GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Machine Learning as a Service: Extract data from product labels, business forms, and image files
Agency: Department of Health and Human Services - HHS/FDA/ODT
Problem addressed: This AI project is designed to assist in capturing data submitted to & reviewed by the FDA by extracting text and data, determining its structure, and saving the information in a more useful format
Purpose and benefits: The ability to extract information such as nutrition information and ingredients from product labels, tabular data, invoices and receipts, and handwritten forms without the need to have users retype or copy & paste the data into FDA applications.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: Filer Evaluation prioritization using risk-based decision Machine Learning approach
Agency: Department of Health and Human Services - HHS/FDA/OII
Problem addressed: The FDA's Office of Information Operations (OIO) has an opportunity to enhance its evaluation capabilities across over 4,000 filers in the current FDA inventory by implementing a systematic, data-driven approach to risk assessment and prioritization. By developing standardized evaluation processes and integrated analytical tools, OIO can optimize resource allocation, improve consistency in risk identification, and strengthen the FDA's capacity to effectively protect public health through targeted regulatory oversight.
Purpose and benefits: OIO is responsible for filer evaluations. There are over 4,000 filers in the current FDA inventory and this ML-based risk scoring approach to identify high-risk filers reduces the burden of sorting through the information manually and provides a standard process for conducting evaluations for the staff. An interactive dashboard has been developed that displays model outputs in various forms for staff use.
Topic area: IT
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Electronic translational services for regulatory documents for articles offered for import
Agency: Department of Health and Human Services - HHS/FDA/OII
Problem addressed: FDA' Office of Information Operations (OIO) seeks to implement automated translation capabilities for foreign language documents essential to import operations. By integrating translation services directly into OIO staff workflows, the Agency can improve import screening to better serve stakeholders while maximizing staff capacity for core public health protection activities.
Purpose and benefits: The implemented solution automated the electronic translation of inspection/investigation and import documents, labels, industry guidance, materials related to policy and regulatory, presentations and educational records. This automation significantly reduced time spent by staff to translate documents, reduced the need to find a translator to read and understand foreign language documents, increased reliability and timeliness for enforcement actions, increased destruction of misbranded FDA regulated products at the IMFs, and increased the ability to provide regulatory materials in foreign languages.
Topic area: IT
Development stage: Deployed
First used: 2026-04-24
US federal AI use case: AI-Powered Video Analytics for Law Enforcement
Agency: Department of Health and Human Services - HHS/FDA/OII
Problem addressed: The AI is intended to solve the challenge of manually reviewing large volumes of surveillance video, which is time-consuming, labor-intensive, and prone to human error.
Purpose and benefits: Faster identification of persons, vehicles, and events of interest through AI-powered video search and filtering.
Improved accuracy and objectivity in surveillance review and analysis.
Increased situational awareness via real-time alerting and behavior detection.
Greater operational efficiency, enabling limited staff to manage larger video workloads.
Data-driven decision-making supported by trend analysis and visual dashboards.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI is used to filter relevant video, but outputs are verified by humans, decisions/actions are performed by humans
First used: 2026-01-23
US federal AI use case: Computer Vision to translate and mine Product Labeling photos to analyze labeling for potential violations
Agency: Department of Health and Human Services - HHS/FDA/OII
Problem addressed: The intent is to reduce the amount of time import operations users spend reviewing product labeling of imported products for violations.
Purpose and benefits: Reduce time to spot violations on imported products increasing efficiency of reviews.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The Computer Vision (CV) project aims to automate label extraction (identification of label information) for regulatory compliance, reducing manual effort and improving accuracy in detecting label discrepancies.
US federal AI use case: Intelligent Document Processing to analyze current import entry documentation for potential discrepancies
Agency: Department of Health and Human Services - HHS/FDA/OII
Problem addressed: The intent is to assist in the process of discrepancy identification between documentation submitted by trade and CBP line data submitted.
Purpose and benefits: By streamlining the manual document review process for standard entry documentation from trade, we can significantly reduce the time required for each line review, freeing up substantial analyst capacity to focus on high-risk shipments that pose greater threats to public health and safety. This efficiency improvement enables better resource allocation, reduces processing bottlenecks, and supports faster clearance times for compliant shipments while maintaining robust oversight. The enhanced operational efficiency directly supports FDA's core mission by enabling more targeted, risk-based resource deployment and improving both trade facilitation and import safety program integrity.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: AI Audit Resolution Assistant (AIARA)
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The DFI PRF AIARA project leverages AI to streamline the single audit process.  The team utilizes the Robotic Process Automation (RPA) to extract data from the Federal Audit Clearinghouse to create templates for both the audit notification and management decision letters, and to generate a comprehensive report of the auditee and its findings.
Purpose and benefits: Since its launch, the AIARA has successfully processed and resolved 73 audits. Automation has resulted in an estimated total of 276 hours of work saved. The AIARA has significantly enhanced both efficiency and consistency in the audit resolution process.
Topic area: Other
Development stage: Pilot
First used: 2026-07-24
US federal AI use case: Knowledge Navigator
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The objective is to develop an AI model that can answer detailed and complex questions about the key programmatic document, Application and Program Guidance (APG), that is issued annually before the application cycle opens. The PoC LLM has Applicant and Program Guidance (APG) documents for 10 loan repayments and scholarship programs.
Purpose and benefits: This will allow loan repayment and scholarship analysts and call center agents to  better respond to public inquiries from applicants and participants
Topic area: IT
Development stage: Pilot
First used: 2026-06-24
US federal AI use case: Medical records summarization
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The AI will reduce the amount of time required to review and evaluate a claim.
Purpose and benefits: The AI will take medical records number in the thousand and produce a summary document of key element to conduct a claims review.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: NOFO Compliance Assistant
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: HRSA leadership would benefit from an automated solution to evaluate HRSA Notice of Funding Opportunities (NOFO) against dynamically-changing Executive Orders (EO) and OMB memos to ensure NOFO compliance with White House priorities.
Purpose and benefits: This solution will enhance operational efficiency by reducing document creation time from weeks to days while ensuring more consistent, error-free NOFO's through automated quality assurance.  The solution is intended to provide more consistent, readable NOFOs and reduce barriers for smaller organizations.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: PRF Program Chatbot
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Answer the inquiries related to GAO, FOIAs, and Litigation quickly and efficiently
Purpose and benefits: PRB leadership is equipped to manage the PRF program effectively, by responding to adhoc inquiries that enhances the customer experience while adapting to current reductions in staff
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-02-25
US federal AI use case: Scholar Match
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The current candidate evaluations and placement process is complex and challenging for both analysts and participants. By enhancing this process with AI/ML support could optimize resource allocation and candidate satisfaction, significantly impacting workforce distribution and efficiency in critical health areas.
Purpose and benefits: This would improve the process of matching NHSC and Nurse Corps Scholars going into clinical service in underserved communities.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Scholarship Insight
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The NHSC and Nurse Corps Scholarship reviewers need to receive and score thousands of essays submitted and the manual process delays the application processes. We are looking forward to building a Generative AI program that looks at all of the previous essays and how the scoring rubric was done and train it to do the first cut at scoring the essays. This could enhance the fairness and efficiency of scholarship evaluations, ensuring a thorough review process that supports equitable student opportunities.
Purpose and benefits: Reduce the amount of time required for internal or external reviews to evaluated NHSC and Nurse Corps scholarship applications and ensure that human reviewers are following the appropriate scoring rubric.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Site Application Analysis
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The current application review process requires internal analysis to review many complex documents to determine Site Eligibility to employ a member of the NHSC or Nurse Corps. By enhancing this process with AI/ML support could revolutionize and streamline the NHSC, Substance Use Disorder Treatment and Recovery (STAR)and Nurse Corps Site Application Process.
Purpose and benefits: This would improve the current highly manual process for reviewing Site applications for internal analysts but ensure that clinical sites are being approved for NHSC, Nurse Corps, and STAR LRP.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: RefineAI - Enhanced Summary Statements
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: DIR would benefit from an automated system that would correct or solve grammatical issues, redundant comments, and acronym expansion at the merging stage within ARM to increase efficiency in panels and devote more time to discussion with hopes of more useful feedback without the pressure of time constraints due to editing. At least ten percent of overall ORC panel time is devoted to editing in real time for grammatical and duplicative issues. On average, discussion of an application takes 60 minutes; if we save 6 minutes of editing time per application, we can lower hourly rates for contractor support and lower hourly rates for editing post panel. In addition, we can shift focus in panel to content-specific discussion promoting higher quality feedback.
Purpose and benefits: The expected benefits of the automated solution are:
- Increasing efficiency during ORC panel discussions, ultimately leading to lower contractor support and ORC costs, by providing a merged summary statement that highlights duplicate comments, expands acronyms, and proposes grammatical fixes at the merging state to devote more time to content-specific feedback in panel discussions
- Increasing quality of feedback to applicants
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: FOIA Exemption-Aligned Redaction
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: The FOIA staff will leverage this solution to review records in response to FOIA requests, search the internet for publicly available information (names, email addresses, contact information of federal and non-federal individuals), and identify personally identifiable information (PII). The goal is to reduce the burden on the HRSA FOIA staff and process the requests quickly. Automating the manual process will save analysts time and effort and will help reduce/prevent human error. The FOIA staff reviews between 35-50 pages per hour, totaling 600 hours per month.
Purpose and benefits: The expected benefits of the automated solution are:
- Reducing the manual effort and the time that the FOIA staff spends on converting non-PDF electronic records to PDF
- Identifying information to withhold/redact under one or more FOIA exemptions
- Proposing the redaction markings for human QA
- Reducing the size of the PDF
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: HRSA Data Warehouse ChatBot
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: HRSA Data Warehouse Chatbot to respond to public Inquiries.
Purpose and benefits: Increase transparency by making health data more accessible, actionable, and equitable — enabling faster insights, smarter decisions, and broader community engagement
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Code Conversion for PowerBI Migration
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Generate code to support migration and conversion from Tableau to PowerBI to save license cost as well as migration labor costs
Purpose and benefits: Save cost for development of the solution including eliminating contract labour costs
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: HRSA Fact Sheets
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Generate HRSA’s Program Fact Sheets for public use and reduce the cycle time by eliminating manual review and validation process.
Purpose and benefits: Public can immediately find clear, authoritative facts about HRSA programs, funding, workforce, outcomes. Also Strengthens grant proposals, community outreach, and health planning based on accurate, timely information
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Program Reporting  System Knowledge base Chatbot
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Help users get relevant information quickly instead of in lengthy manuals, FAQs, or scattered documentation and reduce support cost.
Purpose and benefits: Provides instant, accurate answers to user queries, reducing the time spent searching for information.  Additionally, it reduces call center support costs and minimizes the number of calls from grantees seeking system help, improving operational efficiency.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Program Reporting  System  Natural Language Search
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Simplified Information availability for faster decision making and eliminate the maintenance cost for the legacy SOLRs search server.
Purpose and benefits: Improved efficiency and saves time by presenting information in a simple, natural language format, helping grant program officers make faster, informed decisions and enhancing overall grant performance monitoring.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI identification of "High-Risk" Health Centers
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Manual identification of high-risk health centers is resource-intensive and may miss key indicators across large datasets. Would support a proactive, data-driven approach to inform site visit schedules and technical assistance planning.
Purpose and benefits: AI-driven risk identification would allow BPHC to better allocate resources, prioritize site visits, and provide tailored technical assistance. This will help improve compliance, operational performance, and ultimately the quality of care delivered by health centers
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Targeted Technical Assistance
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: AI will help identify the health centers/ geographic areas that will benefit the most from targeted technical assistance on clinical topics for improving quality metrics
Purpose and benefits: This will lead to better quality of care and improve health outcomes for patients. At the same time this will reduce costs and staff burnout and increase patient satisfaction with their care
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Program Compliance and Reporting Knowledge Base Chatbot
Agency: Department of Health and Human Services - HHS/HRSA
Problem addressed: Users often need to navigate multiple technical assistance webpages, manuals, documents, FAQs, or submit inquiries through the contact form to find answers. This can be time-consuming and increases time and support capacity.
Purpose and benefits: Provides instant, accurate answers to user inquiries, reducing the time spent searching for information. It would also reduce call center and staff support costs and increase capacity to assist with more complex issues by minimizing calls and inquiries from grantees seeking publicly available information, improving operational efficiency.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: NICHD RPAB AI/ML NICHD Relevance Model
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications.
Purpose and benefits: This AI use case increases the efficiency of the grant referral process and ensures difficult applications are triaged in a quicker manner.
Topic area: Admin Functions
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: The primary objective is to improve the efficiency, accuracy, and consistency of grant application referral assignments while streamlining the internal process for referring new applications. The AI-generated output supports subject matter experts by providing additional information that helps them make faster decisions and prioritize applications for review. All AI output is used solely as an assistive tool, and every referral decision undergoes 100% human review. Therefore, this use case does not meet the definition of a high-impact AI.
First used: 2026-04-25
US federal AI use case: Applying for Grants Chat Bot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Save time for applicants by guiding users through the application process step-by-step, recommending additional resources for grant writing, and helping determine eligibility.
Purpose and benefits: It can guide users through the application process step-by-step, recommend additional resources for grant writing, and help determine eligibility to save time.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Assisted Referral Tool (ART)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool suggests which study sections an application might best fit for peer review. The applicant can then get a better idea of the topics being reviewed in that study section and the expertise the reviewers are likely to have. This tool is also used internally to help efficiently assign applications into study sections.
Purpose and benefits: This tool provides information to applicants about the context of the eventual review of their applications and increases efficiency of internal study section assignments.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-15
US federal AI use case: Autism Spectrum Disorder (ASD) Classification Model for Children using Deep Neural Network
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Automated approaches for table extraction
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Automated Basic-Applied Categorization of extramural grants
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This machine-learning algorithm uses information about NIMH-funded research projects to categorize them as basic or applied research per the federal definitions for each.
Purpose and benefits: The algorithm is intended to be consistent in identifying basic and applied research, reduce burden of review by NIMH staff, and provide a complementary perspective to human review.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Biomedical Citation Selector (BmCS)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Time-consuming human review of individual journal articles from multidisciplinary journals to determine inclusion in MEDLINE.
Purpose and benefits: More efficient and effective indexing and inclusion of relevant journal articles, standardization of citation record selection, and reduced processing time
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Clinical Trial Predictor
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Applications that propose clinical trials that are submitted to NOFOs that do not allow clinical trials cannot be funded, no matter how well they do in review, because they were not reviewed using all appropriate clinical trial criteria. This application allows NIH to identify clinical trial applications submitted to NOFOs that do not allow clinical trials so they can be withdrawn before being reviewed and potentially transferred to a NOFO that does allow clinical trials.
Purpose and benefits: The AI tool predicts whether grant applications may involve clinical trials based on the text of their titles, abstracts, narratives, specific aims, and research strategies. It is very difficult to deal with misclassified CTs that make to review on a CT not allowed FOA: no matter how good the score is, the IC cannot fund them.  The CT prediction algorithm is used to help identify potential CTs on CT not allowed NOFOs, mainly the parent R01.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-23
US federal AI use case: ClinicalTrials.gov Protocol Registration and Results System Review Assistant
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The quality control review process at ClinicalTrials.gov is time- and resource-intensive.
Purpose and benefits: Some potential benefits include increased efficiency, consistent reviews, resource optimization, and increased scalability.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-08-23
US federal AI use case: Collections Summarization Chatbot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Complex, time-consuming methods for discovery of content, and difficulty for users to understand the scope of content through summarization.
Purpose and benefits: Improved discovery and understanding of content in NLM Digital Collections
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: CSR Public Chatbot (CPC)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool answers questions from the public, potential reviewers, and applicants by directly pulling relevant information from official government web pages, instead of searching FAQ lists.
Purpose and benefits: Applicants and Reviewers can get their NIH grant applications and peer review questions answered quickly and efficiently
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-22
US federal AI use case: DAIT AIDS-Related Research Solution
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: DAIT POs need to identify grant applications that involve AIDS-related research (ARR) so they can evaluate them for additional funding.
Purpose and benefits: DAIT ARR suggests prioritization of grant applications that are likely to include AIDS-Related Research to assist POs in prioritizing which grants to select, which improved the review time and quality of review for ARR applications.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-18
US federal AI use case: Detecting Overlapping Science (DOS)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool detects applications that represent potential duplicate funding (funding the same research in different projects). It examines applications as they are submitted to the NIH and sends a report to relevant personnel in the agency.
Purpose and benefits: Detect and prevent duplicate funding with real-time examination of incoming grant applications in a speedy manner.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Detection of Implementation Science focus within incoming grant applications
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool uses natural language processing and machine learning to calculate an Implementation Science score that is used to predict if a newly submitted grant application proposes to use science that can be categorized as "Implementation Science."
Purpose and benefits: The AI report tool assigns the grant application to a particular division for routine grants management oversight and administration.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-20
US federal AI use case: Expansion of Generative AI (GenAI) Caption Generation for all Collections Videos
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: High cost and low efficiency of video transcript and caption generation.
Purpose and benefits: Improved standardization and accuracy of generated video captions
Topic area: IT
Development stage: Pilot
First used: 2026-11-20
US federal AI use case: Federal IT Acquisition Reform Act (FITARA) Tool
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Contracting officers use this tool to help identify whether a Statement of Work meets the criteria of Federal IT Acquisition Reform Act (FITARA).
Purpose and benefits: Contracting Officers can use this tool to indicate if Statements of Work are likely to be IT-related, which saves significant manual effort and time required to identify relevant contracts.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-17
US federal AI use case: Generative AI (GenAI) Still Image Tagging
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Lack of a cost-effective mechanism to label and describe digital images.
Purpose and benefits: Enhanced searchability and discoverability of images included in the NLM Digital Collections, increasing access to valuable medical and scientific resources and supporting research and health.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: individual Functional Activity Composite Tool (inFACT)
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Internal Referral Module (IRM)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Automated Assignment of Grants to Program Officers
Purpose and benefits: The original IRM application grew out of a desire to refer applications to the appropriate Program Officer to manage the scientific research that fit their portfolio.  This manual referral of grant applications still exists within IRM and has been complemented by use of AI/NLP capabilities.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The output from this AI use case does not drive any agency decision, including the categories listed in section 6 of the memorandum.  The output is a recommendation to assign a grant application to the appropriate Agency staff based on the scientific content of the application.  The person to whom the grant application has been referred to can accept, reject, or reassign the application based on their expertise.
First used: 2026-02-23
US federal AI use case: JIT Automated Calculator (JAC)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: NIGMS requires extra justification to fund investigators whose total grant money exceeds $1.5M.  Applicants are required to submit lengthy forms detailing all of their support and those of their key personnel.  These forms are quite long and require quite a bit of time to read and tally up funding totals.  This NLP searches the entire form and adds up all of the totals to help NIGMS program staff determine total funding for all key personnel of a grant application.
Purpose and benefits: At NIGMS we like to know how much total support an investigator has to ensure that we are not funding PIs who are already adequately resourced. However, JIT Other Support forms consist of many pages of freeform text in PDF format.  Thus, it can be quite tedious for program officers (POs) to copy and paste information from these forms into a spreadsheet to determine how much funding a PI has. JAC can perform these calculations for POs automatically (assuming, of course, that the information has been entered correctly by the PIs).
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-23
US federal AI use case: LLM Support for Admin Services
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Mapping Sequence Data to Research Outcomes
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Effectively allocating resources needed to process, manage, and store a high volume of sequence data.
Purpose and benefits: More efficient operations and resource allocation
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Medical Text Indexer-NeXt Generation (MTIX) MEDLINE Indexing
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Time-consuming and burdensome manual indexing of MEDLINE citations
Purpose and benefits: Cost-effective, timely, and efficient indexing of MEDLINE citation records.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-23
US federal AI use case: MicroStrategy Evaluation
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: NanCI: Connecting Scientists
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: NanCI phone/web application to connect scientists. The app uses AI to match scientific content to users' interests. By collecting papers into a folder, a user can engage the tool to find similar articles in the scientific literature and can refine the recommendations by up or down voting recommendations. Users can also connect with others via their interests and receive and make recommendations via this social network. Users: Cancer Research Trainees at NCI and across the USA.
Purpose and benefits: Cancer research trainees indicate feeling overwhelmed by information and finding things of interest is a challenge. Furthermore they feel isolated. NanCI helps them home in on key content of interest and connect with others who share those interests.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-03-23
US federal AI use case: NBS Virtual Assistant
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Service Desk Operations
Purpose and benefits: A robust knowledge base supports the NBSC user community, significantly reducing service requests to the ONBS Service Desk. This allows Subject Matter Experts (SMEs) to concentrate on critical operational and maintenance activities.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-25
US federal AI use case: NCI-DOE Collaboration, MOSSAIC project (Modeling Outcomes using Surveillance Data and Scalable AI for Cancer)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: MOSSAIC applies deep learning natural language processing (NLP) and foundation models to population-based cancer data collected by NCI's Surveillance, Epidemiology, and End Results (SEER) program. DOE's Oak Ridge National Lab (ORNL) has data use agreements (DUAs) with multiple SEER registries to access and train models using SEER data. Two APIs are in production use in the data management system used by the SEER registries -- OncoID which predicts whether a pathology report is related to cancer and OncoIE which extracts key tumor characteristics from unstructured pathology report text.  Together these APIs are an important part of moving the US towards near real-time cancer incidence reporting.  In addition, a third API OncoMetsID, which predicts whether a pathology report is indicative of metastatic disease, is in a pilot phase to use in conjunction with other sources of information in the registries to identify recurrent disease.
Purpose and benefits: MOSSAIC enhances the infrastructure of the SEER cancer registries by providing tools that can increase the efficiency and accuracy of manual data abstraction by automatically extracting cancer surveillance data elements.  SEER registries receive millions of unstructured clinical text documents that must be manually reviewed, leading to a lag in reporting of US cancer incidence trends.  Automated tools such as those developed by MOSSAIC will help us achieve near real-time incidence trends and ultimately a more meaningful report card on the status of cancer in the US.
Topic area: Science
Development stage: Deployed
First used: 2026-01-21
US federal AI use case: NHLBI Chat
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This is a generic AI chat tool that provides a secure chat interface (similar to public tools like ChatGPT) for NHLBI Chat. This tool enables staff to safely and securely explore how generative AI can be used on their sensitive (but non-PII/PHI) workloads.
Purpose and benefits: NHLBI Chat is a secure LLM tooling providing access to the Azure OpenAI API so that all NHLBI staff can explore generative AI for their day-to-day need.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-09-24
US federal AI use case: NIAMS AI Chatbot Pilot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: To provide a secure, protected environment for NIAMS staff (OD, EP, IRP and IT) to explore, test, and understand how to use AI to be more efficient with a wide variety of administrative tasks such as general research, summarizing/querying documents, drafting emails, generating programming code, and creating presentation outlines?, etc.
Purpose and benefits: The Azure-hosted NIAMS GenAI Chatbot helps employees be more efficient with a wide variety of administrative tasks such as summarizing/querying documents, drafting emails, and creating presentation outlines?, etc.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-09-24
US federal AI use case: NICHD RPAB AI/ML Application Referral System
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications.
Purpose and benefits: This AI use case increases the efficiency of the grant referral process and reduces overlapping efforts in grant referral review.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The primary objective is to improve the efficiency, accuracy, and consistency of grant application referral assignments while streamlining the internal process for referring new applications. The AI-generated output supports subject matter experts by providing additional information that helps them make faster decisions and prioritize applications for review. All AI output is used solely as an assistive tool, and every referral decision undergoes 100% human review. Therefore, this use case does not meet the definition of a high-impact AI.
First used: 2026-08-24
US federal AI use case: NLP Automated Referral
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Referring applications manually is tedious and time consuming.  Using an automated approach allows staff to focus their time on more difficult tasks.
Purpose and benefits: Automated referral allows NIGMS to retain institutional referral knowledge by training on historical data, eliminates delays in referral by assigning applications as soon as they come in, and reduces burden on staff members and allows them to allocate more of their time to other high value tasks.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: First, the NLP Automated Referral tool produces non-binding recommendations regarding which NIGMS program officer is most appropriate to manage an incoming grant application, along with two alternative suggestions. Program officers must actively accept the assignment or refer the application to a more appropriate program officer, and they have full discretion to ignore or override the AI tool’s recommendations. The system’s output is therefore not a principal basis for any legal or binding decision; it is one of several informational inputs to an internal workflow choice made by human staff.
Second, all funding and programmatic decisions are made through established peer review and programmatic processes, governed by existing NIH/NIGMS policies and human judgment. Final funding decisions are made by the NIGMS Director in consultation with the NIGMS Advisory Council, the NIGMS Deputy Director, and the NIGMS Division Directors, not the individual program officers who manage the applications. As a result, the AI’s output does not directly affect an individual’s or organization’s access to Federal funding or other critical government resources or services, nor does it alter anyone’s legal status or rights.
Finally, the NLP Automated Referral tool does not fall into any of the categories of AI use cases identified in Section 6 of M-25-21 that are automatically designated high-impact. It is a routing tool used for internal staff portfolio management.
First used: 2026-08-20
US federal AI use case: OCIO GenAI Advisor
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: OIT Help Desk Chatbot
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: The OIT help desk would like to shorten resolution time for tickets, help empower users, generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports.
Purpose and benefits: If used effectively, the OIT help desk can shorten resolution time for tickets, help empower users, generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Person-level disambiguation for PubMed authors and NIH grant applicants
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: A person or entity may use several name variations on publications and/or grants, which causes uncertainty in attributing research contributions
Purpose and benefits: Correct attribution of grants, articles, and other products to individual researchers is critical for high quality person-level analysis. This improved method for disambiguation of authors on articles in PubMed and NIH grant applicants can inform data-driven decision making
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-02-23
US federal AI use case: Portfolio Analysis Summarization Tool (PAST)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Reduce effort for summarization of custom research portfolios
Purpose and benefits: Rapid summarization of custom research portfolios which will be used to support program staff and others across the institute.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Program Classification Coding
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: To aid users, a solution has been developed that suggests the specific PCC that should be assigned to each application.
Purpose and benefits: The Division staff is responsible for handling a large volume of applications during each council round, with the task of processing and assigning PCCs. To aid users, a solution has been developed that suggests the specific PCC that should be assigned to each application.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: RCDC AI Validation Tool
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Research Performance Progress Report (RPPR) Report Comparison
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Identification of duplication and overlap in grants year over year.
Purpose and benefits: Research Performance Progress Reports (RPPR) are used by recipients to submit progress reports to NIH on their grant awards. AI can analyze such data to identify duplication and overlap in grants year over year.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Scientific summaries tool
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Enhancing scientific summary development for communicating scientific achievement.
Purpose and benefits: Within NIAID DIR we have team that drafts justifications for personnel actions based on the research being performed. This tool will be created to help them quickly and effectively prepare justifications for personnel actions for investigators in specific research fields.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Similarity-based Application and Investigator Matching (SAIM)
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: SRDMS NLP COI
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Scientific review officers need to identify individuals who may pose a conflict-of-interest (COI) during the grant application review process.
Purpose and benefits: SRDMS NLP COI is able to automate the identification of individuals who may pose a conflict-of-interest during the grant application review process, which saves significant time and effort by the SRO during application review and promotes consistency in identifying COIs.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-19
US federal AI use case: Stem Cell Auto Coder
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Study Section Clustering Tool (SSCT)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Efficiently organizing grant applications into appropriate study sections based on scientific similarity.
Purpose and benefits: The Study Section Clustering Tool (SSCT) enhances the agency’s mission by automating and streamlining the organization of grant applications into scientifically relevant study sections, improving efficiency and reducing manual effort. It ensures applications are reviewed by experts with appropriate expertise, adapting over time to changes in scientific fields through periodic model updates. This data-driven support leads to higher-quality peer review processes, promoting more effective research funding decisions
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-01-23
US federal AI use case: Synonymy prediction in the UMLS Metathesaurus
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: TB Case Browser Image Text Detection
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Without OCR technology, validation of text on images is manually intensive and, without proper controls, can create the risk of sensitive information coming into the system.
Purpose and benefits: Provides additional protection against PII/PHI ingress into the TB Portals imaging dataset in a far more automated process.
Topic area: Science
Development stage: Deployed
First used: 2026-01-19
US federal AI use case: Tool for PO Lookup Assignment (TPAL)
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: This tool helps NIGMS program staff determine the most appropriate SME at NIGMS and/or the most appropriate IC for a research proposal.
Purpose and benefits: There are many occasions in which Division Directors, Branch Chiefs, and Program Officers wish to receive suggestions for the most appropriate people to talk to about a project proposal or where to send a proposal that might not be appropriate for NIGMS.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-07-20
US federal AI use case: Transformative Research Award Anonymization Check (TRAAC)
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Machine learning pipeline for mining citations from full-text scientific articles
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Machine learning system to predict translational progress in biomedical research
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: AI Software for Conference/Workshop Summaries
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: CylanceProtect
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: NIH Grants Virtual Assistant
Agency: Department of Health and Human Services - HHS/NIH
Problem addressed: Provides easy access to grants information and policies.
Purpose and benefits: Chat Bot to assist users in finding grant related information.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-05-20
US federal AI use case: Open AI SharePoint Document Assistant
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired
US federal AI use case: Splunk IT System Monitoring Software
Agency: Department of Health and Human Services - HHS/NIH
Development stage: Retired