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Description
This book explains how artificial intelligence (AI) works and helps readers adopt, use, and evaluate AI applications in public health. It serves as an introduction to AI literacy for beginners without a prerequisite for a math or information technology background. The conceptual frameworks for AI in public health also serve well for future agentic AI design.
With the help of this textbook, instructors will be able to teach students the fundamental concepts and skills to identify unmet needs in public health and implement AI as part of their solutions. This book addresses evaluation issues for AI in public health that other books have not. The meta-AI homework with grading metrics provides metacognitive approaches for AI-human interactions.
The book features 11 chapters organized in six parts:
- Part I introduces AI and explains why we need it in public health (AI literacy courses).
- Part II explains design/evaluation models of AI applications in public health.
- Part III discusses big data in public health, which is the foundation of AI applications (data awareness courses).
- Part IV focuses on case studies of usage of AI applications in public health (e.g., EquiRisk (Equity aware risk stratifications), PMCO-AI (Personalized Motivation, Capability and Opportunity empowered by AI), and PAPO (Policy Aware Personalized Opportunities))
- Part V concerns living with AI, including education of the public health workforce.
- Part VI discusses the future of AI in public health, including challenges and opportunities.
Artificial Intelligence in Public Health is timely and essential reading for public health students and staff interested in improving their AI literacy and embracing this disruptive technology that could transform the field. It can be used as a core text for students in public health informatics BS/MS/PhD or certificate programs. It can also be used as a secondary text for students in BS/MS programs in schools of public health or students in health informatics/biomedical informatics programs with a public health concentration. Practitioners in public health agencies at different levels (federal, state, county, city, town, community) and researchers in public health informatics may also find the book useful for their work.
Part I: Introduction.- Chapter 1 Introduction.- Chapter 2 AI Components.- Part II: Modeling of AI Applications for Public Health.- Chapter 3. Design Models for AI in Public Health.- Chapter 4. Evaluation Methods for AI in Public Health.- Part III: Big Data in Public Health.- Chapter 5. Data Issues for AI.- Chapter 6 Public Health Domain Data for AI.- Part IV: Cases Studies of AI Applications for Public Health.- Chapter 7. Al Applications for Public Health Systemic Factors.- Chapter 8. Al Applications for Public Health Personal Responsibilities.- Part V: Living with AI.- Chapter 9. From Acceptance to Thinking Partner.- Chapter 10. AI and Workforce for Public Health.- Part VI: Futures.- Chapter 11. Challenges and Opportunities in the Future.
Min Wu has held a Ph.D. in medical informatics from the University of North Carolina at Chapel Hill since 2003. Now an associate professor in the Zilber College of Public Health at the University of Wisconsin, Milwaukee, his research focuses on identifying unmet public health needs and implementing technological solutions. His writing has appeared in the Journal of Medical Informatics, the Journal of Medical Systems, and Academic Radiology. Dr. Wu is a past recipient of the Best Article Award for his work in the Journal of Digital Imaging. Dr. Wu has taught health informatics graduate courses for over 20 years and gained a deep and broad understanding of biomedical informatics. Recently, Dr. Wu published review articles on Big data application on biomedical research and health care and Wearable technology application in health care and closely tracks AI's impact on public health issues to prevent disease, promote health and prolong life.
