Artificial intelligence across the aging continuum: Mechanistic geroscience, therapeutic innovation, and clinical impact

Aging emerges from nonlinear interactions among primary, antagonistic, and integrative hallmarks that progressively erode tissue resilience. As global demographics shift and chronic disease burden intensifies, extending healthspan with mechanistic precision has become imperative, accelerating the incorporation of artificial intelligence into geroscience. AI leverages multi-omics, spatial biology, imaging, and clinical data to reveal nonlinear structures linking hallmark interactions to tissue vulnerability and organismal decline. These mechanistic insights inform target prioritization, perturbation-based pathway modeling, and rational design of multi-target geroprotectors, including compounds already advancing through clinical trials. Beyond discovery, AI supports synthetic data generation, cross-disease repurposing, and personalized geriatric care through digital phenotyping and predictive analytics. However, these advances hinge on confronting fundamental challenges in data quality, confounding variables, batch effects, and technical artifacts that risk encoding spurious correlations, necessitating hierarchical experimental validation and explainable AI to distinguish causal mechanisms from epiphenomena. Algorithmic bias, digital ageism, privacy vulnerabilities, and infrastructural inequalities further threaten to exacerbate disparities among vulnerable aging populations. This review uniquely traces AI across the complete translational continuum, from hallmark-grounded biomarker discovery to clinical deployment, while positioning validation rigor and ethical infrastructure as core scientific determinants of a transformative AI-geroscience ecosystem.