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Is artificial intelligence a friend or foe to epidemiology?
Emaan Rashidi1, Madeline Brooks2, Ahmed Hassoon2
1Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Annals of Epidemiology
|January 14, 2026
Summary
Artificial intelligence and machine learning (AI/ML) offer new ways to study disease causes and spread in public health. Epidemiologists must adapt training and methods to use AI/ML effectively for robust scientific insights.
Area of Science:
- Epidemiology and Public Health
- Artificial Intelligence in Medicine
- Biostatistics and Data Science
Background:
- Epidemiology is crucial for understanding disease distribution and determinants.
- The field has evolved significantly, incorporating advanced statistical methods.
- Artificial intelligence/machine learning (AI/ML) presents new opportunities and challenges for epidemiology.
Purpose of the Study:
- To examine how epidemiologists can effectively utilize AI/ML.
- To address the methodological and ethical considerations of AI/ML in epidemiology.
- To guide the integration of AI/ML into epidemiologic practice and training.
Main Methods:
- Review of core epidemiologic domains (study population, measurement, inference) in the context of AI/ML.
- Analysis of AI/ML applications for data measurement, inference, and population health insights.
- Exploration of challenges including generalizability, bias, data quality, and model reliability.
Main Results:
- AI/ML offers potential to enhance data measurement, inference, and public health insights.
- Effective AI/ML use requires careful population definition, sampling, and external validation.
- Rigorous assessment of data quality and model reliability is essential for interpretation.
Conclusions:
- Strategic integration of AI/ML into epidemiology is vital for advancing science and public health.
- Epidemiology must adapt training, invest in infrastructure, and foster interdisciplinary collaboration.
- Ensuring robustness, reproducibility, and relevance in the evolving informational landscape is key.
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