The Public Health AI Handbook
Evaluating AI Tools for Public Health Practice
Welcome to The Public Health AI Handbook
AI tools often perform worse in the field than in development studies. Health departments, ministries of health, and public health agencies worldwide face the same challenge: separating tools that work from tools that were validated on clean data they will never have.
Three questions drive this handbook: Which AI tools actually perform in real-world public health settings? How do you evaluate a model when your data is messy, delayed, and incomplete? What happens when the algorithm is wrong and public health action follows?
This resource is continuously updated as new research emerges.
Important Disclaimers
This handbook is for educational and informational purposes only. It does not provide official public health guidance, operational protocols, or policy recommendations. It is not a substitute for guidance from public health authorities (CDC, WHO, state/local health departments).
Public health use remains context-specific. Officials should validate AI outputs before action, follow applicable privacy and data-governance requirements, and meet professional standards in their jurisdiction.
Information may become outdated as AI tools, evidence, and public health practice change. Verify recommendations with current public health guidelines and protocols before implementation.
This handbook does not provide legal or regulatory advice. Consult qualified legal counsel for questions about data governance, procurement, liability, and regulatory compliance.
Recent highlights
- 20 Oct Course syllabus: grading rubric, learner AI-use policy, and CEPH competency map Instructors can adapt the assessment criteria and data-use safeguards to public health AI teaching. In Course Syllabus Template
- 10 Oct EASAC and FEAM recommend clearer Annex III guidance for healthcare AI (opens the source in a new tab) Healthcare AI classification determines which regulatory obligations apply to a particular use. In AI Policy and Governance in Healthcare
- 9 Oct HHS civil enforcement for 42 CFR Part 2 substance-use treatment records (opens the source in a new tab) Programs that hold substance-use treatment records now face civil enforcement for Part 2 violations, the first time it has been available. In AI for Substance Use and Overdose Prevention
- 8 Oct Anthropic cyber-defense access for hospitals and public infrastructure (opens the source in a new tab) Public health security teams can assess access programs for defending the systems their organizations operate. In Selecting and Using Large Language Models
- 7 Oct OECD Expert-Led Madrid Action Plan for scaling AI in health (opens the source in a new tab) The proposal connects implementation capacity, oversight, infrastructure, and financing to responsible scale. In AI Policy and Governance in Healthcare
Start Here
Read the evidence gaps first, then the core surveillance example.
- Executive Summary: Key findings and evidence gaps
- AI in Healthcare: A Brief History: Context for AI in public health
- Disease Surveillance and Outbreak Detection: Core AI application
Then continue to Evaluating AI Systems for Healthcare before adopting any tool.
For role-specific reading paths, see the Preface; for teaching, see the Course Syllabus Template and the Case Study Library.
An AI assistant helping you should follow the Guidance for AI Agents.
Companion Handbooks
The Physician AI Handbook
Peer-Reviewed Evidence Across Every Medical Specialty
What works. What doesn’t. What happens when AI is wrong.
The Biosecurity Handbook
Biological Security in the AI Era
A guide for two communities that don’t speak the same language.
The Life Sciences AI Handbook
Evidence for AI in Biology and Drug Discovery
What works, what remains unproven, and what stands in the way.
Free to read and reuse under CC BY 4.0 · DOI 10.5281/zenodo.18263442 · How to cite
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