Social, structural and environmental determinants of health, such as food or housing insecurity, systemic racism or chronic stress, account for 60–80% of the modifiable risk of disparities in marginalized populations1,2. Such determinants have been difficult to address systematically because of their complexity, multidimensionality and heterogeneity (Fig. 1). Emerging precision health methods use large-scale person-generated health data from smartphones and wearables to better characterize and, ultimately, improve health and well-being through strategies customized to individual context and need3,4. Applying artificial intelligence and machine learning to person-generated health data allows unprecedented assessment of recursive, networked and latent associations between everyday life and health, including social, structural and environmental exposures, behaviors, biometrics, and health outcomes. Thus, precision health provides an important opportunity for reducing health disparities among minoritized racial or ethnic groups, or those who are under-resourced.
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