Dr. Loveleen Gaur is an internationally recognized academic leader, educator, and researcher in Artificial Intelligence, Generative AI, and Data Analytics. She serves as Director of the Symbiosis Artificial Intelligence Institute (SAII), Symbiosis International University, Pune, where she leads interdisciplinary education and ethical AI innovation.With over two decades of experience, Dr. Gaur has held senior academic roles at institutions in India and abroad, including Amity University, and holds adjunct professorships at Taylor’s University (Malaysia) and the University of the South Pacific (Fiji). Her work spans curriculum design, doctoral supervision, research leadership, and global collaborations.Named among the World’s Top 2% Scientists (Stanford/Elsevier, 2024), she has authored and edited numerous books and published extensively in high-impact journals. Her research focuses on AI in healthcare, explainable AI, sustainability, and business intelligence. A Senior IEEE member and journal editor, she is committed to advancing ethical, socially impactful AI.Dr. Ajith Abraham is the Pro Vice-Chancellor for Academics, Research, Incubation, and International Relations at Bennette University. He is also the Founding Director of Machine Intelligence Research Labs (MIR Labs), a global non-profit scientific network that connects academia and industry to support research and innovation. He is also serving as Vice Chancellor of Sai University, Chennai. His research interests include artificial intelligence and machine intelligence, cyber-physical systems, the Internet of Things (IoT), network security, Web intelligence, sensor networks, and data mining. He serves as Chair of the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing and has held editorial leadership roles, including Editor-in-Chief of Engineering Applications of Artificial Intelligence. Dr. Abraham earned his Ph.D. in Computer Science from Monash University, Australia.

• 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