Artificial Intelligence in Immunoengineering: Methods, Models, and Translational Applications examines how AI reshapes immunoengineering to design immune-modulating biomaterials, imaging systems, and diagnostic tools. The book presents an integrated view of data-driven approaches that accelerate discovery and translation in biomedicine. It 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. Sections address 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 will gain standardized workflows, mechanism-aware modeling, and reproducible pipelines that advance immunotherapies and diagnostics from concept to clinical impact.

• Standardizes AI workflows for immune data, providing templated pipelines, pitfalls, and validation checklists
• Presents mechanism-aware modeling that combines predictive ML with causal and dynamical approaches tailored to immune biology
• Offers translation and regulatory guidance, including study design, validation, documentation, and deployment considerations