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Elsevier

AI-Driven Diagnostics for 6G-Enabled Smart Healthcare

  • 1st Edition - October 1, 2026
  • Latest edition
  • Editors: Sangeeta Kumari, Ashish Khanna, Bal S. Virdee, Samiya Khan
  • Language: English

AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the health… Read more

Description

AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the healthcare sector. The book begins by highlighting the evolution of healthcare technology and the critical role of AI-driven diagnostics, emphasizing how 6G facilitates real-time, ultra-reliable communication. Key features of 6G, such as ultra-low latency and massive connectivity are discussed, showcasing their impact on advanced healthcare applications like remote diagnostics and patient monitoring. In addition, the integration of AI in medical diagnostics is examined, focusing on machine learning and deep learning techniques that enhance disease detection through medical imaging and clinical data analysis.

Users will also find content that explores the benefits of remote patient monitoring, particularly for underserved populations, and delves into edge AI for localized, low-latency diagnostic processing. Other sections cover real-time imaging diagnostics are highlighted and address predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Final sections cover ethical considerations and regulatory challenges.

Key features

  • Combines artificial intelligence with next-generation 6G networks, addressing critical advancements in healthcare diagnostics
  • Emphasizes the capabilities of 6G in enabling ultra-low latency and high-speed data transfer, facilitating real-time decision-making in clinical settings
  • Addresses the ethical considerations and regulatory frameworks necessary for implementing AI diagnostics in a 6G-enabled environment, ensuring patient privacy and compliance
  • Includes real-world case studies that demonstrate the successful application of AI diagnostics over 6G networks, providing actionable insights and lessons learned for practitioners

Readership

Biomedical Engineers seeking to integrate advanced AI techniques into imaging technologies and medical devices. Computer Scientists interested in applying machine learning and deep learning algorithms to enhance diagnostic accuracy and real-time data processing in healthcare

Table of contents

1. Introduction to AI in Healthcare and 6G Networks

2. Fundamentals of 6G Networks for Healthcare

3. AI-Driven Medical Diagnostics

4. 6G-Enabled Remote Patient Monitoring

5. Edge AI and Distributed Diagnostics

6. Real-Time Imaging Diagnostics with 6G

7. AI Models for Disease Prediction

8. Integrating IoT and Wearable Devices with 6G

9. Smart Hospitals Powered by 6G and AI

10. Blockchain for Secure 6G-Driven Diagnostics

11. Ethical and Regulatory Challenges in 6G Healthcare

12. AI and 6G in Personalized Medicine

13. Robotics and 6G-Enabled Surgical Systems

14. Case Studies of 6G-AI Diagnostics in Practice

15. Future Trends and Emerging Technologies

Product details

  • Edition: 1
  • Latest edition
  • Published: October 1, 2026
  • Language: English

About the editors

SK

Sangeeta Kumari

Dr. Sangeeta Kumari is an Assistant Professor at Bennett University, holding a Ph.D. from NIT Raipur. With 24 publications, her research focuses on wireless sensor networks (WSNs), artificial intelligence (AI), and blockchain applications. She has organized special sessions at international conferences in Vietnam, the Czech Republic, and the UK, and has delivered keynotes on underwater wireless sensor networks and the Internet of Things (IoT). Dr. Kumari's work includes developing AI-driven healthcare solutions and ensuring IoMT data security using Hyperledger Fabric for smart cities. Her contributions have been published in high-impact journals, including those by Springer, Elsevier, and IEEE. Currently, she is leading collaborative research projects with the Indian Institute of Technology Delhi on underwater network modeling and drone security applications. Additionally, Dr. Kumari is an Associate Member of IEEE, an Ambassador and Advisor for Women in Data Science (WiDS) 2024, and actively participates in various scientific gatherings as a conference organizer and keynote speaker.
Affiliations and expertise
Assistant Professor, Bennett University, Greater Noida, India

AK

Ashish Khanna

Ashish Khanna is Professor in Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain. His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning.

Affiliations and expertise
Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India

BV

Bal S. Virdee

Prof. Bal S. Virdee holds a BSc (Engineering) Honours in Communication Engineering and an MPhil from Leeds University, UK, along with a PhD from the University of North London. He has served as an academic at both Open University and Leeds University. Before his academic career, he worked as a research and development Electronic Engineer at Teledyne Defence and PYE TVT (Philips) in Cambridge. Throughout his tenure at the university, Prof. Virdee has taken on various roles, including Health and Safety Officer, Postgraduate Tutor, Examination’s Officer, Admission’s Tutor, and Course Leader for MSc/MEng Satellite Communications, BSc Communications Systems, and BSc Electronics. In 2010, he became the Academic Leader for Undergraduate Recruitment. He is also an active member of the ethical committee, the school's research committee, and the research degrees committee, contributing significantly to the academic community.
Affiliations and expertise
Director, Centre for Communications Technology, London Metropolitan University, London Metropolitan University, London, UK

SK

Samiya Khan

Dr. Samiya Khan is an alumna of Sri Venkateswara College, University of Delhi (Bachelor’s Degree), Institute of Informatics and Communication, University of Delhi, South Campus (Master’s Degree) and Jamia Millia Islamia, New Delhi (Ph.D. – Computer Science). Currently, she is working as a postdoctoral research fellow at University of Wolverhampton, United Kingdom. Her current research focuses on the use of artificial intelligence for Internet of Things (IoT) applications.
Affiliations and expertise
Lecturer, Computer Science, University of Greenwich, Greenwich, UK