Displaying 14 results for

Guidance on protecting scientific data integrity when using generative AI tools in research and public health contexts.

How explainable AI and One Health principles can be integrated to combat zoonotic diseases and cross-sector health threats.

GenAI-in-healthcare framework with 4 principles: map applications to strengths, define evaluations, balance safety, ensure transparency.

Perspective on applying machine learning in healthcare: cautions against shortcuts, emphasizes evidence, validation, and equity.

Framework for transforming public health practice with AI, covering core capabilities, governance, and equity-centered implementation.

Analysis of how AI could transform public health planning—including forecasting and resource allocation—while raising equity concerns.

Framework for designing AI evaluations that matter: linking AI system assessment to real-world outcomes and stakeholder needs.

Webinar on AI applications in health policy and systems research, exploring the shift from experimentation to real-world implementation.

Content analysis of social determinants of health accelerator plans using AI/NLP to identify themes and equity gaps at scale.

ASTHO Profile data shows how public health agencies are adopting AI, revealing policy gaps, workforce challenges, and uneven use across states.

Connie Moon Sehat discusses ethical considerations for using AI in public health communication.

ASTHO informatics and innovation leaders gathered for an introduction to AI and its potential applications in state and territorial public health.

CDC guide for STLT public health agencies on adopting generative AI tools: governance, risk management, and responsible use practices.

Critical look at why evaluating AI health interventions is often inadequate and what better evaluation frameworks should require.