Personal Intelligence: A Causal Framework for Human-Centric Health AI

Population-scale AI systems excel at prediction but fall short of individual understanding. This framework paper introduces Personal Intelligence (PI), a causal reasoning architecture designed to personalize health AI at the level of one. Built on the Personal Health Knowledge Graph (PHKG) and a layered causal-learning stack, PI links physiological, behavioral, and contextual data to learn high-probability cause-effect relationships unique to each person. Reinforcement learning fine-tunes interventions based on observed outcomes, closing the gap between correlation and comphrension. The framework integrates population-validated clinical priors for safety, then progressively activates causal discovery as personal evidence grows. By ensuring that every recommendation is traceable to it's causal path, PI emphasizes explainability, safety and accountability. 

This paper positions Personal Intelligence as a necessary complement to population modeling in healthcare, addressing the limitation of data sufficiency, interpretability, and clinical relevance in the emerging N-of-1 era.