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Artificial Intelligence in Epidemiology

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Artificial Intelligence in Medicine

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Abstract

John Last defined epidemiology as “The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems.” It underscores that epidemiologists are not concerned only with disease but with health-related events, and that ultimately epidemiology is committed to control of disease. Initially focused on the disease, the objects of investigation in epidemiology now correspond to any factor that may influence the state of health of the human being, i.e., biological, clinical factors, in relation to the physical, mental, and social environment. Regardless of the field considered and the type of epidemiology (clinical or population based) referred to, the basic brick of epidemiology remains the data. The data must be as valid and precise as possible to ensure validity and reliability of results. The use of artificial intelligence and its methods can occur at different levels and in several areas of epidemiology. At present, we can consider three main use cases. First, AI can add to a long tradition of using more or less sophisticated observational data analysis methods. It has a role to play in causal inference. Second, AI can intervene at the stage of reconciling and structuring siled and varied data sources. Finally, AI can simply bring new ways of exploring and using data, such as sentiment analysis applied to social media. These three use cases are found, in practice, often intermingled and do not necessarily meet in isolation from each other. The private sector (intermediation platforms) and policy makers are the two other actors involved in the forms that AI uses in epidemiology will take.

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Authors and Affiliations

  1. IRIS Institut de Recherche Interdisciplinaire sur les enjeux Sociaux, UMR8156 CNRS – U997 Inserm – EHESS – Université Sorbonne Paris Nord, Paris, France

    Thomas Lefèvre

  2. Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France

    Thomas Lefèvre

  3. CERPOP, Center for Epidemiology and Research in POPulation Health, Université de Toulouse, Inserm, UPS, Toulouse, France

    Cyrille Delpierre

Authors
  1. Thomas Lefèvre
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  2. Cyrille Delpierre
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Correspondence to Thomas Lefèvre .

Editor information

Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Lefèvre, T., Delpierre, C. (2022). Artificial Intelligence in Epidemiology. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_97

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  • DOI: https://doi.org/10.1007/978-3-030-64573-1_97

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64572-4

  • Online ISBN: 978-3-030-64573-1

  • eBook Packages: MedicineReference Module Medicine

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