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This chapter provides case studies about artificial intelligence (AI) applications for systemic factors in public health. It begins with a conceptual framework and a structured design process for developing effective AI solutions in this complex domain. This exploration is organized around key application areas: enhancing disease surveillance and epidemiological forecasting, optimizing emergency and disaster response, improving population health management, and advancing health equity-aware risk stratification. For each topic, the chapter moves from concept to practice by proposing case scenarios and demonstrating methodical AI solution frameworks. A detailed step-by-step walkthrough of an equity-aware risk stratification model (EquiRisk) is provided to exemplify how to apply our AI design principle, “encoding justices,” in public health practices (e.g., diabetes patients within food deserts).

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Al Applications for Public Health Systemic Factors

  • 章节

  • 发表日期：

    2026年

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  • Min Wu

摘要

This chapter provides case studies about artificial intelligence (AI) applications for systemic factors in public health. It begins with a conceptual framework and a structured design process for developing effective AI solutions in this complex domain. This exploration is organized around key application areas: enhancing disease surveillance and epidemiological forecasting, optimizing emergency and disaster response, improving population health management, and advancing health equity-aware risk stratification. For each topic, the chapter moves from concept to practice by proposing case scenarios and demonstrating methodical AI solution frameworks. A detailed step-by-step walkthrough of an equity-aware risk stratification model (EquiRisk) is provided to exemplify how to apply our AI design principle, “encoding justices,” in public health practices (e.g., diabetes patients within food deserts).