications, and challenges - p. 1 2. Chapter 2 Understanding training data and mitigating biases in training data - p. 25 3. Chapter 3 Calibrating generative AI models for healthcare - p. 39 4. Chapter 4 Explainability in generative AI and large language models - p. 57 5. Chapter 5 Ethical considerations in generative AI development and usage - p. 75 6. Chapter 6 Ethical concerns of generative AI in healthcare applications - p. 91 7. Chapter 7 Ethical concern of data privacy and patient data ownership - p. 107 8. Chapter 8 Trust, accountability, and informed consent: Cornerstones of ethical practice in clinical medicine - p. 131 9. Chapter 9 Personalized medicine and data privacy: Where to draw the boundary? - p. 155 10. Chapter 10 Autonomous medical diagnosis: How to balance accuracy and accountability? - p. 175 11. Chapter 11 Health equity and generative AI: Role, impact, and challenges - p. 197 12. Chapter 12 Lawfulness and generative AI - p. 211 13. Chapter 13 Empathy and generative AI: Role and ethical challenges - p. 227 14. Chapter 14 Role of governability and generative AI for healthcare - p. 243 15. Index - p. 261
Generative Artificial Intelligence and Ethics for Healthcare conducts a deep dive into the potential issues and challenges associated with Generative AI applications. The book begins with foundational concepts of generative AI and then explores ethical theories, including specific case studies in healthcare, and concludes with discussions on policy and future implications. Written for healthcare professionals, policymakers, academics and AI developers, by authors who have a thorough understanding of AI and machine learning in the healthcare.
1. Chapter 1 Generative AI in healthcare: Introduction, concept, applications, and challenges - p. 1 2. Chapter 2 Understanding training data and mitigating biases in training data - p. 25 3. Chapter 3 Calibrating generative AI models for healthcare - p. 39 4. Chapter 4 Explainability in generative AI and large language models - p. 57 5. Chapter 5 Ethical considerations in generative AI development and usage - p. 75 6. Chapter 6 Ethical concerns of generative AI in healthcare applications - p. 91 7. Chapter 7 Ethical concern of data privacy and patient data ownership - p. 107 8. Chapter 8 Trust, accountability, and informed consent: Cornerstones of ethical practice in clinical medicine - p. 131 9. Chapter 9 Personalized medicine and data privacy: Where to draw the boundary? - p. 155 10. Chapter 10 Autonomous medical diagnosis: How to balance accuracy and accountability? - p. 175 11. Chapter 11 Health equity and generative AI: Role, impact, and challenges - p. 197 12. Chapter 12 Lawfulness and generative AI - p. 211 13. Chapter 13 Empathy and generative AI: Role and ethical challenges - p. 227 14. Chapter 14 Role of governability and generative AI for healthcare - p. 243 15. Index - p. 261
