High-Performance and Supercomputing in the Medical Sector using AI

This interdisciplinary guide explores the applications of High-Performance Computing (HPC) and Supercomputing in healthcare, bridging the gap between computational science and clinical practice. Through practical case studies, it examines HPC-enabled genomic sequencing, medical image reconstruction, AI-driven diagnostics, predictive modelling, and large-scale epidemiological research. The book also addresses the deployment of scalable computing infrastructures across GPU clusters, hybrid cloud-HPC systems, and exascale environments, while considering the ethical, regulatory, and environmental challenges associated with advanced healthcare computing.

Key Features

• Explores the applications of High-Performance Computing and Supercomputing in medical imaging, genomics, drug discovery, and AI-assisted diagnostics.
• Provides an application-oriented framework that integrates HPC, AI/ML, and cloud technologies, supported by case studies, datasets, and project ideas.
• Covers emerging topics such as exascale computing, quantum bioinformatics, digital twins, and green HPC for next-generation computational medicine.
• Addresses ethical, legal, and data security considerations, including blockchain-based information management and the responsible use of AI.
• Adopts an interdisciplinary approach enhanced by illustrations, learning objectives, and clinical case studies.

This book is aimed at researchers, practitioners, and graduate students in biomedical engineering, computer science, healthcare technology, and related disciplines, offering both theoretical foundations and practical insights into the future of computational medicine.