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]
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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?
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Information may become outdated as AI tools, evidence, and public health practice change.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.
Start Here
Start with the evidence gaps, then read the core surveillance example. This path shows how the handbook evaluates AI tools before public health action.
- 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.
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
Companion Handbooks
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.
The Life Sciences AI Handbook
AI for biomedical discovery, molecular design, cellular systems, laboratory automation, and translational research. For researchers, biotechnology teams, computational biologists, physician-scientists, and students evaluating AI systems across molecules, cells, experiments, and therapeutic development.
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