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arXiv · 2604.14086

The Epidemiology of Artificial Intelligence

Harsh Parikh·Tyler McCormick·Emily Johnson·Leo Hickey·Megan Ranney·Bhramar Mukherjee
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Abstract

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.

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Harsh Parikh, Tyler McCormick, Emily Johnson, Leo Hickey, Megan Ranney, Bhramar Mukherjee. 2026-04-15. The Epidemiology of Artificial Intelligence. https://arxiv.org/abs/2604.14086

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