Generative Artificial Intelligence and Ethics for Healthcare

by Loveleen Gaur PhD, Ajith Abraham PhD

Overview

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.

Product Details

ISBN-13:	9780443331244
Publisher:	Elsevier Science
Publication date:	09/01/2025
Pages:	225
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

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.

Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.

Table of Contents

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 Balance 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

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From the Publisher

A single resource that fills a crucial gap in understanding the ethical dimensions of rapidly evolving AI technologies in healthcare