Researchers at the Translational Genomics Research Institute and City of Hope outline a framework that integrates AI-driven analyses of large-scale health data with aggregated single-case experimental designs. By leveraging artificial intelligence to predict patient subgroups and validating those predictions through personalized N-of-1 trials, the approach seeks to refine precision interventions and optimize treatment strategies for healthy aging.

Key points

• AI-based population modeling integrates EHR and omics data to predict subgroup-specific intervention responses.
• Aggregated N-of-1 trial designs with deep phenotyping validate predictive AI models and reveal individual heterogeneity.
• Framework supports ultra-precision interventions—such as antisense oligonucleotides and geroprotectors—for healthy aging outcomes.

Why it matters: This integration of AI-driven evidence with personalized trial designs accelerates precision therapy validation, transforming clinical decisions for healthy aging.