HealthTech Horizons

Charting the Future of Smart Healthcare Innovations, Deepfake and Metaverse
1. Auflage 2026
ISBN: 978-87-438-0991-3
Verlag: Taylor & Francis Ltd

Buch, Englisch, 306 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 610 g

Reihe: River Publishers Series in Biomedical Engineering

HealthTech Horizons provides a concise yet comprehensive view of how cutting-edge technologies are transforming healthcare. Covering AI and machine learning in diagnostics, deep learning architectures (CNNs, RNNs, GANs) for genomics and imaging, synthetic data augmentation, and optimization algorithms (MRMR, EHO, HHWO) for complex biomedical datasets, this book bridges research with real-world applications.

It also examines the disruptive rise of deepfake technologies and the metaverse in telemedicine, surgical training, and immersive patient care, while addressing the ethical and computational challenges of integrating these tools responsibly. Designed for researchers, clinicians, and innovators, HealthTech Horizons is both a technical reference and a roadmap for the next era of smart, ethical, and intelligent healthcare systems.

The digital revolution in healthcare is no longer on the horizon—it’s here. HealthTech Horizons dives deep into the convergence of biomedical science and advanced computational techniques, offering a research-driven perspective on how technology is redefining modern medicine. This book explores:

- Artificial intelligence and machine learning in diagnostics, treatment planning, and personalized medicine.

- Deep learning architectures (CNNs, RNNs, GANs) applied to genomics, medical imaging, and biomarker discovery.

- Synthetic data augmentation and generative adversarial networks (GANs) for enhancing predictive accuracy.

- Feature selection and optimization algorithms (e.g., MRMR, EHO, HHWO) for high-dimensional biomedical datasets.

- Deepfake technologies and their dual role in healthcare innovations and security threats.

- Metaverse applications in telemedicine, surgical simulation, and immersive patient care.

- Ethical and computational challenges in deploying AI responsibly in clinical practice.

Bridging research and practice, this book is an indispensable resource for data scientists, bioinformaticians, clinicians, and healthcare innovators seeking to understand—and shape—the next frontier of smart healthcare systems. HealthTech Horizons is not just about the future; it is a roadmap for leveraging algorithms, data, and virtual ecosystems to create resilient, ethical, and intelligent healthcare solutions.

Zielgruppe

Academic, Postgraduate, and Professional Practice & Development

Autoren/Hrsg.

Mallik, Saurav

Seth, Soumita

Soufiene, Ben Othman

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Gesundheitssystem, Gesundheitswesen

Inhaltsverzeichnis

Chapter 1. Introduction: Fundamentals of HealthTech Horizons with Smart Healthcare Innovations, Deepfake and the Metaverse Chapter 2. Health Digital Twins: A New Era in Healthcare Chapter 3. Exploring Deepfake Technology in Healthcare: Innovations, Applications, and Challenges Chapter 4. Regulatory Frameworks and Ethics in Smart Healthcare Innovations: Deepfakes and the Metaverse Chapter 5. Emergency Response Training in Metaverse Scenarios Chapter 6. Orchestrating Healing Harmonies: The Sublime Confluence of Artificial Intelligence and Healthcare Chapter 7. Hybrid Optimization Techniques for Gene Expression Analysis: The EHOSVM Paradigm Chapter 8. Decoding Breast Cancer: Unleashing RNA-Seq Insights with Advanced Deep Learning and Modified WOA Chapter 9. A Smart Healthcare Innovation for Detecting Colorectal Cancer through the Integration of ADAM Optimizer and Categorical CrossEntropy Loss Function-based CNN Method Chapter 10. Improved Prediction of Heart Disease through Machine Learning Models and Optimized Hyperparameters Chapter 11. GWF Algorithm for Image Forgery Detection

Über Autor(innen)

Dr. Saurav Mallik is currently working as Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, USA. Previously, he worked as Postdoctoral Fellow in Harvard T.H. Chan School of Public Health, University of Texas Health Science Center at Houston, and University of Miami Miller School of Medicine, USA. He obtained his Ph.D. degree in the Department of Computer Science and Engineering from Jadavpur University, Kolkata, India in 2017 while his Ph.D. work was carried carried out in Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India while he was Junior Research Fellow and Visiting Scientist. He obtained the award of Research Associateship from CSIR (Council of Scientific and Industrial Research), MHRD, Govt. of India in 2017. Dr. Mallik has more than 130 research papers in different top high impact factor peer-reviewed International Journals, Conferences and Book Chapters having an H-index of 19. He is working as the active member of Institute of Electrical and Electronics Engineers (IEEE), USA, ACM and American Association for Cancer Research (AACR), USA and Bioclues, India. His research interest includes computational biology, knowledge retrieval and data mining, bioinformatics, biostatistics and machine learning/deep learning.

Dr. Soumita Seth is currently serving as an Assistant Professor in the Department of Computer Science and Engineering of Future Institute of Engineering and Management, Kolkata, India, Affiliated to MAKAUT, Kolkata. Previously, she worked as Assistant Professor in the Department of Computer Science and Engineering of Brainware University, Kolkata (Mar–Aug 2023) and in the Department of Computer Science and Engineering of Pailan College of Management and Technology, Kolkata, India, Affiliated to MAKAUT, Kolkata (Feb 2022– Mar 2023). She is recently submitted her Ph.D. thesis in the Department of Computer Science & Engineering (CSE) from a state-government university, Aliah University (AU), Kolkata, India. Previously, she completed M.Tech. and B.Tech. degrees in the departments of CSE and IT, respectively. She is collaborating on her Ph.D. research with The University of Texas Health Science Center at Houston (UTHealth), USA. She has academic experience of almost 6 years, research experience of 2 years, and industrial experience of 2 years. Dr. Seth has more than 10 research publications in different top high impact factor peer-reviewed international journals, conferences, book chapters and books. Her research interests include computational biology, data mining, bioinformatics, pattern recognition, biological regulatory networks, statistical application on bioinformatics, machine learning/deep learning.

Dr. Ben Othman Soufiene is currently serving as an Assistant Professor in PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Tunisia. Previously, he served as an Assistant Professor of computer science at the University of Gabes, Tunisia from 2016 to 2023. He received his Ph.D. degree in computer science from Manouba University in 2016 for his dissertation on “Secure data aggregation in wireless sensor networks”. He also holds an M.Sc. degree from the Monastir University, 2012. His research interests focus on the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning and big data. Dr. Ben Othman has published more than 70 publications in reputed international journals, conferences, and book chapters. He serves as an associate editor/academic editor for international journals including IEEE Access, IEEE Sensors, IEEE Internet of Things, Elsevier, Springer, Taylor & Francis, IGI, IET, Telecommunication Computing Electronics and Control, and Wiley. Dr. Ben Othman is a Technical Program Committee Member for more than a dozen international conferences.