flowchart LR
A[Part I:<br/>Foundations] --> B[Part II:<br/>Applications]
B --> C[Part III:<br/>Implementation]
C --> D[Part IV:<br/>Resources]
D --> E[Part V:<br/>Future]
style A fill:#ffffff,stroke:#2563eb,stroke-width:2px,color:#334155
style B fill:#ffffff,stroke:#2563eb,stroke-width:2px,color:#334155
style C fill:#ffffff,stroke:#2563eb,stroke-width:2px,color:#334155
style D fill:#ffffff,stroke:#2563eb,stroke-width:2px,color:#334155
style E fill:#ffffff,stroke:#2563eb,stroke-width:2px,color:#334155
click A "/foundations/history.html"
click B "/applications/surveillance.html"
click C "/implementation/evaluation.html"
click D "/practical/toolkit.html"
click E "/future/emerging.html"
The Public Health AI Handbook
Evaluating AI Tools for Public Health Practice
Peer-reviewed evidence for the decisions public health leaders actually face.
Welcome to The Public Health AI Handbook
Most AI tools perform worse in the field than in the paper. 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. This is a guide to that evaluation, grounded in peer-reviewed evidence, built for the people making those decisions.
Three questions drive every chapter: 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?
The handbook covers five areas: foundations of public health AI, applications in surveillance and forecasting, implementation and evaluation frameworks, practical tools and code, and future directions.
This resource is continuously updated as new research emerges.
Important Disclaimers
This handbook is for educational and informational purposes only and does not constitute 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 officials remain solely responsible for programmatic decisions, validating AI tool outputs before public health action, ensuring regulatory compliance (HIPAA, data protection laws), and meeting professional standards in their jurisdiction.
Information may become outdated given the rapidly evolving nature of AI technology and public health practice. 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.
If You Only Have 10 Minutes
New here and want the core value fast? Follow this three-step path:
- 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 before adopting any tool.
For role-specific reading paths, see the Preface.
Explore the Handbook Series
The Physician AI Handbook
Clinical AI across every ACGME-recognized medical specialty: FDA-cleared diagnostic tools, clinical decision support, AI-assisted documentation, LLMs in clinical practice, medical liability, privacy and HIPAA, workflow integration, and evaluation frameworks. Peer-reviewed evidence from JAMA, NEJM, Lancet, and specialty journals. For physicians, health system leaders, and anyone building or deploying clinical AI.
The Biosecurity Handbook
Where AI capability meets biological risk: laboratory biosafety, the Biological Weapons Convention, dual-use research oversight, DNA synthesis screening, AI-enabled pathogen design risks, LLM information hazards, red-teaming, autonomous lab agents, and governance frameworks for AI-bio convergence. For biosecurity professionals, AI safety researchers, policymakers, and laboratory personnel.
Book Structure
- Part I: Foundations (Chapters 1–3) – AI concepts, data quality, public health history with technology
- Part II: Applications (Chapters 4–8) – Surveillance, forecasting, genomics, clinical AI, substance use
- Part III: Implementation (Chapters 9–13) – Evaluation, ethics, privacy, safety, deployment
- Part IV: Resources (Chapters 14–16) – Toolkit, project walkthroughs, AI-assisted coding
- Part V: Future (Chapters 17–22) – Emerging tech, global health, policy, misinformation, LLMs, behavioral AI
License & Citation
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to: Share, copy, redistribute, adapt, remix, and build upon this material for any purpose, including commercially, with attribution.
Full license details | CC BY 4.0 Legal Code
How to Cite
Tegomoh, B. (2025). The Public Health AI Handbook: Evaluating AI Tools for Public Health Practice. DOI: 10.5281/zenodo.18263442. URL: publichealthaihandbook.com