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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.14086 |
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| _version_ | 1866911596128763904 |
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| author | Parikh, Harsh McCormick, Tyler Johnson, Emily Hickey, Leo Ranney, Megan Mukherjee, Bhramar |
| author_facet | Parikh, Harsh McCormick, Tyler Johnson, Emily Hickey, Leo Ranney, Megan Mukherjee, Bhramar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14086 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Epidemiology of Artificial Intelligence Parikh, Harsh McCormick, Tyler Johnson, Emily Hickey, Leo Ranney, Megan Mukherjee, Bhramar Other Statistics 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. |
| title | The Epidemiology of Artificial Intelligence |
| topic | Other Statistics |
| url | https://arxiv.org/abs/2604.14086 |