The Epidemiology of Artificial Intelligence

TL;DR

This paper introduces a framework for studying AI as a health determinant at the population level, distinguishing between ambient and personal AI exposure, and discusses implications for epidemiology and health equity.

Contribution

It proposes a novel conceptual framework borrowed from environmental epidemiology to measure and study AI's health effects at the population level.

Findings

AI functions as a determinant of health.

Existing experimental methods are inadequate for population-level effects.

Illustrates ideas with US survey data.

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.