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Closing the AI benefits gap: Systems design for population health equity
Jessica Morley1, Luciano Floridi2
1Digital Ethics Center, Yale University, 85 Trumbull Street, CT, 06510, United States.
Public Health
|February 20, 2026
Summary
Artificial Intelligence (AI) in healthcare shows limited population health improvements due to inadequate foundations and an individual-level focus. Realigning AI to population health goals and co-creating solutions with diverse stakeholders is crucial for realizing its potential.
Area of Science:
- Public Health
- Health Informatics
- Artificial Intelligence
Background:
- Artificial Intelligence (AI) promises to enhance healthcare effectiveness, efficiency, and equity, but widespread benefits and population-level outcome improvements are not consistently observed.
- Current AI applications in healthcare often face challenges in scaling and demonstrating tangible population health gains, despite successful implementations.
Purpose of the Study:
- To identify the root causes of the "benefits gap" in healthcare AI implementation.
- To propose a new framework for AI development and deployment in healthcare to achieve population health objectives.
Main Methods:
- Analysis of challenges and limitations in current AI healthcare applications, drawing from the 2024 Global Health in the Age of AI symposium.
- Argumentation for a fundamental shift in AI's function and foundational design in healthcare.
Main Results:
- The "benefits gap" in healthcare AI is attributed to two core issues: inadequate foundational infrastructure and an inappropriate focus on optimizing individual health rather than population health.
- Existing policy interventions are insufficient to close this gap; a systemic redesign is required.
Conclusions:
- AI must be repurposed for population-level health functions, supported by robust, systems-designed foundations.
- Co-creation of AI's function and foundations by affected communities, healthcare professionals, and AI developers is essential to realize AI's population health potential and prevent a "healthcare AI winter".
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