AI Research for Public Health (2026)

AI Research for Public Health (2026) is sponsored by Centers for Disease Control and Prevention (CDC). This program funds AI research projects that improve public health surveillance and response. It aligns with CDC's AI Strategy (FY 2026-2030) which outlines supporting public health with accelerated AI adoption, strengthening AI governance, advancing AI capabilities across data platforms, and empowering an AI-ready workforce.

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The CDC’s Public Health Data Strategy and AI Strategy are shaping CDC’s AI policy. Additionally, the IC Program (funded by the Public Infrastructure Grant) and Rural Health Transformation Program, two of the largest funding streams to modernize our public health and health care infrastructure over the last few years, may offer financial support to jurisdictions for certain AI-related projects.

The third in a series of articles exploring how law and policy are a cornerstone of public health data modernization. Other articles in the series include: Why Law and Policy Are Essential to Public Health Data Modernization , and Opportunities and Legal Considerations for Data Modernization Through the Rural Health Transformation Program AI is playing an increasingly prominent role in public health data modernization.

In some instances, the policy conversation is forward looking and reflects the public health ecosystem’s trajectory. This is apparent in the various strategy plans released by U.S. Department of Health and Human Services (HHS) and the Centers for Disease Control and Prevention (CDC); the CDC’s AI strategy being one such example.

Other future oriented examples are the projects and initiatives proposed in the CDC Implementation Center Program (IC Program) and Rural Health Transformation Program . There are also burgeoning examples of how AI is deployed in the current moment, such as in HHS’s comprehensive inventory of AI use cases .

While broadscale and sustainable funding to finance public health adoption of AI tools is limited, there are certain data modernization funding streams that jurisdictions are harnessing to enable AI adoption. This article examines AI in the context of public health data modernization, including through CDC’s Public Health Data Strategy and AI Strategy, both of which are shaping CDC’s AI policy.

It next looks at how the IC Program (funded by the Public Health Infrastructure Grant ) and Rural Health Transformation Program, two of the largest funding streams to modernize our public health and health care infrastructure over the last few years, may offer financial support to jurisdictions for certain AI-related projects.

It concludes with some reflections on the future investments needed to bolster the safe and effective use of AI as part of modernizing our public health data infrastructure. At the end of April 2026, CDC released their annual 2026 Public Health Data Strategy .

As in prior years, the strategy is organized around four goals: (1) strengthen the core of public health data, (2) accelerate access to analytic and automated solutions to support public health investigations, (3) visualize and share insights to inform public health action, and (4) advance more open and interoperable public health data. The 2026 update featured inclusion of AI in the specific metrics and objectives.

For example, under the second goal to “accelerate access to analytic and automated solutions”, the plan includes more specific objectives, including for the CDC to publish generative AI guidance and resources for State, Tribal, Local, and Territorial (STLT) health departments, establish or foster at least one public-private partnership with an AI provider to facilitate STLT access to AI technology, and leverage AI to support cross comparison of data sources in CDC’s unified data platform, 1CDP .

Just prior to the release of the Public Health Data Strategy, in March 2026, CDC released its AI Strategy for FY 2026-2030.

As with the Public Health Data Strategy, the AI Strategy is organized around four pillars: (1) support public health with accelerated AI adoption, (2) strengthen AI governance and public trust, (3) advance AI capabilities across CDC enterprise data platforms, and (4) empower an AI-ready workforce to unlock innovation. Each pillar is buttressed by a vision and strategic objectives.

The AI strategy includes objectives specific to both CDC and its workforce, such as promoting AI competency among the CDC workforce and advancing AI capabilities across CDC’s data platforms. Other objectives speak to the broader public health environment, such as publishing guidance for STLT health departments and promoting a skilled AI workforce among STLTs.

To meet these policy objectives, the CDC continues to build out its guidance to STLT health departments. In 2026, it published three AI-specific resources, Considerations for Generative AI in Public Health , Considerations for Agentic Research in Public Health , and Considerations for Disclosing Generative AI Use in Scientific Work .

It has also published information on two of its most well-established use cases, the development of a CDC chatbot and AI’s role in reducing the spread of Legionnaires’ Disease . Certainly, AI has a place in public health data modernization. Emerging use cases help illustrate both its benefits and cost-savings.

With any transition though, even a digital one, there are costs such as training staff, dedicating staff time, and procuring technology. Without policies that provide sustained funding to provide the means to enable the adoption of tools, whether AI will be transformative for health departments remains to be seen.

And while the creation of resources to build knowledge in health departments is valuable, the health departments best equipped to utilize such materials are likely to be those already most well-resourced. Although there is a growing body of informational and knowledge-building resources for health departments, the financial assistance for AI initiatives is more limited.

Nevertheless, both the IC Program and Rural Health Transformation Program may be bright spots in an otherwise very limited funding landscape. The IC Program has three priority areas, including Frontier Solutions , intended to support innovative and emerging technologies such as AI to enhance data exchange and performance.

As the IC Program ramps back up, jurisdictions that have projects with AI components that are aligned with the new priority areas and use cases (chronic disease, maternal and child health, cancer reporting, and biothreat radar detection) may have the opportunity to participate in Wave 2 . Furthermore, the Rural Health Transformation Program offers another opportunity for some states to use AI to advance rural health.

For example, Texas included the Lone Star Advanced AI and Telehealth as an initiative in its proposal with the aim of streamlining administrative tasks, detecting patients at greatest health risk, and improving service delivery.

One of Alaska’s initiatives, Spark Technology and Innovation , proposes to offer providers training and technical assistance on how to leverage AI and increase the number of hospitals and clinics using AI technology. While the CDC strategy plans offer a sense of where the agency is headed, health departments are still operating in an opaque and confusing legal environment.

There remains no comprehensive federal AI regulation, and in fact, there are efforts at the federal level to hamper state regulation. There is also a tension within federal AI policy and rhetoric, with some federal policy advocating for minimal regulation to ensure rapid AI advancement and innovation .

In contrast, the strategy from federal agencies such as the CDC is more tempered, underscoring the importance of public trust, governance, transparency and accountability . And funding, like the regulatory environment, is piecemeal. The benefits of AI may be substantial; the commensurate risks are as well.

There is a need for strong governance and guardrails that go beyond lip service, and a cohesive strategy, detailed guidance, and sustainable funding to underpin the safe and effective use of AI in public health data modernization. This post was written by Meghan Mead, J. D.

, Deputy Director, Network for Public Health Law. The Network promotes public health and health equity through non-partisan educational resources and technical assistance. These materials provided are provided solely for educational purposes and do not constitute legal advice.

The Network’s provision of these materials does not create an attorney-client relationship with you or any other person and is subject to the Network’s Disclaimer . Support for the Network is provided by the Robert Wood Johnson Foundation (RWJF). The views expressed in this post do not represent the views of (and should not be attributed to) RWJF.