Health systems, public health agencies, research institutions, and community-based organizations should proactively participate in this evolving AI policy landscape to safeguard health equity. Institutions can start by tracking federal rule-making and public comment opportunities related to the AI Action Plan, ensuring that the voices of historically underserved populations are included and that equity criteria (e.g., demographic representation in data sets, algorithmic fairness) are prioritized.
Research universities and funders can embed equity metrics into AI-health-care studies, diversify participation in dataset development, and evaluate how AI tools impact different racial, ethnic, rural, low-income, immigrant, and disability populations.
Healthcare delivery systems should assess their AI procurement and deployment frameworks to ensure transparency, avoid perpetuating bias, and plan for digital access gaps (such as broadband, device access, and language barriers) that could otherwise widen the divide.
Communicate internally and externally that the acceleration of AI is not just a technical issue but a health equity imperative: clarity about how innovations serve underserved communities, training for staff on bias and structural inequality in algorithms, and partnerships with community organizations to ensure inclusive design are key.