Description
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.- Establishes a basic understanding of the concept of Generative AI, along with various ethical challenges- Focuses on specific issues such as Data Privacy, Patient Data Ownership, Trust, Accountability, and Informed Consent- Explores the latest concepts of Health Equity, Lawfulness, and Empathy in relation to Generative AI and the role of governability
Table of Contents
1. Generative AI in Healthcare: Introduction, Concept, Applications, and Challenges2. Understanding Training Data and Mitigating Biases in Training Data3. Calibrating Generative AI Models for Healthcare4. Explainability in Generative AI and LLMs5. Ethical Considerations in Generative AI Development and Usage6. Ethical Concerns of Generative AI in Healthcare Applications7. Ethical Concern of Data Privacy and Patient Data Ownership8. Trust, Accountability, and Informed Consent: Cornerstones of Ethical Practice in Clinical Medicine9. Personalized Medicine and Data Privacy: Where to Draw the Boundary?10. Autonomous Medical Diagnosis: How to Balance Accuracy and Accountability?11. Health Equity and Generative AI: Role, Impact, and Challenges12. Lawfulness and Generative AI13. Empathy and Generative AI: Role and Ethical Challenges14. Role of Governability and Generative AI for Healthcare
