Resources for Public Health Practitioners
Essential frameworks, definitions, tools, and guidance for implementing AI responsibly and effectively in public health practice.
Essential frameworks, definitions, tools, and guidance for implementing AI responsibly and effectively in public health practice.
Official U.S. government definitions and foundational concepts for understanding AI in public health contexts.
Official Definition (15 U.S.C. 9401(3)): "A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments."[24]
AI systems use computational techniques to solve problems, analyze data, and support decision-making processes that traditionally required human intelligence.
Official Definition: "An application of artificial intelligence... characterized by providing systems the ability to automatically learn and improve on the basis of data or experience, without being explicitly programmed."[24]
ML is a subset of AI focused on algorithms that improve performance through exposure to data, enabling pattern recognition and predictive modeling.
Real-time monitoring of health indicators (symptoms, emergency visits) to detect outbreaks before laboratory confirmation.
Using historical and real-time data to forecast disease trends, resource needs, and intervention impacts.
AI techniques that extract structured insights from unstructured text (clinical notes, death certificates, news).
Training AI models across multiple institutions without centralizing sensitive data, preserving privacy.
Mathematical techniques that protect individual privacy while enabling statistical analysis of populations.
Ensuring AI systems perform equitably across demographic groups and don't perpetuate health disparities.
Federal and international frameworks guiding responsible AI development and deployment in public health.
The National Institute of Standards and Technology (NIST) AI Risk Management Framework provides a comprehensive approach to managing risks associated with AI systems. It emphasizes trustworthiness, safety, security, and resilience.[22]
Core Functions: Govern, Map, Measure, Manage—addressing risks throughout the AI lifecycle.
View NIST AI RMFOffice of Management and Budget guidance establishes government-wide standards for responsible AI acquisition and deployment, emphasizing innovation balanced with public trust and safety.[23]
M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust
M-25-22: Driving Efficient Acquisition of Artificial Intelligence in Government
CDC's framework for using AI in public health emphasizes safe and secure implementation with human oversight, research excellence, and robust security. The AI Accelerator (AIX) program demonstrates federal leadership in responsible AI deployment.[5]
View CDC AI VisionThe World Health Organization's ethical guidance provides the first harmonized international standards for AI in health, emphasizing human rights, autonomy, privacy, and equity.[9]
Six Principles: Protecting autonomy, promoting well-being, ensuring transparency, fostering responsibility, ensuring inclusiveness, promoting AI that is responsive and sustainable.
View WHO GuidanceResources for developing AI literacy, assessing organizational readiness, and building workforce competencies.
Evaluate your organization's preparedness for AI adoption across data infrastructure, governance, workforce capabilities, and ethical frameworks. Based on CDC's approach to assessing public health AI maturity.
Key Dimensions: Data quality & interoperability, technical infrastructure, workforce skills, governance structures, privacy & security, stakeholder engagement.
Public health professionals implementing AI need skills across domains: epidemiology, data science, ethics, policy, and change management. CDC's AI Community of Practice (2,200+ members) provides peer learning.[5]
Hands-on training covering AI fundamentals, model interpretation, responsible deployment, and critical evaluation of AI outputs. Tailored to public health practitioners without deep technical backgrounds.
Request TrainingComprehensive documentation for AI models following best practices for transparency and reproducibility. Each model card details intended use, training data, performance metrics, limitations, and ethical considerations.
View Model CardsReal-world examples of AI deployment in public health settings, including COVID-19 forecasting, opioid overdose prediction, and foodborne illness outbreak investigation. Learn from successes and challenges.
Explore Case StudiesDetailed technical guidance on implementing privacy-preserving AI techniques (federated learning, differential privacy), HIPAA compliance, data governance, and security best practices for health data.
Privacy Framework Security PracticesPeer-reviewed research, white papers, and technical reports covering AI methodologies, validation studies, ethical frameworks, and implementation science. All citations and evidence sources documented.
View Publications All ReferencesCDC's comprehensive resources on AI/ML for public health, including the AI Accelerator program, National Syndromic Surveillance Program, and public health data strategy.
Visit CDC AI ResourcesNational Institute of Standards and Technology tools, frameworks, and guidance for trustworthy AI development, including the AI Risk Management Framework and technical standards.
Visit NIST AI PortalWorld Health Organization's Global Initiative on AI for Health (GI-AI4H), providing international guidance, standards, and resources for implementing AI in health systems globally.
Visit WHO ResourcesOur team provides hands-on assistance with AI readiness assessment, pilot program design, workforce training, and technical implementation for public health agencies.