Federated Intelligence in Medicine: AI-Driven Robotics for Secure and Intelligent Healthcare Systems explores the rapidly evolving field where medical robotics intersects with cutting-edge artificial intelligence and secure data-sharing technologies. As healthcare demands more precise, less invasive, and personalized treatment options, the integration of Generative AI and Federated Learning has become essential. This book addresses the critical need to understand how these technologies collectively transform medical robotics, enabling adaptive, secure, and efficient healthcare solutions that leverage decentralized patient data while safeguarding privacy. Covering a comprehensive range of topics, the book begins with foundational knowledge in medical robotics and Generative AI, progressing to advanced concepts such as federated learning and architectural modeling tailored for AI-driven robotic systems. Readers will explore algorithms enhancing surgical robotics, remote robotic surgeries, and human-robot interaction. The text also tackles AI-driven diagnostic robots, privacy preservation through homomorphic encryption and differential privacy, and emerging medical imaging techniques integrated with robotics. Later chapters examine the synergy of large language models, agent AI, and edge AI with robotic technologies, providing both theoretical frameworks and practical case studies that illuminate real-world applications and challenges. This book is an invaluable resource for researchers and academicians engaged in artificial intelligence, robotics, and medical technology.

It also serves undergraduate and graduate students in Biomedical Engineering, Electronics and Electrical Engineering, and Computer Science and Engineering. By bridging the gap between AI-driven decision-making, robotics implementation, and secure data-sharing, this book equips readers with the knowledge to pioneer advancements that will define the future of intelligent and secure healthcare systems.