Artificial intelligence and machine learning in immunosenescence: from biomarker discovery to clinical translation

The global population is undergoing unprecedented aging, with immunosenescence established as a core upstream driver of nearly all age-related chronic diseases, imposing a massive global clinical burden. For decades, immunosenescence research has been mired in three persistent translational bottlenecks: inability to capture interindividual immune aging heterogeneity, failure to decode complex multi-layered biological regulatory networks, and inefficient therapeutic development pipelines. Artificial intelligence (AI) and machine learning (ML) have emerged as promising solutions to these bottlenecks, yet existing literature fails to systematically bridge AI technical advances with clinical immunology practice, and often does not distinguish proof-of-concept evidence from the steps needed for clinical use. This review provides a holistic, critical overview of AI/ML applications across the full translational spectrum of immunosenescence research, from mechanistic discovery, biomarker development, diagnostic innovation to therapeutic development and personalized medicine. We further analyze unresolved technical, ethical, and regulatory barriers to clinical translation, including underrecognized fundamental flaws in current model design. Emerging AI technologies to address these limitations are outlined, alongside a clinically realistic translational roadmap and key future research trends. This review fills critical gaps in existing literature, providing a rigorous framework to shift the field from "AI for AI's sake" to clinical utility-focused research, ultimately advancing healthy aging interventions.