Artificial intelligence (AI) systems increasingly shape how people access health information, make medical decisions, and receive care -- yet epidemiology lacks frameworks for measuring AI exposure or studying its health effects at the population level. Here we argue that AI now functions as a determinant of health and propose a conceptual framework, borrowed from environmental epidemiology, for studying it. We distinguish ambient AI exposure -- algorithmic curation and AI-mediated institutional decisions that affect populations regardless of individual choice -- from personal AI exposure -- direct, volitional use of AI tools. We characterize AI's possible causal roles in epidemiological models, show that existing experimental approaches are inadequate for capturing chronic, population-level effects, and illustrate these ideas with nationally representative US survey data. We discuss implications for study design, health equity, and AI governance.
@article{arxiv.2604.14086,
title = {The Epidemiology of Artificial Intelligence},
author = {Harsh Parikh and Tyler McCormick and Emily Johnson and Leo Hickey and Megan Ranney and Bhramar Mukherjee},
journal= {arXiv preprint arXiv:2604.14086},
year = {2026}
}