Artificial intelligence is not a distant possibility in public health — it is already here. Health departments are using machine learning to predict disease outbreaks. Hospitals are deploying AI to reduce diagnostic errors. International organizations are building surveillance platforms that can detect epidemics weeks before traditional systems do. And the professionals who can bridge the gap between population health expertise and AI capability? They are in extraordinarily high demand.
This emphasis exists because the public health workforce needs people who understand both sides: the science behind community health and the tools that are reshaping how we practice it. You will not just learn about AI as a concept. You will learn how to actually use it — to analyze health data, evaluate digital interventions, navigate ethical dilemmas around algorithmic decision-making, and design strategies that make AI work for real communities, especially those who are too often left behind by technological advances.
Whether you see yourself working in a local health department, a tech company with a health mission, a global NGO, or a federal agency pushing to modernize public health infrastructure, this emphasis gives you the skills and the credibility to walk into those spaces and contribute from day one.
Unlock AI Capabilities
Gain practical skills in predictive analytics, machine learning interpretation, and digital health platforms — capabilities that most public health graduates simply do not have yet. You become the person in the room who can both ask the right population health questions and work with AI tools to find answers.
Enter New Job Markets
Health tech companies, digital health startups, consulting firms, and government agencies are all hiring people with population health backgrounds who can navigate the AI space. This emphasis positions you for roles that did not exist five years ago — and that are growing faster than almost any other field.
Expand Digital Health Impact
Telehealth, mobile health apps, electronic health records — these tools become dramatically more powerful when AI is applied well. You will learn how to increase the reach and effectiveness of digital health interventions across diverse populations, including underserved and low-resource communities.
Modernize Public Health
Public health agencies at every level — local, state, federal, and international — are under pressure to adopt AI responsibly. The same is true in the private sector and in industry. You graduate ready to contribute to that transformation, with an understanding of both the technology and the ethical, policy, and equity dimensions that come with it.
What You Will Learn
Apply AI and digital health methods to analyze public health data and interventions
You will be introduced to core principles of artificial intelligence, machine learning, and digital health applications within public health. The curriculum covers data ecosystems that include electronic health records, participatory surveillance systems, telehealth platforms, and mobile health applications. Emphasis is placed on multimodal data integration — clinical, behavioral, environmental, and social — along with data interoperability and the use of predictive analytics for disease surveillance and intervention evaluation. You will also examine data governance, privacy, security, ethics, transparency, and responsible AI adoption in real-world settings.
You will develop the ability to apply AI-based tools to process and analyze public health data, support disease monitoring, and evaluate population health interventions. You will learn to interpret outputs from predictive and classification models, identify data quality and bias concerns, and apply ethical and data stewardship frameworks — particularly in low-resource and underserved communities.
Design and implement AI-based strategies for real-world public health challenges
You will explore design thinking and systems approaches for creating and deploying AI-enabled public health solutions. The coursework covers governance frameworks for AI — transparency, explainability, and accountability — and the regulatory and policy landscape influencing digital health innovation at local, national, and global levels.
You will learn to design AI-informed public health strategies using appropriate data sources, analytics frameworks, and implementation approaches. You will be able to assess model validity and generalizability, anticipate unintended consequences, and integrate community input into technology design. You will demonstrate the ability to collaborate across disciplines to translate AI insights into actionable, equitable, and culturally grounded public health solutions.
Critically assess the social, cultural, technical, and policy implications of AI in public health
You will explore how AI shapes public health systems, public trust, and decision-making, with an emphasis on social-technical and political dimensions. Topics include data justice, digital divides, community engagement, and global perspectives on AI in public health policy and practice.
You will develop the capacity to critically evaluate societal and policy dimensions of AI in public health, assess implications of emerging technologies for reducing health disparities, and communicate evidence-based insights to policymakers, health program leaders, and community stakeholders. You will demonstrate the ability to integrate ethical, technical, and cultural perspectives when assessing the benefits and risks of AI in public health contexts.
Emphasis Courses
| GHI 463 | Foundations of Public Health Applications of Artificial Intelligence | 3 |
| PHP 465 | Leadership and Strategy for Population Health Using Generative AI | 3 |
| EPID 453 | Health Data Science Practice | 3 |
| HSD 415 | Design Visualization Practices for Health | 3 |
| HSD 420 | Makerspaces, Design Practices, and Community Impacts | 3 |
| BSM 441 | Diagnostic Technologies and Their Role in Healthcare | 3 |
| INFO 402 | Data Ethics | 3 |
| SOC 301A | Introduction to Computational Social Science | 3 |
| BIOS 452 | Health Data Analysis and Communication Methods | 3 |
| PHP 465 | Leadership and Strategy for Population Health Using Generative AI | 3 |
| GHI 463 | Foundations of Public Health Applications of Artificial Intelligence | 3 |
Note: A required course not taken as the required selection may be counted as an elective. Total emphasis units: 12 (3 required + 9 elective).