The Public Health AI Handbook
A Field Guide for Evaluating AI in Public Health
What works. What doesn’t. What to ask before adoption.
Welcome to The Public Health AI Handbook
AI performance in public health surveillance and outbreak settings often falls short of controlled trial results. This handbook provides evidence-based guidance for evaluating AI tools in real-world public health practice, covering surveillance, forecasting, and outbreak response across resource-constrained settings.
Three questions drive every chapter: Which AI tools actually perform in real surveillance and outbreak settings? How do I evaluate a model when my 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. You can read sequentially or jump to the section most relevant to your work.
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Information may become outdated given the rapidly evolving nature of AI technology and public health practice.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.
Quick Start: Choose Your Path
Select the pathway that matches your role and immediate needs:
State/Local Health Departments
“I need to evaluate AI tools my agency is considering”
Start here: - AI Fundamentals - What AI actually is - Evaluation Framework - Audit vendor claims - Ethics & Privacy - Critical considerations
Epidemiologist/ Public Health Workforce
“I want to use AI in my surveillance or public health research work”
Start here: - Disease Surveillance - AI for outbreak detection - Forecasting - Predictive models - Practical Tools - Hands-on code - Using LLMs: Theory & Practice - ChatGPT, Claude, Copilot for analysis
Jump to: Case Studies for real implementations
Policymakers/ Director
“I need to make informed decisions about AI adoption”
Start here: - Global Health Perspectives - Strategic overview - Policy & Governance - Frameworks and regulations - Ethics & Fairness - Responsible AI principles - AI Misinformation - Combat health misinfo
Health Communication/ Behavioral Health
“I work in health communication, behavior change, or community outreach”
Start here: - AI Misinformation - Detect and counter health misinfo - Behavioral Interventions - AI chatbots, personalized messaging - Using LLMs: Theory & Practice - Communication assistance tools
Focus: Messaging, behavior change, combating misinformation, personalization
Frontline Healthcare Workers/ Clinicians
“I’ll be using AI tools in direct patient care or community health”
Start here: - Clinical AI Applications - Real-world use cases - Deployment & Workflow - Integration strategies - Evaluation - Assessing tool reliability
Focus: Practical usability, workflow integration, patient-facing implications
AI Developers/ Engineers/ Researchers (Technical)
“I’m building or customizing AI systems for public health”
Start here: - Data Quality & Pipelines - Public health data requirements - Practical Toolkit - Code examples and tools - AI-Assisted Coding - GitHub Copilot, Cursor, development tools - Model Evaluation - Validation frameworks
Focus: Technical limitations, data requirements, model behavior
New to AI entirely? Read Part I: Foundations sequentially.
About This Handbook
The Public Health AI Handbook is an open-source, practical guide for understanding and applying artificial intelligence in public health.
This is a field guide for:
- Epidemiologists who want to use AI tools without becoming machine learning engineers
- Public health practitioners seeking to understand AI capabilities and limitations
- Students training in public health and data science
- Policymakers making informed decisions about AI adoption in health departments
The Current State of AI in Public Health
According to the 2024 NACCHO Public Health Informatics Profile:
- Only 5% of local health departments currently use AI tools
- 84% have no plans for AI implementation
- Large health departments are 3x more likely to adopt AI than smaller ones
Meanwhile, the CDC has deployed 55 AI solutions across its programs, contributing to an estimated $3.7 million in labor cost savings.
The gap between federal capabilities and local health department readiness is where this handbook aims to help.
What Makes This Different
What you’ll get:
- Practical tools and working code
- Real case studies (successes and failures)
- Honest assessments of what AI can and cannot do
- No prerequisites beyond basic public health knowledge
- Open access forever
What you won’t get:
- Math-heavy theory without application
- Generic “AI is amazing” hype
- Ignoring messy real-world data problems
- All the answers (no one has them)
- Overpromising future capabilities
- Paywalled content
Book Structure: Your Roadmap
Part I: Foundations
Essential AI concepts, data principles, and historical context
Chapters 1-3 Start here if new to AI
Key topics: AI fundamentals, machine learning basics, data quality, public health history with technology
Part II: Current Applications
Disease surveillance, forecasting, genomics, clinical support
Chapters 4-7 Jump here for specific use cases
Key topics: Outbreak detection, predictive modeling, genomic surveillance, clinical AI
Note: Comprehensive LLM coverage in Part V (Large Language Models chapter)
Part III: Implementation and Evaluation
Evaluation, ethics, privacy, safety, deployment
Chapters 8-12 Critical for real-world implementation
Key topics: Model evaluation, ethics & fairness, privacy protection, AI safety, deployment strategies
Part IV: Practical Resources
Tools, code examples, step-by-step project guides, AI-assisted coding
Chapters 13-15 Hands-on learning
Includes: AI toolkit, first project walkthrough, AI-assisted coding and development tools (VS Code, Copilot, Cursor, Git/GitHub), Python/R code examples
Part V: The Future
Emerging technologies, global health, policy, misinformation, LLMs (theory & practice), behavioral AI
Chapters 16-21 Forward-looking perspectives
Topics: Emerging AI methods, global health applications, policy & governance, AI-generated misinformation and infodemics, Large Language Models in public health (theory, practice, privacy, validation), AI-driven behavioral interventions and personalized health coaching, research frontiers
How to use this handbook
Browse and Search
- Browse chapters in the Table of Contents
- Use the search box to find specific topics
- Click “copy” icons to copy code examples
Choose Your Path
- For practitioners → Jump to relevant applications
- For students → Read sequentially Part I → V
- For researchers → Check case study library
- For policymakers → Start with Emerging Technologies
License & Citation
© 2025 Bryan Tegomoh. All rights reserved.
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
APA Style: Tegomoh, B. (2025). The Public Health AI Handbook: A Practical Guide to Artificial Intelligence in Public Health Practice (Version 1.1) [Open-access digital handbook]. https://publichealthaihandbook.com
Getting Started
New to AI? → Begin with the Preface then AI in Healthcare: A Brief History
Want hands-on practice? → Jump to Your AI Toolkit for Public Health
Looking for specific applications? → Browse Part II: Current Applications