GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Aivia
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Provides machine-learning-based object classification in microscopy applications
Purpose and benefits: Provides AI-based segmentation, enhancement, and prediction in microscopy applications. This tool is utilized in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Alphafold
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Prediction of atomic models from CryoEM specimens.
Purpose and benefits: This tool uses ML to build de novo atomic models for proteins based on amino acid sequence alone. This tool is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Automated approaches to analyzing scientific topics
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Slow and inefficient means by which decision makers are able to evaluate portfolios
Purpose and benefits: Assist decision makers in analyzing topics in their portfolios
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-02-23
US federal AI use case: cryoDGRN
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Prediction of protein flexibility and variability
Purpose and benefits: Software that is used to evaluate protein flexibility and variability in the dataset (https://github.com/zhonge/cryodrgn). This tool is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: crYOLO
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automated screening of CryoEM specimens
Purpose and benefits: This tool is an open-source machine learning-based particle picker (https://cryolo.readthedocs.io/en/stable/). This tool automatically picks targets based on its general model or an adapted model using small number of manually selected particles. It is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-21
US federal AI use case: CryoSPARC
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Prediction of protein flexibility and variability
Purpose and benefits: Proprietary software that is used for cryoEM data processing. Some steps in the workflow use ML to evaluate the protein flexibility and variability in the dataset (https://cryosparc.com/). This tool is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-05-22
US federal AI use case: DeepEMhancer
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automated screening of CryoEM specimens
Purpose and benefits: Open-source software that is used to obtain final "sharpened" cryoEM maps (https://github.com/rsanchezgarc/deepEMhancer). This algorithm uses a ML model to estimate the noise in the model and refine the local areas of the map and is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: Dual Use Research of Concern (DURC) Categorization LLM
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: FIJI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Provides machine-learning-based object classification in microscopy applications
Purpose and benefits: Provides AI-based segmentation, enhancement, and prediction in microscopy applications. This tool is utilized in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Identification of emerging areas
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Identification of the rate of progress across scientific fields to inform data-driven decision making
Purpose and benefits: Help decision makers to target new investments to topics with the greatest potential to accelerate scientific progress
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-02-23
US federal AI use case: Imaris
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Provides machine-learning-based object classification in microscopy applications
Purpose and benefits: Provides machine-learning-based object classification in microscopy applications. This tool is utilized in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: ModelAngelo
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Prediction of atomic models from CryoEM specimens.
Purpose and benefits: This tool uses ML to build atomic models in cryoEM maps, with or without amino acid sequence input. This tool is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: NIGMS Azure Open AI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: There are a number of situations in which administrative activities can be augmented by generative AI, especially when classification of documents is needed but no training data exist.
Purpose and benefits: We are using large language models (LLMs), in particular Open AI's models, for business process improvement.  We have used these models for visualization of a grant portfolios as well as numerous classification problems: IC prediction, clinical trial prediction, research area prediction, etc.  These models allow us to classify documents through simple prompt engineering rather than the laborious process of creating a custom training set from scratch.  These models also allow us to reduce the number of applications that humans need to review from tens of thousands of applications to mere hundreds or fewer for a number of tasks.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-09-24
US federal AI use case: Pangolin Lineage Classification of SARS-CoV-2 Genome Sequences
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Process a high volume of sequence data in real time to identify meaningful mutational patterns while minimizing the need for human effort.
Purpose and benefits: Improved user retrieval of SARS-CoV-2 genome sequences based on classification and tracking of specific lineages, including those associated with mutations that may decrease effectiveness of therapeutics or protection provided by vaccination.?
Topic area: Science
Development stage: Deployed
First used: 2026-04-21
US federal AI use case: Prediction of protein 3D structures
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Prediction of transformative breakthroughs
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Slow and inefficient identification of topics likely to produce a scientific breakthrough
Purpose and benefits: Predicting discoveries that are likely to be transformative breakthroughs in science can improve data-driven decision making
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-02-23
US federal AI use case: Ptolemy
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automated screening of CryoEM specimens
Purpose and benefits: Algorithm used to find and classify areas in low magnification CryoEM images for imaging (https://github.com/SMLC-NYSBC/ptolemy). This tool is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories.
Topic area: Science
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Research Area Tracking Tool
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Analysis staff needed assistance identifying research areas associated with individual projects.
Purpose and benefits: Within NIAID we have team that codes grants based on the research being proposed. They also prepare reports for high priority research areas. This tool was created to help them quickly identify projects that fall into a specific research field.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-20
US federal AI use case: Systematic investigation of the National Human Genome Research Institute History of Genomics and the Human Genome Project Archive
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: TB DEPOT (Tuberculosis Data Exploration Portal)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: There was a lack of deidentified, multidimensional tuberculosis socioeconomic, clinical, imaging, and pathogen genomic data available for researchers to use in developing models and learning more about TB cases.
Purpose and benefits: Provide a no-code application for users to explore and analyze multidimensional TB Portals data.
Topic area: Science
Development stage: Deployed
First used: 2026-01-19
US federal AI use case: TB Portals Outlier Detection Lambda Function
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The quality of chest X-ray (CXR) images uploaded by TB Portals Program partners varied significantly, and as the scale of images increased, NIAID needed a way to identify outliers in the imaging dataset.
Purpose and benefits: Detect potential low-quality chest X Rays to flag as potentially being unsuitable for AI/ML training and flag for quality improvement.
Topic area: Science
Development stage: Deployed
First used: 2026-01-21
US federal AI use case: Topaz
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automated screening of CryoEM specimens
Purpose and benefits: This tool is an open-source machine learning-based particle picker (https://github.com/tbepler/topaz). This tool automatically picks targets given a small number of manually selected particles. It is utilized by the Cryo-EM core in support of research projects within NIEHS DIR and DTT laboratories..
Topic area: Science
Development stage: Deployed
First used: 2026-01-21
US federal AI use case: Protein Modeling with AlphaFold
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Protein modeling
Purpose and benefits: Enormous leap forward in speed and accuracy in predicting protein folds and complexes.
Topic area: Science
Development stage: Deployed
First used: 2026-01-20
US federal AI use case: eSlate Bot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool answers questions using genAI by pulling information directly from the eSlate nomination packages.
Purpose and benefits: CSR leadership can make quick decisions about slate approval and make any further improvements to slate review process
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-25
US federal AI use case: SRO Handbook Bot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool answers Scientific Review Officer (SRO) questions by retrieving relevant information from the handbook, saving the user time looking for answers to their questions.
Purpose and benefits: The system will help SROs perform policy and handbook searches based on policy numbers and related keywords, using semantic understanding to improve search accuracy.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Detection/Identification of Reviewer Expertise and Grant Application Content
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The AI is designed to detect and identify grant application content and reviewer expertise such as pediatric or other domains to support accurate matching and more efficient, informed decision-making
Purpose and benefits: The tool is expected to enhance the agency’s mission by improving the accuracy, fairness, and efficiency of processes such as reviewer assignment and grant application analysis. By automating the detection of relevant content and expertise, it reduces manual workload, ensures better alignment between reviewers and applications, and supports more informed decision-making. This leads to more equitable and effective funding outcomes, ultimately benefiting the general public through improved support for research and programs that address critical needs.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: Detection of AI-Generated Reviewer Critiques
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool detects if a given written critique was generated by AI tools, such as large language models (LLMs), as the reviewers are not allowed to write their critiques using these AI assistants. Flagged critiques are examined by NIH staff and discussed with the reviewers.
Purpose and benefits: NIH policy mandates that reviewers produce independent analyses of grant applications based on their expertise and knowledge. The use of genAI to produce a written critique of a grant application is in violation of NIH policy and fails to provide an independent assessment of the application.
Topic area: Science
Development stage: Pilot
First used: 2026-01-20
US federal AI use case: MirBot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The AI tool addresses the challenge of efficiently and consistently processing incoming study PDFs by automatically extracting key information needed for grant review workflows.
Purpose and benefits: The AI tool helps maintain continuity in the grant review process by ensuring consistent extraction and presentation of key study information, even as experienced staff retire or transition. This reduces the training burden on new team members and preserves institutional knowledge through standardized workflows.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-10-24
US federal AI use case: Notebooks Hub
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Helping scientists and researchers code their applications, dashboards, and data analyses more quickly, have higher code standards, and gain more insight from their data
Purpose and benefits: More time can be spent on the subject matter exploration and understanding than on coding and analysis frameworks that help/support these goals. Current assessments indicate 40% speed increases to develop code and applications, with improvements increasing year-over-year
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-08-25
US federal AI use case: Ask Aithena
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Helps researchers stay on top of the latest research in their field and quickly get up to speed in new fields by providing conversational answers while also providing verifiable references for the user to read primary sources.
Purpose and benefits: Increased productivity of researchers due to more time spent doing science and less time researching about other people's science
Topic area: Science
Development stage: Deployed
First used: 2026-07-23
US federal AI use case: Writing code using AI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Limited users able to develop code.
Purpose and benefits: Assistance with novice users writing code to achieve data transformations.
Topic area: IT
Development stage: Deployed
First used: 2026-06-24
US federal AI use case: ChIRP - A ChatGPT Model for the NIH Intramural Community
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: We hoped to load text into the chatbot to assist in summarization and identification of prominent themes.  ChIRP was moderately helpful for our use case, primarily because we used it at a stage in development where documents could not be directly uploaded into the chatbot
Purpose and benefits: Chatbots with the ability to scan, summarize, and compare document text could likely assist researchers working with large qualitative datasets.
Topic area: Science
Development stage: Pilot
First used: 2026-02-25
US federal AI use case: Software Approval Agent
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: All software requests currently go to the IT Service Desk, who then need to research the approval status, respond to the end user, and forward the request to other offices in order to process, respond, and route the request to the ISSO or Administrative staff. The agent will help route software requests directly to the correct parties based on the approval status of the requested software.
Purpose and benefits: Helps staff follow standard policies & procedures thereby improving operational efficiencies.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: LibreChat
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The NHGRI-internal generative chat and image capabilities of LibreChat will provide an alternative to using publicly available chat services that may expose NIH data. Prompt data is stored within NHGRI's system boundaries and will not be used to train public models.
Purpose and benefits: It will allow NHGRI staff to utilize chat and image generative AI services using available LLMs from various CSPs through a single interface without exposing NHGRI data to train public models.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Ethics AI Agent
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Users repeatedly ask Ethics staff the same type of question, which causes challenges for Ethics staff to manage their tasks effectively.
Purpose and benefits: Helps staff follow standard policies & procedures thereby improving operational efficiencies.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Help Desk AI Assistant
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Help Desk staff need to write new responses to IT service desk requests and inquiries. The agent will provide Help Desk staff with ideal language when responding to tickets.
Purpose and benefits: Help IT staff communicate with end user in a clear, consistent message to improve customer service quality and efficiencies.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Data Management and Sharing Plans - Assistant Review Tool
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool uses NLP to identify the areas in the grant application that deal with the Data Management and Sharing Plan (DMSP). It is designed to assist the reviewer in determining whether the application meets the NIH Data Management and Sharing requirement.
Purpose and benefits: This tool improves productivity and efficiency by streamlining the process and pre-processing the DMS plan against a checklist. It assists the reviewer in their task. The reviewer makes the final determination based on the results and the text of the application.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: ITAC SOP Chat
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The AI provides natural language searching of ITAC SOP, policy, and as-built documents allowing users to easily locate knowledge and easily view the document section that includes the knowledge.
Purpose and benefits: The AI will increase operational efficiency by allowing ITAC users to more easily search and locate knowledge from SOP, policy, and As-Built documents.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: BDC Website Search
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The AI provides natural language searching of BDC website information, making it easier for users to find relevant information.
Purpose and benefits: The AI will provide a more user friendly and efficient information search system.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: BioData  Catalyst Harmonized Data Model
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Harmonizing complex scientific concepts in retrospectively collected data.
Purpose and benefits: The AI will help lower the barrier to use and improve the quality of biomedical data for research to improve public health outcomes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NHLBI Chat Workflow - Data Management and Sharing Plan
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Assists program officers in reviewing grant applications for a specific query in the checklist (DMSP).
Purpose and benefits: Improved efficiency in workflow for Program officers
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: NHLBI Chat Workflow - Foreign Component
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Assists program officers in reviewing grant applications for a specific query in the checklist (Foreign Component).
Purpose and benefits: Improved efficiency in workflow for Program officers
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: Merops
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automate and expedite copyediting of scientific manuscripts
Purpose and benefits: More efficient and cost-effective source of copy-editing manuscripts.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: OSPIDA RPAB Scientific Coding Assistance Tool (CAT)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: OSPIDA's Referral Program and Analysis Branch (RPAB) is responsible for scientific coding assignments of grant applications. They assign scientific codes to grants based on different objectives, selecting from a list of over 3,000 scientific codes related to NIAID's research areas.
Purpose and benefits: RPAB's Scientific CAT will significantly save time spent on the initial manual assignment of scientific codes and promote consistency in coding across applications.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: AWS Exscribo
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Typical transcription services struggle to handle scientific and biomedical terminology, and many platforms do not include the ability to pretrain for specific words, or offer the ability to perform queries on that text in the transcription system itself.
Purpose and benefits: The AWS Exscribo application is specifically built to allow for custom vocabularies, editing of meeting transcriptions, and model retraining, making it well-suited to handle complex medical terminologies. It also has the benefit of using AWS Bedrock, which enables Gen AI prompting on the transcription.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: Virtual Assistant for the NIAMS Grant Management Applications
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: New investigators and staff often struggle to locate policies, workflows, and troubleshooting steps buried across SharePoint guides, SOP documents, and in?app help screens for the two NIAMS Grants Management applications. In addition, support tickets and emails consume significant SME staff's time.
Purpose and benefits: Streamlined Application Support Operations: The AI-powered virtual assistant can handle user questions and inquiries, reducing the burden and support time spent by grant SMEs and the IT application development team. It provides self-service options for users to resolve issues independently.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Grant award process efficiency
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Reducing time burden of repetitive tasks related to the grants award process
Purpose and benefits: Improvement of operational efficiency for making grant awards
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: Coding translation
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Translates code between programming languages, e.g. rewrite python script in equivalent R commands; translate natural language to code, e.g. write a python script that will convert this gene matrix to a transcript matrix etc.
Purpose and benefits: Saves time in troubleshooting programming code or rewriting code in a different language
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Assist in research on the association between hearing loss and dementia for the NIDCD EARssentials hot topic presentation
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Assist in deepening my understanding the statistical methodologies applied in research examining the association between hearing loss and dementia.
Purpose and benefits: Improving understanding of statistical methods in population research of hearing loss and dementia.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI-generated podcast describing recent advances in methods for analysis of RNA-seq data
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Genomic analysis is a rapidly evolving field with advances in analytical methods arising frequently. This makes it difficult to stay on top of new techniques.
Purpose and benefits: Improved understanding of new genomic analytical methods
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced Journal Clubs
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: A lack of engagement, efficiency, and productivity at journal clubs due to the burden of gathering topically relevant and high quality journal articles for review.
Purpose and benefits: Make journal clubs more engaging and informative with highly relevant articles recommended by the AI.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Grants Portfolio Analysis
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Facilitate scientific program management and grant portfolio assessment
Purpose and benefits: Better monitoring of grants for alignment with current policies and regulations.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Identifying pain vs non-pain research
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Identifying pain research studies is complicated because of the ubiquity of pain in language. NIH program staff working in the pain research field need to be able to distinguish pain research from opioid research, both of which get classified as "Pain Research" by RCDC. This usually requires significant investment of staff time, however, since a large enough training dataset has been developed we are trying to train various ML algorithms to be able to identify grants that are truly researching pain compared to grants that merely mention pain in their text.
Purpose and benefits: It is expected that this will decrease the amount of staff time needed to curate a portfolio as starting point of analyses and that it will speed up the time it takes to carryout an analysis.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: HEAL Portfolio Topic Analysis
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The HEAL Initiative supports hundreds of pain research studies. It is meant to support pain research that cannot be carried out by an individual IC. One challenge we have is describing what HEAL research is. This project is an attempt to give program staff a starting point and visualization that describes the topics covered by HEAL research so that staff can start to understand the HEAL portfolio and how the different programs in HEAL are related or different. It will also allow staff to be able to explain how the HEAL portfolio is different from portfolios of different ICs. It may be possible for staff to carry out this analysis but using a ML algorithm would require a fraction of the staff time needed for this and be more consistent. While it may be possible for staff to classify the topics in the relatively small HEAL portfolio without AI, having a ML method in place will allow a single staff member to compare the HEAL portfolio to the much larger NIH portfolio using consistent methods.
Purpose and benefits: This will allow us to describe various portfolios that consist of 100s or 1000s of grants without requiring the time of dozens of staff members. The output will allow staff members to communicate summaries of their porfolios consistently and clearly.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Determining how federal pain resarch has responded to the Federal Pain Research Strategy.
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The IPRCC has asked NIH to evaluate if all FPRS priorities are being addressed by NIH research. The task would require staff to categorize over 6000 grants into up to 13 FPRS priority areas. A single staff member can curate 5 grants per hour using these parameters, and agreement among staff members is approximately 30%. Therefore, we are carrying out this project using ML to be able to complete the project in a timely manner and without the need of significant staff time investment. It will identify areas of research that require staff to investigate further, instead of having staff curate the whole portfolio.
Purpose and benefits: Allow staff to complete the analysis requested by the IPRCC without the need for grant by grant curation of the entire Federal Pain Research Portfolio
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Transformer-Based Metadata Alignment Workflow
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Inconsistent data elements and differing definitions in glossaries of metadata structures across research data ecosystems hinder interoperability and FAIR-aligned reuse.
Purpose and benefits: Speed improvements in metadata harmonization across ecosystems, enabling more discoverable, reusable, and interoperable datasets to support secondary research, cross-program analysis, and interdisciplinary biomedical discovery. Enhances readiness for large-scale AI/ML applications by providing scalable semantic alignment capabilities and strengthening metadata infrastructure.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: LLM-Assisted Referral Justification Email Generator
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Referral justifications are often time-consuming to draft manually and require consistent interpretation of referral guidelines and application content.
Purpose and benefits: Reduces Program Officer workload by generating structured draft justifications grounded in referral guidelines. Promotes consistency, transparency, and standardization in referral workflows while improving efficiency and decision traceability. Supports broader adoption of AI in operational decision support with human-in-the-loop oversight.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI Assist
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This solution is designed to address the challenges of manually processing and analyzing large volumes of unstructured data—including documents, reports, and policies—which is often time-consuming, labor-intensive, and prone to human error.
Purpose and benefits: The implementation of an AI-powered analytics solution will streamline data processing, reduce human error, and enable faster, more accurate decision-making at NINDS. This will enhance operational efficiency, strengthen compliance, and allow staff to focus on higher-value tasks.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Portfolio analysis and grant summarizing program
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Extracts specific information from hundreds of grant applications in a consistent reproducable way
Purpose and benefits: This AI program automates the process of analyzing text from a few to many hundreds of grants using a custom prompt and an Azure OpenAI models
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: RAG System For Travel Planning
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The chatbot answers questions from the users based on the user guide and potentially categorizing the IT helpdesk tickets.
Purpose and benefits: Instant answers to common travel questions and reduce hours spent manually searching through the user guide
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Generating Metadata for Web Archive Resources
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Time-intensive process needed to generate accurate and descriptive metadata about web resources
Purpose and benefits: More consistent, reliable, accurate and publicly available information about resources
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Incorporating External Information into Taxonomy
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Time consuming and inefficient process of reading journal articles and manually copying new organism names from those articles into internal databases
Purpose and benefits: Reduced time spent manually reading journal articles to find novel organism names and manually entering those names into internal database
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using Llama to summarize PubMed Central (PMC) full text articles that contain information on protein function
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Inefficient and manual process to assign accurate function to proteins and protein models
Purpose and benefits: This will increase efficiency of curators’ work in providing up-to-date functional annotation for prokaryotic protein family models for use in the annotation pipeline and in adding this to RefSeq proteins.
Topic area: Science
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: Leveraging NLP and LLMs to identify and characterize NIH prevention research via 160-topic taxonomy
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Semi-automate manual curation
Purpose and benefits: More timely assessments of NIH spending in specific areas within prevention research.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: Driving Efficiency and Expansion of Dietary Supplement Label Database Data through AI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Enhance database QA and enhance database records acquisition rate
Purpose and benefits: More rapid sourcing of dietary supplement label data and enhanced quality assurance of database records
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI-enabled User Support and Impact Monitoring
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: User support for DSLD data inquiries and monitoring of DSLD usage and impact in the research community.
Purpose and benefits: Support AI adoption in the federal workforce, offer new ways to address the complex analytical needs of DSLD’s “super-user” community by allowing for a deeper exploration of the data than the standard DSLD web interface can provide, and provides the opportunity to evaluate the AI tool and to make incremental improvements by fine-tuning the model and interface.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Section 508 Azure OpenAI Chatbot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool will streamline access to compliance resources, guidance, and support, empowering NIH staff and stakeholders to meet federal accessibility requirements. Designed to efficiently handle help desk inquiries, the chatbot will adhere to NIH’s security protocols and provide accurate, role-specific information.
Purpose and benefits: This tool will streamline access to compliance resources, guidance, and support, empowering NIH staff and stakeholders to meet federal accessibility requirements. Designed to efficiently handle help desk inquiries, the chatbot will adhere to NIH’s security protocols and provide accurate, role-specific information.
Topic area: IT
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: Enhancing the RCDC with Generative AI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The goal is to assess whether generative AI can enhance the RCDC process by minimizing time-consuming, resource-intensive routine and manual tasks and improving overall efficiency.
Purpose and benefits: Gen AI is expected to save time, reduce manual workload, and improve productivity by automating resource-intensive processes. These efficiencies support the agency's mission by enabling better resource allocation and enhanced categorization of NIH research.
Topic area: Science
Development stage: Deployed
First used: 2026-05-24
US federal AI use case: NIH Travel Policy AI Chatbot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The AI-powered chatbot is designed to address a key operational challenge: the high volume of routine travel policy inquiries directed toward NIH administrative staff. These questions, often repetitive and time-consuming, divert valuable human resources from more complex and strategic responsibilities.Specifically, the AI chatbot leverages Generative Artificial Intelligence (AI) to provide accurate, consistent, and real-time responses to questions related to the Federal Travel Regulation (FTR) and the NIH Travel Policy Handbook. By doing so, it significantly reduces the dependency on staff to manually research and craft responses to standard inquiries. In addition, the chatbot serves as the foundation for a self-service portal for the NIH community. This portal empowers employees and stakeholders to independently access authoritative travel policy guidance 24/7—improving efficiency, enhancing user experience, and ensuring policy compliance across the organization.
Purpose and benefits: The Office of Financial Management (OFM) envisions this AI-powered chatbot as a critical support tool for NIH staff during the upcoming transition to a self-service travel planning model, aligned with the government-wide shift to GSA’s ETS Next travel system, recently named GO.gov. With this transition slated to begin in 2026, the chatbot will serve as an intelligent, always-available assistant that simplifies the travel planning process for thousands of NIH employees. Enhanced Operational Efficiency.Improved User Experience for NIH Staff.Support for a Modern, Self-Service Government.Increased Compliance and Accuracy.Indirect cost benefit to public. Up to 60–80% reduction in inquiry volume handled by human agents. 160 FTE hours saved per month at the central NIH Travel office. 1000 FTE hours saved per month across the IC Community Travel offices. Drastic reductions in average response times, often from days or hours down to seconds.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: PowerAutomate Delinquent Submissions
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Senior Auditors are required to spend significant time—approximately 25% of an FTE per auditor annually—on manually monitoring award recipients’ submission status, contacting recipients, providing oversight, and documenting compliance at multiple intervals leading up to the fiscal year end.
Purpose and benefits: By automating these processes, the AI will enable auditors to redirect roughly 25% of their time from low-value administrative tasks to high-skill activities that generate measurable impact, increasing efficiency, strengthening oversight, and sustaining our division’s annual ROI of approximately 600%. This will ultimately return more funds to NIH from award recipients, directly supporting the agency’s mission and benefiting the public through more effective use of federal resources.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: ABE (AI-driven. Beneficial. Efficient.)
Agency: Department of Health and Human Services - HHS/OASH/OIDP
Problem addressed: By centralizing federal HIV program information in a custom Knowledge Base, ABE addresses the challenges of fragmented data and inefficient workflows, supporting operational efficiencies through a reduction in labor force and contracts, and providing the ability to quickly access, summarize, analyze, and create resources.
Purpose and benefits: ABE is expected to deliver key benefits by streamlining operations, reducing costs, and improving data-driven decision-making related to HIV/AIDS programs. ABE will serve as a mechanism to expose other federal partners to the usage of AI within a protected knowledge base, increasing work output efficiencies and fostering federal partnerships through expanded Knowledge Base assets and usage.
Topic area: IT
Development stage: Pilot
First used: 2026-01-24
US federal AI use case: AI Harvest Service
Agency: Department of Health and Human Services - HHS/OCIO
Problem addressed: The Harvest Service addresses inconsistent or incomplete metadata across distributed HHS open data sources by normalizing descriptions, creating concise summaries, and tagging data assets for improved searchability
Purpose and benefits: Enhanced discoverability of HHS  data from distributed agency-owned systems; improved user accessibility of health- and human-related data; faster identification of relevant data assets for researchers, policymakers, and the public; and increased value of open data investments.
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Federal Assistant AI Agent
Agency: Department of Health and Human Services - HHS/OCIO
Problem addressed: HHS and its Family of Agencies have a diverse, spread out basis of content that can be confusing to navigate and requires a user to have knowledge of HHS org structure to be able to obtain answers to their questions; leveraging Agentic AI using Turnkey app can reduce burden on citizens to find answers to their questions about HHS' services, and can reduce the volume of calls to HHS agency contact center representatives to answer common questions
Purpose and benefits: Allows end-users to leverage a single point of entry and plain language to find information across all HHS and its family of agency websites, allowing only authoritative government sources, to provide answers to questions in a conversational manner without requiring the user to be able to navigate and understand a complex web of content and bureaucracy.  Reduction in costs for contact centers via lower call volume and decreased time spent per calls.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: ChatGPT
Agency: Department of Health and Human Services - HHS/OCR
Problem addressed: Staffing shortage
Purpose and benefits: More efficient investigations.
Topic area: Law Enforcement
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: CoPilot Outlook
Agency: Department of Health and Human Services - HHS/OCR
Problem addressed: Staffing shortage
Purpose and benefits: Faster correspondence with public.
Topic area: Law Enforcement
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: Similar Opportunities (KNN)
Agency: Department of Health and Human Services - HHS/OS/ASFR
Problem addressed: Agencies do not receive the highest number of proposals from capable applicants as applications generally tend to come from the same set of applicants.
Purpose and benefits: Increase the quality and capability of applicants submitting grant proposals to federal agencies.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: Applicant Help Chatbot
Agency: Department of Health and Human Services - HHS/OS/ASFR
Problem addressed: Answer user questions about Grants.gov
Purpose and benefits: Faster answers to questions at a lower cost
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-05-19
US federal AI use case: DECIDE
Agency: Department of Health and Human Services - HHS/SAMHSA
Development stage: Retired
US federal AI use case: Document drafting and editing
Agency: Department of Health and Human Services - HHS/SAMHSA
Development stage: Retired
US federal AI use case: Document summarization
Agency: Department of Health and Human Services - HHS/SAMHSA
Development stage: Retired
US federal AI use case: AWS Kendra Search tool
Agency: Department of Health and Human Services - HHS/SAMHSA
Problem addressed: Kendra address the technical limitations of efficient access to public information.
Purpose and benefits: The SAMHSA STORE houses a tremendous wealth of mission-critical information for public consumption and utilization. Kendra provides an efficient and comprehensive way for the public to access this information.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-06-25
US federal AI use case: Duplicate Identification Process
Agency: Social Security Administration - Chief Information Officer
Problem addressed: This AI use case identifies, flags, and marks duplicates, reducing the amount of time spent to review cases for hearings.
Purpose and benefits: The use case improves efficiency and accuracy of disability determinations/decisions.
Topic area: Benefits Processing
Development stage: Deployed
First used: 2019-09-01
US federal AI use case: Handwriting Recognition of Forms; Intelligent Document Processing Platform - Forms Classification and Data Extraction
Agency: Social Security Administration - Chief Information Officer
Problem addressed: This AI use case classify, identify and extract structured, semi-structured forms containing printed and handwritten fields. The use case increases automation, reduces clerical errors and reduces customer wait times.
Purpose and benefits: The use case improves efficiency and accuracy
Topic area: Benefits Processing
Development stage: Deployed
First used: 2021-01-02
US federal AI use case: PolicyNet Plain Language Search Engine
Agency: Social Security Administration - Chief Information Officer
Problem addressed: The system is designed to help users quickly find relevant information, improving efficiency and customer service. Ongoing enhancements and data analysis support more accurate and effective search results.
Purpose and benefits: The system streamlines access to information for staff, enabling more efficient and accurate decision-making. This leads to improved service delivery and significant time savings, benefiting both agency operations and the public.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-09-21