Analysis of how AI could transform public health planning—including forecasting and resource allocation—while raising equity concerns.
Perspective on applying machine learning in healthcare: cautions against shortcuts, emphasizes evidence, validation, and equity.
Report on the AI capability gap between advanced AI developers and public health agencies, and strategies to close the divide.
As AI adoption grows in public health, understanding its uses and ensuring responsible application are key to protecting privacy, minimizing bias, and improving outcomes.
5 steps for public health leaders to use AI responsibly: build awareness, pilot uses, manage risk, invest in staff training.
JISC analysis of AI's environmental footprint in context—energy, water, carbon—arguing for proportionate concern and greener AI design.
Substack newsletter on AI applications in public health—policy implications and emerging tools for public health practitioners.
CDC blog on AI in medicine and public health: imaging, big data, and ethical challenges post-pandemic. Rasooly & Khoury, March 2022.
How the data center boom driven by AI risks health outcomes in vulnerable communities through noise, pollution, and energy burdens.
Op-ed arguing public health's real risk is AI hesitation, not adoption—urging professionals to engage and shape AI use. Forbes, 2026.
Study on using AI to improve public health campaigns across diverse U.S. communities through personalization and data-driven messaging.
Critical look at why evaluating AI health interventions is often inadequate and what better evaluation frameworks should require.
Discussion of health equity and ethical considerations when using AI in public health programs and policy decisions.
Poll finding many U.S. adults consider AI-generated health information useful and reasonably reliable for health questions.
NCSL overview of legal and regulatory considerations for states addressing artificial intelligence in legislation and policy.
Policy and community health considerations for mitigating public health impacts from AI data center siting and operations.
UNEP overview of AI's environmental footprint—energy, water, e-waste, emissions—with policy recommendations for greening data centers.
CDC's vision for AI in public health: accelerating detection and response, reducing operational burden, and empowering an AI-ready workforce.