Authors:

Loveleen Gaur, Ajith Abraham

• ISBN: 9780443331244
• Published: September 12, 2025
• Format: Paperback
• Language: English
• Publisher: Academic Press (Elsevier)

Description:

Generative Artificial Intelligence and Ethics for Healthcare explores how generative AI is transforming clinical practice, research, and health systems, and what it means to use these technologies responsibly. The book starts with core concepts and architectures of generative AI and large language models, then examines ethical theories, case studies in healthcare, and the policy and governance questions that follow from real-world deployment. It closes with forward-looking chapters on lawfulness, empathy, health equity, and governability in AI-enabled healthcare ecosystems.

Written by leading experts in AI and machine learning, the book is designed to help healthcare professionals, policymakers, academics, and AI developers understand both the opportunities and the risks of generative AI in health, from bias and data privacy to transparency, accountability, and patient trust.

Key Features:

• Provides a clear, accessible introduction to generative AI concepts, architectures, and applications in healthcare.
• Examines ethical challenges including bias in training data, explainability, trust, accountability, and informed consent. :contentReference[oaicite:1]{index=1}
• Covers critical topics such as data privacy, patient data ownership, health equity, and lawfulness in AI deployment.
• Includes dedicated chapters on autonomous diagnosis, personalized medicine, empathy in AI systems, and the role of governability.
• Connects ethical analysis with policy debates and regulatory trends, helping readers anticipate emerging governance frameworks.
• Offers practical insights for clinicians, AI teams, and institutions that are evaluating or implementing generative AI tools.

Readership:

Healthcare professionals; policymakers and regulators; AI developers and engineers; academics and researchers; students and educators interested in AI ethics, digital health, and health policy. 

About the Authors:

Loveleen Gaur is Professor and Director at the Symbiosis Artificial Intelligence Institute (SAII), Symbiosis International University, Pune. An internationally recognised researcher in generative and explainable AI, AI in healthcare, and data analytics, she has authored and edited numerous books and high-impact journal articles and was listed among the world’s top 2% scientists by Stanford University and Elsevier. 

Ajith Abraham is Vice-Chancellor at Sai University, Chennai, and former founding director of Machine Intelligence Research Labs (MIR Labs). With 35+ years of multidisciplinary research, more than 1,500 publications, and over 60,000 citations, he is consistently ranked among the most cited AI scientists worldwide and has led major research projects across the US, EU, and Asia. 

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

Why buy this book?

• It offers a single, structured reference that connects technical aspects of generative AI with concrete ethical and legal questions in healthcare.
• It helps hospitals, regulators, and AI teams anticipate risks—bias, privacy breaches, opaque decisions—and design responsible governance frameworks.
• It supports clinicians and health leaders who must evaluate AI tools, align them with professional standards, and maintain patient trust.
• It is ideal for teaching and training, providing a complete overview of concepts, case studies, and policy discussions around AI ethics in health.

Keywords:

Generative AI, Healthcare ethics, Artificial intelligence in health, Data privacy, Patient data ownership, Health equity, Explainability, Accountability, Lawfulness, Empathy, AI governance, Trustworthy AI

Target Audience:

Clinicians, Hospital and health system leaders, Policy makers, Health regulators, AI developers, Data scientists, Bioethicists, Health informatics specialists, Academic researchers, Graduate students in AI and health

Genre:

Healthcare, Artificial intelligence, Ethics, Medical ethics and professional conduct, Health policy, Technology and society

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