Artificial Intelligence in Immunoengineering

Methods, Models, and Translational Applications
Erscheinungsjahr 2027
ISBN: 978-0-443-51657-3
Verlag: Elsevier Science

Buch, Englisch, 440 Seiten, Format (B × H): 191 mm x 235 mm

Artificial Intelligence in Immunoengineering: Methods, Models, and Translational Applications examines how AI reshapes immunoengineering to design immune-modulating biomaterials, imaging systems, and diagnostic tools. It presents an integrated view of data-driven approaches that accelerate discovery and translation in biomedicine. Convergence is critical to the success of immunoengineering, and drawing on fields outside of its largely biomaterials-based beginnings, to areas such as biomedical data science, computational medicine, and public health, quickening its progress and broadening its impact. This book surveys biological data from single-cell to spatial omics and proteogenomics, and offers practical machine learning frameworks, generative and mechanistic models, and multimodal integration strategies. It addresses bench-to-bedside translation, validation pipelines, and regulatory considerations for AI-enabled immunoengineering tools. The book highlights the rapid progress of multidisciplinary research within immunotherapy and immunoengineering. Looking forward, the prospects of immunoengineering appear promising, with further advancements in disease prevention, diagnostics, and treatment on the horizon. Graduate students, academic researchers, clinician-scientists, and computational biologists gain standardized workflows, mechanism-aware modeling, and reproducible pipelines that advance immunotherapies and diagnostics from concept to clinical impact.

Autoren/Hrsg.

Zanganeh, Steven

J Webster, Thomas

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

1. Artificial Intelligence in Immunoengineering: Scope and Opportunities
2. Immune Data Landscape: From Single-Cell to Spatial and Proteogenomics
3. AI in Tissue Engineering and Regenerative Immunology
4. Biomaterials, Nanomedicine, and AI-Driven Immunomodulation
5. Machine Learning Frameworks for Immune and Bioengineering Data
6. Generative and Mechanistic Models in Bioengineering and Immunity
7. Multimodal Fusion: Integrating Imaging, Omics, and Engineered Models
8. AI in Immunoimaging and Digital Pathology
9. From Bench to Bedside: Validation, Deployment, and Regulation
10. Future Directions: Digital Twins, Systems Bioengineering, and Responsible AI

Über Autor(innen)

J Webster, Thomas
Dr. Thomas J. Webster is a pioneering professor in biomedical engineering, recognized for his leadership in academia and industry. His research focuses on applying nanotechnology to develop advanced materials for medical use, significantly improving healthcare outcomes. Dr. Webster has held prominent faculty positions in top U.S. and international universities, chaired departments, and directed international centers in biomaterials. He has also founded numerous companies translating his research into FDA-approved medical products. Widely published and a sought-after speaker, his work has been featured in major media outlets and science museums. Dr. Webster is renowned for organizing global conferences and symposia on nanomedicine and biomaterials, fostering interdisciplinary collaboration. He is the recipient of numerous prestigious awards and fellowships from leading scientific and medical organizations, demonstrating his impact and innovation in biomaterials, nanomedicine, and biomedical engineering.

Zanganeh, Steven
Dr. Steven Zanganeh is an Assistant Professor at the New York Institute of Technology, USA. He earned his PhD in Biomedical Engineering from the University of Connecticut, USA, in 2014. He completed his postdoctoral training at Stanford University, USA, and Memorial Sloan Kettering Cancer Center in New York, USA.