Immuno-Engineering in the AI Era: From Fundamental Research to Clinical Translation

Abstract

Artificial intelligence (AI) plays a pivotal role in advancing immune engineering, a field aimed at developing personalized therapies for cancer, autoimmune diseases, and infections. AI is overcoming significant challenges in immune engineering, such as immune system complexity, patient-specific variability, and the dynamic interactions within the immune microenvironment. This perspective highlights how AI is bridging the gap between laboratory research and clinical application through three major pillars: 1) Mechanistic Decoding, where AI integrates multi-omics data to understand immune system complexity and predict molecular interactions; 2) Therapeutic Innovation, where AI designs personalized immunotherapies, such as optimizing antibody-antigen binding and immune receptor dynamics; and 3) Clinical Acceleration, where AI enhances clinical trial designs, speeds up drug development, and adjusts therapies in real-time based on patient responses. Further discussion addresses algorithmic bias, data privacy, and the need for global standards in AI-powered decision-making, ethical and regulatory challenges. AI is not only enabling breakthroughs in immune engineering but also paving the way for customized immune-based therapies. It ensures that the technologies are applied responsibly and equitably in clinical practice.