Loveleen Gaur, Ajith Abraham

Generative Artificial Intelligence and Ethics for Healthcare (eBook, ePUB)

• Format: ePub

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. - 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 225
• Erscheinungstermin: 11. September 2025
• Englisch
• ISBN-13: 9780443331251
• Artikelnr.: 75467335

Autorenporträt

Dr. Loveleen Gaur is an internationally renowned academic leader, educator, and researcher in the fields of Artificial Intelligence, Generative AI, and Data Analytics. She currently serves as the Director of the Symbiosis Artificial Intelligence Institute (SAII), Symbiosis International University, Pune, where she is spearheading interdisciplinary education and ethical innovation in AI. With over two decades of academic experience, Dr. Gaur has held senior faculty positions at premier institutions in India and abroad. She served as Professor at Amity University and continues to hold honorary adjunct professorships at Taylor's University (Malaysia) and the University of the South Pacific (Fiji). Her global academic engagements span curriculum design, PhD supervision, research leadership, and strategic international collaborations. In 2024, she was recognized among the World's Top 2% Scientists by Stanford University and Elsevier for her impactful scholarly contributions. She has authored and edited numerous research books with leading international publishers including Elsevier, Springer, Taylor & Francis, IGI Global, De Gruyter, and River Publishers, and has published extensively in high-impact journals indexed in SCI, Scopus, Web of Science, and ABDC. Her research spans domains such as AI in healthcare, neurodegenerative disease prediction, generative and explainable AI, sustainability, and business intelligence. Dr. Gaur is also Co-Editor of the SCOPUS-indexed Taylor & Francis journal Communications in Statistics - Case Studies, Data Analysis and Applications. She holds multiple editorial roles-Guest Editor, Review Editor, and Topic Editor-for top-tier journals by Springer, MDPI, Frontiers, and others. As a Senior Member of IEEE, she actively contributes to the AI community through thought leadership, international speaking engagements, and advocacy for ethical, inclusive AI practices. Her teaching portfolio includes undergraduate to doctoral courses on Generative AI, Deep Learning, Large Language Models (LLMs), Predictive Analytics, Business Intelligence, Power BI, and Explainable AI. Dr. Gaur is also certified in Agentic AI, AI for Business, and Generative AI Applications through globally recognized programs from IBM, Vanderbilt University, IIT Madras, and the University of Pennsylvania. At SAII, Dr. Gaur is dedicated to shaping the next generation of AI professionals who are not only technically adept but also ethically grounded-bridging AI with societal impact across domains like healthcare, agriculture, education, and industry.

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 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