Loveleen Gaur, Ajith Abraham

Generative Artificial Intelligence and Ethics for Healthcare

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 225
• Erscheinungstermin: 1. September 2025
• Englisch
• ISBN-13: 9780443331244
• ISBN-10: 0443331243
• Artikelnr.: 71847233

Autorenporträt

Loveleen Gaur, PhD currently works as an adjunct professor with Taylor University, Malaysia & University of South Pacific, Fiji, and academic consultant with Australian School of Graduate Studies. Before moving to USA, she was a Professor with Amity University, India. She's supervised several PhD scholars, Post Graduate students, mainly in Artificial Intelligence and Data Analytics for business and healthcare. Under her guidance, the AI/Data Analytics research cluster has published extensively in high impact factor journals and has established extensive research collaboration globally with several renowned professionals. She's a senior IEEE member and Series Editor with CRC and Wiley. She has high indexed publications in SCI/ABDC/WoS/Scopus and several Patents/copyrights on her account, edited/authored many research books published by world-class publishers. She has specialized in the fields of Artificial Intelligence, Machine Learning, Pattern Recognition, Internet of Things, Data Analytics and Business Intelligence. She has been honored with prestigious National and International awards.

Inhaltsangabe

1. Generative AI in Healthcare: Introduction, Concept, Applications and
Challenges
2. Understanding Training Data and Mitigating Biases in Training Data
3. Calibrating Generative AI Models for Healthcare
4. Explainability in Generative AI and LLMs
5. Ethical considerations in Generative AI development and usage
6. Ethical concerns of Generative AI in Healthcare applications
7. Ethical concern of Data Privacy and Patient Data Ownership
8. Trust, Accountability, and Informed Consent: Cornerstones of Ethical
Practice in Clinical Medicine
9. Personalized Medicine and Data Privacy: Where to draw the boundary
10. Autonomous Medical Diagnosis: How to make balance between Accuracy and
Accountability
11. Health Equity and Generative AI: Role, Impact and Challenges
12. Lawfulness and Generative AI
13. Empathy and Generative AI: Role and Ethical challenges
14. Role of Governability and Generative AI for Healthcare