Federated Intelligence in Medicine
AI-Driven Robotics for Secure and Intelligent Healthcare Systems
- 1st Edition - February 1, 2027
- Latest edition
- Editors: Manisha Guduri, Chinmay Chakraborty, Nabih Jaber, Keping Yu
- Language: English
Federated Intelligence in Medicine: AI-Driven Robotics for Secure and Intelligent Healthcare Systems explores the rapidly evolving field where medical robotics intersects with c… Read more
Description
Description
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.
Key features
Key features
- Explores the integration of Generative AI and Federated Learning for adaptive and secure medical robotic systems
- Provides practical case studies that demonstrate real-world applications in surgical and diagnostic robotics
- Addresses privacy preservation techniques, including homomorphic encryption and differential privacy for healthcare data security
- Covers emerging trends in AI-driven medical imaging and human-robot interaction technologies
- Bridges theoretical concepts with implementation strategies for researchers, professionals, and students in medical technology
Readership
Readership
Table of contents
Table of contents
2. Intelligent Robotics and Collaborative Systems: Integrating Artificial Intelligence, Medical Robotics, Federated Learning, and Industry 5.0
3. Parallel Adaptive Transformer Coupling Convolutional Neural Network Framework for Secure Communication and Data Integrity in Medical Robotics
4. An Intelligent Federated Learning Architecture for Remote Robotic Surgery Using Optimized Directed Acyclic Generalized Simplicial Graph Neural Network
5. Conceptual Foundations of Generative Artificial Intelligence and Federated Learning in Healthcare
6. Secure Collaborative AI in Healthcare Through Privacy-Preserving Federated Learning Across Medical Institutions
7. Federated Authentication Framework for Secure and Privacy-Preserving Medical IoT
8. A Novel Approach for Gastrointestinal Endoscopy Image Classification through Enhanced Multiview Feature Fusion and Light Weight Models
9. Enhanced Speaker Diarization and Gender Identification Using CNN-LSTM Hybrid Framework for Secure Clinical Communication in AI-Driven Healthcare Robotics
10. A deep learning-based Autonomous Control in Healthcare Robotic Applications for COVID-19 detection and assistance
11. Multiple-class Fine-Tuned SAM for Enhanced Bariatric Surgery Image Segmentation
12. Human Robot Interaction Using Federated Learning in Healthcare
13. A Federated Multimodal Learning Approach with Adaptive Aggregation for Privacy-Preserving Cardiac Diagnosis
14. FedBioTwin-Rehab: Biomechanics-Regularized Personalized Federated Digital Twins for PrivacyPreserving Wearable Rehabilitation in Knee Osteoarthritis and Freezing of Gait in Parkinson's Disease
15. Medical imaging robotics integration with federated learning for privacy-preserving healthcare systems
16. Robotic Agentic AI for Treatment Process Automation: Towards Patient-centered Healthcare Ecosystem
17. Federated Generative AI on Chest X-Ray Images: Privacy-Preserving Synthetic Data Generation and Clinical Decision Support
18. An Exploration of Generative AI and Federated Learning for Secure Early Diagnosis of Mild Cognitive Impairment using Neuroimaging
19. An Intelligent Breast Cancer Detection Framework Using Stereoscopic Scalable Kolmogorov-Arnold Quantum Convolutional Neural Network Optimized with Bobcat Algorithm for Healthcare Data Analytics
20. A Federated Agentic Artificial Intelligence Framework for Complex Decision-Making in Heart Disease Prediction
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2027
- Language: English
About the editors
About the editors
MG
Manisha Guduri
Dr. Manisha Guduri is currently a Full Time Instructor at the University of Louisiana at Lafayette, USA. She is the author/ coauthor of more than 71 research papers in reputed journals, book chapters, and international conferences. Her research interests include Artificial Intelligence, Biomedical Applications, VLSI/CAD design. She is currently working on VLSI and AI in the biomedical field. She published 5 patents out of which 2 are under FER. She received one patent grant. She is the reviewer of IEEE TVLSI, Microelectronics Journal, IET digital circuits, IEEE Journal of Biomedical and Health Informatics, etc. She has one on-going funded project from the Department of Science and Technology.
She is a senior member of IEEE, USA. She is also currently member of various IEEE Societies such as IEEE Young Professionals, IEEE Women in Engineering, Circuits and Systems, Computer Society, Sensor Council, etc. She is appointed as IEEE WiE CASS representative for 2023 & 2024. She is IEEE WiE DL program Coordinator and IEEE Computer Society Lafayette section Vice Chair for 2024. She has delivered more than 35 invited talk/tutorial speech/expert talk in various platforms like International Conference /technical programs. She has organized 10 international conferences under different roles.
CC
Chinmay Chakraborty
NJ
Nabih Jaber
Dr. Nabih Jaber is an Associate Professor and Chair of the Department of Electrical and Computer Engineering. His research interests include Intelligent Transportation Systems (ITS), wireless communications, coding and information theory, Dedicated Short Range Communications (DSRC) vehicular systems, smart grid power line communications, sensor networks, and Advanced Driver Assistance Systems/Autonomous Vehicles (ADAS/AV). He is also engaged in test-bed implementations and the design of innovative simulation systems.
Dr. Jaber actively contributes to the teaching community, having obtained the University Teaching Certification (UTC) from SEDA, which promotes innovation and best practices in higher education. He has also earned certificates in Leading Effective Discussions, Instructional Skills, Teaching Dossier, and Millennial Students: Myths and Realities.
He currently directs the Innovative Smart Wireless Networking Lab (ISWiNLab) and serves as the IEEE Student Branch Advisor at LTU. Additionally, he has previously served as Director of the Master of Science in Electrical and Computer Engineering (MSECE) Graduate Program.
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