Fostering Healthcare through Artificial Intelligence

A High-Performance Computing Approach
1. Auflage 2025
ISBN: 978-1-032-82118-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 276 Seiten, Format (B × H): 156 mm x 234 mm

In this book, readers can explore the transformative power of artificial intelligence (AI) in reshaping modern healthcare. The book dives deep into how cutting- edge technologies are revolutionizing diagnostics, patient care, and medical research. From the predictive analytics that anticipate disease outbreaks to intelligent systems that enhance clinical decisionmaking, this book reveals how AI technologies are playing a transformative role in healthcare domain. Designed for healthcare professionals and AI enthusiasts, this book presents real- world applications, ethical considerations, and visionary insights from researchers and industry. The book provides readers a comprehensive understanding of AI’s potential to drive meaningful change.

This book:

- Covers high-performance computing field programmable gate array networks for machine learning-based biomedical applications

- Presents machine learning algorithms for cancer susceptibility, recurrence, and survival prediction

- Discusses the use of high-performance computing (HPC) in the execution of low-power processors for healthcare devices

- Demonstrates detailed performance analysis of wearable healthcare devices

- Highlights the use of Pix2pix generative adversarial networks for mammographically occult breast cancer detection

Whether you're navigating the complexities of digital health transformation or simply curious about AI’s role in medicine, Fostering Healthcare through Artificial Intelligence is your essential guide to understanding how technology can foster a healthier, smarter future for all.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Khare, Kavita

Khare, Nilay

Aizaz, Zainab

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik

Inhaltsverzeichnis

1. Introduction To AI And High-Performance Computing In The Healthcare Domain. 2. Introduction To ML Algorithms For Biomedical Applications. 3. Biomedical Signal & Image Processing Using Machine Learning. 4. Machine Learning Algorithms for Cancer Susceptibility, Recurrence and Survival Prediction: Viability, Challenges And Survey On The State-Of-The-Art. 5. AI Technologies For Diagnosis, Prognosis and Survival Of Neurological, Cardiovascular and Infectious Diseases: State-Of-Art Analysis. 6. Case Studies. 7. Application Of Machine Learning to Gynecological Cancers. 8. Wearable Healthcare Devices. 9. High-Performance Computing (HPC) Systems for Healthcare. 10. Quantum Computing in Medical Informatics: Unlocking Revolutionary Potential.

Über Autor(innen)

Kavita Khare received her B. Tech in Electronics and Communication Engineering in 1989, M. Tech. in Digital Communication Systems in 1993 and PhD degree in the field of VLSI Design in 2004. She has nearly 27 years of teaching experience and 200 publications in various international conferences and journals. She has supervised nearly 20 PhD thesis and nearly 42 M. Tech thesis. She also received the Best Paper Award at an international conference in France and is a reviewer of several journals and conference proceedings. Dr Khare co-coordinates short-term courses on VLSI Design organized at MANIT Bhopal. Currently, she is working as a Professor at the Department of Electronics and Communication Engineering in MANIT, Bhopal, INDIA. Her areas of interest are VLSI design and Communication Systems, and her research mainly includes design of arithmetic circuits and various communication algorithms related to synchronization, estimation and routing. She is also a Fellow IETE (India) and member of IEEE, Life Member ISTE.

Zainab Aizaz is a VLSI front-end designer and Research Fellow in the School of Engineering and Informatics at the University of Sussex. She completed her PhD from Maulana Azad National Institute of Technology, Bhopal, India. She has worked as an assistant professor for six years in Electronics and Communication Engineering department in Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India, and as a research fellow for three years in Barkatullah University, Bhopal, Madhya Pradesh, India. She has 17 international journal and conference publications along with 3 book chapters. She also won the state's annual young scientist award in India in the year 2020 in India. Zainab designs hardware for Artificial Intelligence systems on Field Programmable Gate Arrays (FPGA). Her recent focus is to design energy efficient Vector Processor as an extension to the RISCV processor. Her research interests include FPGAs, Hardware description language, Computer architecture, Artificial Intelligence.

Nilay Khare received his B. Tech degree in 1988, M. Tech. degree in Computer Science Engineering from IIT Delhi in 1995 and PhD degree in the field of VLSI Design in 2009. He has nearly 34 years of teaching experience. His major research work is on Hardware Implementation of Neural Network, FPGA implementation of CORDIC processor, Machine Learning, Data Privacy. His PhD thesis is on efficient design of CORDIC processor on FPGA. He has supervised three PhDs in the area of VLSI design namely FPGA implementation of neural networks, hardware implementation of string-matching algorithms and design and implementation of hardware firewall. He has expertise in AI, Machine learning and deep learning fields, and has supervised many UG ang PG projects in these areas. He has published nearly 1000 papers in top journals and conferences. He also has 3 patents to his name. The primary areas of his expertise are AI, ML, VLSI, Theoretical Computer Science, and IoT.