GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Reviewing Global Influenza Vaccine Literature
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To efficiently review a large volume of published literature and identify abstracts related to access to influenza vaccines.
Purpose and benefits: The AI system is expected to save significant staff time by automating the initial literature review process, allowing epidemiologists to focus on in-depth analysis of relevant publications. This increases efficiency and scalability in reviewing global literature related to vaccine access.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-10-24
US federal AI use case: HIV Data Virtual Assistant
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To improve the data retrieval and automation process for HIV researchers by providing information from datasets and generating code for data analysis, thereby alleviating challenges in finding appropriate HIV-related data.
Purpose and benefits: The AI assistant will improve productivity and research efforts by streamlining the process of finding relevant HIV datasets and generating analysis code, saving researchers time and enabling more efficient data-driven research.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Identify infrastructure supports for physical activity (e.g. sidewalks) in satellite and roadway images
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To streamline and automate the surveillance of sidewalks and other infrastructure that support physical activity, reducing the labor and cost associated with manual inspection.
Purpose and benefits: The technology has the potential to significantly minimize the effort required for cataloging sidewalks and related infrastructure, which are important for promoting physical activity. This could lead to more efficient and cost-effective surveillance, supporting public health monitoring and interventions.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-23
US federal AI use case: Immunization Information Systems Guidance Documentation Navigation and Management (IDAB EDAV Azure OpenAI Technology Use)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To provide a modernized, efficient, and user-friendly method for CDC staff to retrieve, interact with, and update Immunization Information Systems (IIS) guidance documentation, improving knowledge retrieval and supporting the creation and validation of new guidance documents.
Purpose and benefits: This AI solution enables faster, more actionable access to IIS guidance for subject matter experts, helps new employees find information more easily, and improves understanding of best practices. It streamlines the process of drafting, refining, and validating new guidance documents, increasing efficiency and accuracy in knowledge management.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: LaserAI
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To reduce screening time and improve accuracy in all phases of systematic reviews by automating title and abstract screening, PDF retrieval, full-text review, and data extraction.
Purpose and benefits: The AI is expected to streamline systematic review processes, reduce manual effort, and improve accuracy in identifying and extracting relevant data. The synthesized and graded data will inform the development of evidence-based infection prevention and control recommendations for healthcare settings.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-04-24
US federal AI use case: NewsScape
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Development of early warning indicators. News articles can form an early warning indicator of public health events and other pieces of information which can be utilized across all major domains of public health. Because of the quantity of news articles, manual efforts are impossible to gather this information.
Purpose and benefits: There are a variety of endpoints that this AI output could help support. Various teams across the CDC have expressed interest in being able to quickly get the right news content, with summaries of those articles to help support outbreak detection, report generation, surveillance and monitoring of pathogen specific news, etc. AI lets us efficiently filter and summarize thousands of news articles a day into a handful of daily "news events" that user can glean information from.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2026-01-23
US federal AI use case: Portfolio Analytics
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To automate the identification of themes within CDC-authored publications, providing richer information for science prioritization, evaluation, and communication.
Purpose and benefits: Automated theme identification will provide centers and divisions with richer, more actionable information about their publications. Combined with impact metrics, this will aid in science prioritization, evaluation, and communication, supporting more effective and efficient scientific resource allocation.
Topic area: Science
Development stage: Pilot
First used: 2026-03-24
US federal AI use case: RAPID Analysis of Policy and Program Documents (RAPID)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To streamline and automate the review and evaluation of policy and program documents, saving staff time, reducing the need for specialized training, providing consistent and complete answers, and enabling easy validation and collaboration.
Purpose and benefits: The web application will save staff time, expand capacity, provide consistent and complete answers to policy surveillance and evaluation questions, reduce intra-rater variability, and enable easy validation and collaboration on policy projects.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-09-25
US federal AI use case: SewerScout: Automated on-site sewage facility detection from aerial imagery to identify failed systems
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To automate the identification of onsite wastewater systems and detect failed systems using aerial imagery, enabling public health departments to efficiently locate and assess septic systems without resource-intensive field visits.
Purpose and benefits: The project will allow state, tribal, local, and territorial public health departments to more easily identify failing septic systems, address contamination risks, and improve disaster response by providing a ready catalog of systems. This will save time and resources compared to manual surveys, especially in rural and remote areas.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Sidekick Comms bot Offering User-friendly Tips (SCOUT)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To reduce the workload of health communicators by automating and simplifying the creation of web pages, social media posts, and other public-facing content, making information more accessible and understandable for the general public.
Purpose and benefits: The solution accelerates content creation, reduces staff burden, and increases the accessibility and clarity of CDC information for the public. All AI-generated content is reviewed by experts to ensure accuracy and quality, supporting the CDC’s mission to provide clear, science-based public health information.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-25
US federal AI use case: Transcribing Cognitive Interviews with Whisper
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Reducing the time and effort required to transcribe cognitive interviews for federal health survey research, enabling faster and higher-quality analysis of qualitative data.
Purpose and benefits: The AI system is expected to significantly reduce the hours required for qualitative review by automating transcription, enabling immediate comparison of interview concepts and answers, and providing timestamps for easier reference. This will accelerate research publication and improve the quality of survey questions used in federal surveys.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-24
US federal AI use case: Use of Natural Language Processing for Topic Modeling to Automate Review of Public Comments to Notice of Proposed Rulemaking
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual review of large volumes of public comments for Notices of Proposed Rulemaking is labor-intensive and time-consuming. The AI is intended to organize and cluster comments by theme, improving the efficiency and effectiveness of manual review and ensuring all topics are accurately reported.
Purpose and benefits: The AI system will enhance the speed and quality of manual review of public comments, enable better thematic organization, and reduce the burden on staff. This supports compliance with legal requirements for public comment review and improves the insights gained from public input.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-04-23
US federal AI use case: Short-term Forecasting of Severe Outcomes for Seasonal and Epidemic Pathogens
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Predict severe disease outcomes - such as emergency department visits or hospital admissions - over short time horizons (1-4 weeks) to improve situational awareness for planning and decision-making at the national, state, and local level. Traditional AI/ML models (e.g. time series models) are mainly used as baselines against which to test and improve more sophisticated modeling methods.
Purpose and benefits: Providing timely, accurate, and actionable forward-looking information on severe disease outcomes to government officials and the public.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2026-10-23
US federal AI use case: CDC Chatbot - Enterprise Data Assistant
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This tool is a general purpose assistant for CDC staff to ask questions of large sets of documents. Staff may take hours to find the relevant information, and this tool enables fast access powered by Retrieval Augmented Generation (RAG).
Purpose and benefits: Staff have increased access to relevant information and documents through faster and easier knowledge management.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-02-24
US federal AI use case: Leveraging AI for Metadata Tagging for Enterprise Data Catalog of CDC
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual metadata tagging for datasets in the CDC Enterprise Data Catalog is inconsistent, incomplete, and time-consuming. The AI automates and standardizes metadata tagging, improving catalog usability and reducing manual effort.
Purpose and benefits: The AI increases the speed and consistency of metadata tagging, making the data catalog more usable for CDC staff. This reduces manual effort, improves the completeness and standardization of metadata, and helps staff more efficiently find and use relevant datasets.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-06-24
US federal AI use case: Malaria parasites DNA barcode geography classification
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To complement epidemiologic investigations of domestic malaria cases by determining the geographic origin of malaria parasite strains, helping to understand how the strain entered the US.
Purpose and benefits: This AI supports epidemiological investigations by providing rapid, automated classification of malaria parasite genotypes to geographic origins. This enhances the ability to track and respond to malaria cases, especially those domestically acquired, and supports public health interventions. 
For more information, see the manuscript:?https://journals.asm.org/doi/full/10.1128/aac.01203-24
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-07-23
US federal AI use case: The School Closure Awareness System
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To efficiently and accurately identify and categorize unplanned school closures across the U.S. using publicly available social media data, replacing a costly and labor-intensive manual process.
Purpose and benefits: The AI system has saved nearly $2 million in contracting fees and reduced human work hours by 200 hours. It enables faster, more comprehensive, and more detailed capture of unplanned school closure data than the previous manual process, supporting CDC’s emergency response and reporting obligations.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2026-11-22
US federal AI use case: Using Generative AI for Stance Analysis of Public Comments on CDC’s Proposed Rules
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual review of public comments for rulemaking is labor-intensive and time-consuming due to the volume and diversity of responses. The AI system automates stance analysis and topic modeling to improve efficiency, accuracy, and insight in the review process.
Purpose and benefits: The AI system can save significant time for CDC’s public policy experts by automating the categorization and stance analysis of public comments, enabling faster and more comprehensive insight gathering for regulatory analysis. This supports compliance with legal requirements and improves the quality of public policy review.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-23
US federal AI use case: A reusable NLP pipeline for clinical narratives preprocessing and characterization
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Autocoding to Support Adverse Drug Event Surveillance
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual coding of adverse drug event reports is time-consuming and slows down the production of prevalence estimates. The AI model will automate and speed up the coding process for surveillance epidemiologists.
Purpose and benefits: The AI model will help epidemiologists quickly determine whether reported adverse drug events meet surveillance case definitions, speeding up the coding process and enabling faster, more accurate prevalence estimates for the surveillance system.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-05-24
US federal AI use case: Automating LIMS Bioinformatics Workflow Configuration and Enhancing Lab Quality Management with AI
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Configuring and customizing bioinformatics workflows in Clarity LIMS is time-consuming and requires specialized expertise. The AI tool automates this process, enabling rapid deployment and lowering the barrier for laboratory staff. It also improves access to quality management and regulatory documentation.
Purpose and benefits: The system can reduce the time required to configure Clarity LIMS workflows, enable rapid deployment during outbreaks, lower the expertise needed for workflow customization, and enhance team learning and training by providing easy access to relevant documentation and best practices.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-01-24
US federal AI use case: DGMH AI Chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Responding to inquiries is time-consuming for staff, especially when information is available but hard to find on public CDC webpages. The AI chatbot drafts initial responses using content from CDC’s website, reducing turnaround time and freeing staff to focus on higher-priority tasks.
Purpose and benefits: The chatbot will reduce turnaround time for responding to inquiries, improve consistency of responses, and allow staff to focus on other priorities. Evaluation will assess response accuracy, completeness, and revision needs, as well as consistency across similar inquiries.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-10-24
US federal AI use case: Distiller SR: AI to screen research articles for Community Guide reviews
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Screening research articles for systematic reviews is time-consuming. The AI tool will automate and speed up the process, supporting the Community Preventive Services Task Force in making timely recommendations.
Purpose and benefits: The AI tool may increase the speed of conducting systematic reviews, expediting the evaluation of public health programs for CPSTF recommendations.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-05-23
US federal AI use case: Evaluating Generative AI for polio containment.
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Respiratory Virus Response (RVR) Data Analysis Concept
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Summarizing subject matter expert (SME) interpretations and knowledge during a public health response is time-consuming. The AI aims to improve efficiency in knowledge dissemination by generating key takeaways from SME-provided information.
Purpose and benefits: The AI solution will improve the efficiency of disseminating essential information to the public, enable quicker SME review and clearance, and enhance understanding of AI limitations (e.g., bias, hallucinations) for future public health applications.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-06-24
US federal AI use case: School LLM initial abstract review process
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual review and categorization of thousands of research abstracts related to school readiness science is time-consuming. The AI enables efficient extraction and categorization of themes, reducing human effort and time.
Purpose and benefits: The AI allows for efficient categorization of thousands of abstracts in a much shorter time frame, with less human effort, and presents results in a user-friendly dashboard for health scientists to use in research and decision-making.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-08-23
US federal AI use case: CDC Chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This tool is a general purpose assistant for CDC staff powered by Large Language Models. Staff can upload documents, summarize information, extract information, create content, develop software code, or general tasks to support operational efficiency.
Purpose and benefits: Staff have used this tool to save an estimated 40,000 hours across various domains including efficiency gains with content creation, software development, and other back-of-the-house tasks within CDC. This has provided a greater than 500% ROI for the agency.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-02-24
US federal AI use case: EDAV Virtual Assistant - Eva ( Microbot Service) - Bot as a Service (BaaS)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This tools serves as a helpdesk to support CDC staff in finding relevant information related to the Enterprise Data, Analytics, and Visualization platform available across existing documentation.
Purpose and benefits: Increased access for staff to documentation and decreased time spent searching for relevant information. This includes a reduction in number of support tickets from staff.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-08-25
US federal AI use case: EDAV Azure DataFactory - Pipeline failure Analysis
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: There are over 4,000 pipelines which provide logs of their status. Manual review is highly time-consuming and error prone due to the scale of logs.
Purpose and benefits: Benefits include improved quality of summaries of logs and additional information that is available faster than traditional manual processes.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: 1CDP (SEDRIC) AIP for Advanced Foodborne Outbreak Investigation (AI Summarization and Receipt Reading)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This AI solution impacts the process of investigating foodborne disease outbreaks. These foodborne outbreaks require cooperative efforts from CDC staff, FDA, USDA, and local agencies and the AI system is used through a centralized data platform System for Enteric Disease Response, Investigation, and Coordination (also known as SEDRIC). For more information on SEDRIC, please go to our website: https://www.cdc.gov/foodborne-outbreaks/php/foodsafety/tools/index.html
Purpose and benefits: SEDRIC's AIP use case provides CDC epidemiologists the ability to accelerate their investigations of multi-state foodborne disease outbreaks by more effectively leveraging data available in a rich data source, such as receipts from grocery stores, that otherwise requires extensive time and human effort to parse through. In addition, this workflow would free up epidemiologists' time and, potentially, increase the frequency with which both CDC and STLT partners could utilize shopper card, receipts, and free text responses to support investigations. There are two main expected benefits from this use case. The first addresses manually entering receipt information, shopper card information, or other such free text field is traditionally error prone and time intensive for a variety of information. This AI system provides a human-in-the-loop opportunity to review and update data entry points while reducing time spent by staff to gain these insights. Having a set structured output as well increased standardization of this information and eases reporting in situations requiring cooperation from multiple organizations. The second benefit revolves around using the summarization capability, the extensive process of mapping common names of different food items is done automatically, greatly reducing the human labor time to generate dashboards of information regarding current foodborne investigations to serve as a decision point to aid in outbreak response.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2026-10-23
US federal AI use case: Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Investigating the similarity of infections for epidemiologic investigations of cyclosporiasis outbreaks. The method enables clustering and comparison of complex genotypes, which are too large and complex for traditional methods, to identify related infections during outbreak tracking.
Purpose and benefits: The AI enables analysis of massive genotype datasets, facilitating rapid and accurate identification of infection clusters. This supports epidemiologic investigations and traceback for cyclosporiasis and other parasites, improving outbreak response and public health interventions.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-19
US federal AI use case: MedCoder - Coding literal text cause of death information reported on death certificates to ICD-10
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Automating the coding of literal text causes of death from death certificates to ICD-10 codes, improving accuracy, efficiency, and timeliness of mortality data for public health surveillance.
Purpose and benefits: MedCoder increased the percentage of deaths that can be automatically and accurately coded from 70-75% to over 85%, resulting in substantial cost savings (hundreds of thousands of dollars) and significantly enhancing the timeliness of data for urgent public health concerns (e.g., COVID, drug overdose deaths), enabling near real-time surveillance.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-06-22
US federal AI use case: NCIRD SmartFind ChatBots - Public and Internal
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Improving efficiency and effectiveness of internal partner mailbox email management and knowledge base maintenance for program staff, and previously, providing public-facing answers to FAQs.
Purpose and benefits: The internal Knowledge-Bot’s SharePoint component helps program staff manage partner emails more efficiently and effectively, enabling shared knowledge base use across mailbox managers. The public-facing chatbots previously provided timely, agency-cleared answers to public and partner questions, supporting rapid information dissemination during the COVID-19 pandemic.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-12-24
US federal AI use case: NIOSH Industry and Occupation Computerized Coding System (NIOCCS)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Replacing manual coding of industry and occupation text with automated coding to standardized codes, reducing cost and increasing speed, accuracy, and consistency for research and analysis.
Purpose and benefits: Reduces the high cost of manual coding, promotes increased coding speed, accuracy, and consistency, and enables more efficient use of industry and occupation data for research and analysis.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-24
US federal AI use case: Nowcasting Injury Trends
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Providing real-time estimates of injury and death trends to enhance situational awareness and expedite surveillance and research activities, especially when gold standard data are delayed.
Purpose and benefits: Enables timelier identification and investigation of emerging injury trends, improving the speed and effectiveness of public health surveillance and response when gold standard data are not yet available.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: Risk Assessment Module (RAM) for the National Diabetes Prevention Program (National DPP) Operations Center.
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To assist in determining if an organization participating in the National DPP is at risk of improperly starting, going inactive, or not achieving necessary goals for continued participation in CDC’s Diabetes Prevention Recognition Program.
Purpose and benefits: The RAM module helps program managers synthesize large amounts of organization-level data to make informed decisions on assisting organizations, leading to increased program participation and improved health outcomes.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-08-24
US federal AI use case: Semi-Automated Nonresponse Detection for Surveys (SANDS)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Manual review of open-ended survey responses is labor-intensive and cost-prohibitive at scale. SANDS automates the detection of nonresponses in survey data, reducing the burden on researchers and improving data quality.
Purpose and benefits: SANDS significantly reduces manual curation time for open-ended survey responses by providing automated scoring and flagging of nonresponses. This enables faster compilation of high-quality datasets for qualitative research and streamlines the review process for researchers.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-09-22
US federal AI use case: Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To improve the accuracy and efficiency of record linkage in CDC’s National Center for Health Statistics (NCHS) Data Linkage Program, particularly for large datasets, by automating the development and selection of blocking groups and reducing manual effort.
Purpose and benefits: Increased accuracy and efficiency in data linkage 

Automation reduces manual effort and increases scalability 

Machine learning algorithms adapt and improve over time, refining linkage processes 

Enables researchers to better examine factors influencing disability, chronic disease, health care utilization, morbidity, and mortality
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-08-20
US federal AI use case: TowerScout: Automated cooling tower detection from aerial imagery for Legionnaires' Disease outbreak investigation
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Rapid identification of cooling towers that could potentially be spreading Legionella bacteria during outbreaks, enabling faster and more efficient outbreak response.
Purpose and benefits: Detects cooling towers approximately 600 times faster than manual searches. Enables more efficient and timely response during Legionella outbreaks. Improves public health response and outbreak containment.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-05-21
US federal AI use case: Assessing Large Language Models for Synthetic Survey Data Generation
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Survey data de-identification is crucial for the NCHS to maximize data utility while protecting privacy, but determining and applying modern best practices requires further research. 

NCHS conducts national surveys and releases microdata (data containing information about individuals) for public use. To protect survey participants’ confidentiality, statistical disclosure limitation techniques have been used to de-identify data, but these methods have drawbacks of losing statistical properties of the original data and thus limiting useful analyses. Additionally, these methods are not designed for very large data or text data. 

Use of synthetic data may offer another option. The goal of synthetic data is to preserve essential statistical features and variable relationships of the original data such that statistical inference based on the synthetic data is close to that of the original data. 

Large language models (LLMs) may be able to address limitations of statistical methods for synthetic data creation, especially for natural language data. We aim to advance knowledge of this application of LLMs to enable staff to select the optimal tools for synthetic data generation.
Purpose and benefits: Current statistical methods for synthetic data generation have drawbacks such as difficulty handling very large datasets, steep learning curve for people with less statistics or coding background, and inability to generate natural language data. 

Thus, if LLMs are evaluated to be successful at synthetic survey data generation, this alternative method would enable more data synthesis at scale, more data synthesis that can be conducted by staff with various levels of statistics backgrounds, and the first ever release of synthetic survey text data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of in-silico genomic and patient datasets using generative ML algorithms
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: GenAIMeta: Generative AI CDC Metadata Query Application
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Metadata plays a crucial role in enhancing public understanding and usage of CDC data. Usable metadata are essential not only for making data easy to find, understand, and use on data.cdc.gov but also for synchronizing with other federal catalogs. Metadata on data.cdc.gov spans 1,056 datasets with ~20 fields each, syncing nightly with federal catalogs. Manual validation, normalization and monitoring of this volume, and the inconsistent quality and completeness of those fields creates bottlenecks for data discovery, governance and downstream analytics. The aim is to leverage EDAV’s Azure OpenAI-powered models to automate metadata validation, standardization and monitoring at scale, replacing error-prone manual checks with real-time, AI-driven oversight.
Purpose and benefits: Objective:  
Phase 1- Automate metadata validation and monitoring on data.cdc.gov using EDAV’s Azure OpenAI API 
Phase 2 - Make useful AI agents that are user centric and make application of the tool broader with tested agent efficacy 
 
Evaluation: 
Approach: 

Asked both general and domain-trained models the same set of real-world questions. 

Benchmarked responses on four metrics: 

Accuracy: Match to ideal answers 

Relevance: Alignment with user needs 

Clarity: Readability and actionability 

Completeness: Coverage of all aspects of the question 
 

Results: 

Domain-trained models outscored general models on every metric. 

Trained models delivered more precise, context-aware, and fully-formed answers. 

General models tended toward vague or overly broad responses. 
 

Conclusion: 

Targeted, domain-specific training significantly boosts an LLM’s ability to meet specialized user requirements. 

 

Key Benefits: 

Actionable insights for better decision-making during time-sensitive scenarios. 

Optimized resource allocation for improved efficiency. 

Enhanced trust in decision-making frameworks through consistent performance.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: Detecting, evaluating, and redacting PII in NAMCS HC Component
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: How effective are open-source PII detection models in identifying and redacting PII? The National Center for Health Statistics’ (NCHS) Division of Health Care Statistics has collected millions of health records with laboratory results from encounters at health centers via the National Ambulatory Medical Care Survey (NAMCS), Health Center (HC) Component. Due to inadvertent errors during data entry and processing, some records contain identifiers (e.g. names, locations, etc.) in non-PII fields. Due to the PII, certain fields cannot be made available for restricted or public use, but reviewing millions of records for PII is not practical.
Purpose and benefits: If the process is feasible, it will significantly increase the healthcare lab data available to researchers for analysis. Additionally, the process could be applied to additional tables and years of data, increasing overall data availability.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Retrieval Augmented Generation (RAG) with Q-Bank
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Using generative AI to gain insight of older adult falls
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Nowcasting Burden and Infection Trends for Seasonal and Epidemic Pathogens
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Improve real-time estimates of disease burden and infection trends for better situational awareness for planning and decision-making at the national, state, and local level. Traditional AI/ML models (e.g. time series models) are mainly used as baselines against which to test and improve more sophisticated modeling methods.
Purpose and benefits: Providing timely, accurate, and actionable information on current and near-future disease risk and effort required for control to government officials and the public.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2026-11-23
US federal AI use case: Automating the distribution of CDC-State Department cables using AI models in the Global Health Center
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The output from the AI system is the automated and timely distribution of CDC-State Department cables. It delivers accurate, formatted messages directly to the intended recipients within seconds, ensuring fast and reliable communication without manual intervention.
Purpose and benefits: This AI use case reduces cable distribution time from 24 hours to 30 seconds, greatly speeding up communication. It saves staff time, improves accuracy, and helps the CDC respond faster to health threats, supporting its mission to protect public health effectively.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-25
US federal AI use case: DHDSP NOFO Technical Assistance Chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The Division for Heart Disease and Stroke Prevention (DHDSP) experienced numerous challenges in coordinating with grantees/recipients to support questions related to their programs. These challenges often resulted in inefficiencies such as inconsistent communication, limited accessibility to data, potential inaccuracies in responses, and delayed responses. 
To address these issues, the TA Chatbot was developed to reduce the administrative burden on staff related to their assigned Technical Assistance (TA) case load.
Purpose and benefits: The chatbot is trained on hundreds of DHDSP NOFO specific and HHS policy documents that PDSB Project Officers, PDSB Data Team, and AREB Evaluation TA Providers would have to search through to find answers to recipient questions. Use of this chatbot will save hundreds of hours of staff time so they can focus on other tasks to support DHDSP-funded recipients.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-08-24
US federal AI use case: Rapid Detection of Acute Releases of Toxic Substances (RaDARTS)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Reporting on Acute Releases of Toxic substances involves review of new media sources and is a highly labor intensive process which may involve missed content. The goal is to rapidly ingest, categorize, summarize, and store data from news media sources to inform situational awareness and surveillance of Acute Releases of Toxic Substances.
Purpose and benefits: This project aligns with the NCEH/ATSDR Strategic Framework to: Monitor and effectively respond to environmental public health hazards, emergencies, and threats that affect domestic and international health security and build appropriate capacity within state, local, territorial and tribal communities. This project will significantly reduce burden on staff, the time it takes to review data, improve the timeliness of information, and all in a cost-effective manner.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: DLS AI Assistant tool
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Regulatory laboratory compliance has become increasingly complex, requiring scientists, lab personnel, and managers to understand laboratory quality regulations like the Clinical Laboratory Improvement Amendments (CLIA). The DLS AI Assistant is a tool designed to help scientists and lab workers by providing relevant guidance, such as the internal DLS Policy and Procedures Manual (DLS PPM) and external CLIA and International Organization for Standardization (ISO) regulations for laboratory quality. It offers personalized responses based on the user's level of expertise. In the final stage of the project, we plan to add a feature that will automatically evaluate Standard Operating Procedures (SOPs) to check if they comply with all relevant documents. However, the DLS AI Assistant is meant to assist and does not dictate compliance.
Purpose and benefits: We estimate that 5-10% of all time spent within the DLS is focused on compliance efforts, such as documentation, training sessions, and audit preparations and participation. The DLS AI Assistant tool supports these quality improvement efforts by helping staff understand and follow laboratory regulations more efficiently. The goal is to streamline the review of compliance efforts without compromising quality. Additional benefits include increased harmonization and reduced time spent on evaluating edge cases. For the CDC, this tool adds another layer of checks and balances and enhances knowledge sharing, ultimately leading to better and more accurate laboratory methods.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-05-25
US federal AI use case: PDF information extraction for 889 Forms
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Document processing and extraction of text from required 889 compliance forms (PDFs and/or image files) into a searchable cloud-based database. Eliminating manual data entry and providing a more transparent log of compliance related information in a repository for users to check, verify, and review.
Purpose and benefits: The expected benefits include greater transparency regarding vendor compliance. Users have an easier way to look up and view vendor information across the center to eliminate duplicate request for vendor compliance. Users and staff will save time and potentially have less human error compared to manual data entry.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: ATSDR Toxicological Assistant chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The ATSDR Toxicological Assistant chatbot via the CDC Chatbot has access to query 180 comprehensive toxicological profiles (A-Z Index of Tox Profiles | Toxicological Profiles | ATSDR).
The chatbot can assist users in accessing toxicological data, answering questions about specific chemicals, providing information on exposure pathways, and offering guidance on health assessments related to environmental contaminants. 
This tool can significantly reduce research time, ultimately enhancing efficiency in accessing critical toxicological information.
Purpose and benefits: This tool significantly reduce research time, ultimately enhancing efficiency in accessing critical toxicological information to effectively and rapid response to general public enquire relative to chemical exposure through to ATSDR Info.
Topic area: Science
Development stage: Deployed
First used: 2026-04-25
US federal AI use case: Machine Learning with Premier Healthcare Data to inform predictive modeling of antibiotic use
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Surveillance data, including the Emerging Infections Program (EIP) and the National Healthcare Safety Network (NHSN) can be linked to  and other inpatient data to better characterize risk factors and outcomes of important healthcare associated infections (HAI) and antimicrobial resistance (AR). However, discharge data often lacks detailed information about inpatient antibiotic use.
Purpose and benefits: This project uses the Premier Healthcare Database (PHD), an electronic health database, to  predict inpatient antibiotic use and length of therapy using data readily available in claims and other electronic health record databases. This adds additional potential sources of information to support insights.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-04-25
US federal AI use case: Machine Learning Techniques for Early Detection and Situational Awareness of Rabies Outbreaks
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Rabies is enzootic in wildlife and modern-day surveillance techniques are too weak to fully capture the geographic extent of outbreaks nor early outbreak detection. Our ML algorithm uses public health surveillance data to "fill in the gaps" inherent in wildlife disease surveillance programs to accurately and rapidly detect outbreaks and deploy public health resources.
Purpose and benefits: This model is currently being using in domestic and international settings for early rabies outbreak detection. This information is shared with relevant public health authorities to initiate preventive actions which often include: public awareness campaigns/social media, deployment of vaccines for animals and people, deployment of testing reagents to bolster surveillance.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-01-25
US federal AI use case: AI-Powered web scanner for digital surveillance of rabies-related news articles
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: US travelers to international destinations are often exposed to rabies, and in some cases have died upon return to the US. Accurate and early identification of rabies outbreaks can help inform US travelers pre-travel healthcare and vaccine decisions. Unfortunately, surveillance for and transparency of rabies outbreaks in international settings is unreliable and rarely reported through official government channels.
Purpose and benefits: This scanner offers a low-resource method of scanning media for evidence of rabies outbreaks that jeopardize US traveler's health, faster and more reliably than relying on formal notifications or announcement from foreign governments.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-05-25
US federal AI use case: Pathogen strain characterization from mixed strain samples
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: We use the DNA sequences at specific places in pathogen genomes to create a "DNA fingerprint" that allows us to link cases of diarrheal illness and identify potential foodborne outbreaks. When a single patient has more than one strain of the same pathogen (e.g. two pathogenic E. coli), the pieces of the DNA fingerprint get mixed together in the sample and make the data unusable for outbreak surveillance. Our ML-based method is intended to sort the pieces of the DNA fingerprints into separate strains and make this data usable for foodborne outbreak surveillance.
Purpose and benefits: Surveillance for diarrheal foodborne outbreaks currently depends upon the availability of bacterial isolates obtained from patient stools to obtain the pathogen "genomic fingerprints" identifying pathogen strains. The availability of these isolates for fingerprinting is declining nationwide due to technological advancements that improve patient care. To maintain our ability to detect outbreaks without isolates, CDC is developing laboratory methods that obtain the pathogen genomic fingerprint directly from the patient stool specimen. However, patient stools frequently contain more than one strain of pathogen, so the ability to deploy these methods and maintain the sensitivity of foodborne outbreak surveillance is dependent upon development of this ML-based method to sort pathogen genomic fingerprint pieces from stool. Based on FoodNet data, we estimate that failure to implement these methods could lead to the loss of up to 75% of the samples currently captured by surveillance for some pathogens. Fewer surveillance samples will mean fewer outbreaks are detected and it will take longer to detect them, resulting in more people affected. For a sense of the scale of the challenge, NORS recorded ~300 outbreaks of Salmonella and E. coli in 2023 that were detected as a result of isolate-based surveillance. Economic impact evaluations have estimated that PulseNet surveillance alone prevents ~270,000 cases of foodborne illness in the US annually for a savings of at least $500,000,000 to the economy.
Topic area: Science
Development stage: Pre-deployment