Abstract
Ageing is a multidimensional biological process characterised by interacting molecular, physiological, functional, and environmental changes that chronological age alone cannot adequately represent. The rapid development of epigenetic clocks, proteomic and metabolomic signatures, imaging-derived biomarkers, wearable sensors, and digital phenotypes has created an increasingly rich but fragmented landscape of aging biomarkers. Artificial intelligence (AI) offers a potential integrative framework that can combine these heterogeneous data streams and convert them into multidimensional estimates of biological age, health risk, functional reserve, and potentially treatment response. Recent studies demonstrate that molecular and clinical information can be integrated into composite aging measures, while independent work shows that wearable movement data, retinal imaging, and speech characteristics can provide non-invasive digital representations of aging-related phenotypes. The emerging concept of multimodal aging clocks extends these approaches by integrating physiological, clinical, imaging, and molecular measurements into a common representation of biological aging.