GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Projected future changes in the geographic distributions of the threatened Plethodon nettingi and a potential competitor
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Estimation of environmental suitability for two salamander species.
Purpose and benefits: The expected benefit of this use case is to create cost savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Random Forest with Aquatic Effectiveness Monitoring
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Protecting aquatic ecosystems and restoring watershed processes can be improved by understanding predictors of sediment in streams after wildfire.
Purpose and benefits: Aids in protecting aquatic ecosystems and restoring watershed processes.
Topic area: Science
Development stage: Pilot
First used: 2025-09-05
US federal AI use case: Forest fire effects remote sensing models
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The AI use case is intended to predict the status of forests after wildfires.
Purpose and benefits: The AI use case supports research into low-cost data sources for mapping forests after a fire.
Topic area: Science
Development stage: Pilot
First used: 2025-07-21
US federal AI use case: GIS Help Desk Chat Bot
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Answer easier Geographic Information System (GIS) help desk questions based on published Knowledge Based Articles (KBAs) which are used to train the model.
Purpose and benefits: Reduce customer wait times when they have a geospatial question. Help desk agents will also have more time to devote to geospatial questions, improving customer service overall.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Select Tree Mapping
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Improving access to data about Select Trees (trees with superior growth for timber production) from Select Tree Registers (physical documents with information about the tree and its location)
Purpose and benefits: Cost savings by reducing the amount of work time that would otherwise be spent doing this repetitive task
Topic area: Science
Development stage: Pilot
First used: 2025-07-01
US federal AI use case: Yearly Incident Procurement Classification Report
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: As part of planning for the upcoming fire season, all previous year incident-related procurements are categorized manually. This is a time-consuming process and humans may not be able to identify more nuanced, purchasing patterns.
Purpose and benefits: The goal is to identify patterns and cost savings opportunities for purchases in the upcoming fire season, which in turn could lead to quicker resource allocation to incident locations.
Topic area: Procurement and Finance Mgmt
Development stage: Pilot
First used: 2024-12-11
US federal AI use case: American Recovery Act Classification Tool
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: This project will use AI/ML to better understand how contracts and agreements funded through the American Relief Act support disaster response themes across the country, such as road repair, timber salvage, and recreation site rehabilitation.
Purpose and benefits: This project will promote accountability and enable public reporting of expenditures.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2025-07-01
US federal AI use case: Automated Road Mapping
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Road and trail maps are essential for effective fire response, forest management, and public safety in wildland areas. Unfortunately, many existing maps are outdated or incomplete. Forest Service employees currently rely on time-consuming and labor-intensive methods like GPS mapping.
Purpose and benefits: Effective implementation may enable the Forest Service to more comprehensively manage road network data at lower cost. Improved data can increase safety on federal lands.
Topic area: Science
Development stage: Pilot
First used: 2025-05-05
US federal AI use case: Policy Analysis
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Review roughly 1500 handbooks and policies to identify any duplications and perform information extraction for analysis.
Purpose and benefits: Decrease time requirement and increase effectiveness of policy and handbook reviews
Topic area: Other
Development stage: Deployed
First used: 2025-08-01
US federal AI use case: Turbo plan
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Accelerating environmental planning with tools and services to achieve the Wildfire Crisis Strategy with specific focus around National Environmental Policy Act planning and appraisal process optimization.
Purpose and benefits: Meet and exceed the Wildfire Crisis Strategy, accelerate the pace and scale of preparedness planning before a catastrophic wildfire, reduce the time and cost of planning, standardize the planning process, reduce planning barriers
Topic area: Energy and Environment
Development stage: Deployed
First used: 2025-07-01
US federal AI use case: Forest Plan review and planning support
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Opportunity to increase business productivity related to forest plan reviews by analyzing data and information at a rapid pace
Purpose and benefits: Speeding up analysis of data, improving service for public, and mission area enablement.
Topic area: IT
Development stage: Deployed
First used: 2025-06-02
US federal AI use case: TreeSearch
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: There is opportunity to streamline the usability of the US Forest Service (USFS) TreeSearch website, the primary database for Forest Service-sponsored research.
Purpose and benefits: Currently, Forest Plan Revision team members would search the USFS TreeSearch database of over 60,000 documents manually to find relevant publications to support their work. This AI solution would expedite the team's research process by providing quick, accurate, and context-aware answers from multiple sources.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2025-03-17
US federal AI use case: Infosec Chatbot
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Information Security AI is a Chatbot that uses generative artificial intelligence (AI) to support users by responding to questions on information security.
Purpose and benefits: Decreases the time required to retrieve information from various National Institute of Standards and Technology (NIST) security publications.
Topic area: Cybersecurity
Development stage: Deployed
First used: 2025-04-09
US federal AI use case: Bugnet
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Detect and map locations of tree damage caused by insects and disease, separate from fire and harvest-caused damage.
Purpose and benefits: The detection of forest damage using satellite imagery and AI is a safer, faster, and cost-efficient way to monitor forest conditions across entire states and in difficult to reach locations.
Topic area: Science
Development stage: Pilot
First used: 2023-06-11
US federal AI use case: Recreation site chatbot
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Estimating recreation use at recreation sites.
Purpose and benefits: Addressing requirements of the federal Expanding Public Lands Outdoor Recreation Experiences (EXPLORE) Act.
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: UAS FS Nursery Seedling Inventory
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The primary purpose of this project is to reduce the amount of time each Forest Service nursery takes to manually count seedlings using images. It takes each nursery 3-5 people between 3-6 weeks at least once a year to manually count seedlings and it’s physically very demanding.
Purpose and benefits: Reducing costs and increasing accuracy of counts.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Forest Mapping using G-LiHT airborne data in interior Alaska
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: This project supports the first comprehensive forest inventory of interior Alaska in the modern era. Machine learning techniques will be used to predict various forest attributes (volume, biomass, composition).
Purpose and benefits: Use of multi-sensor airborne data will improve the accuracy and precision of forest inventory estimates over interior Alaska, improving the quality and reliability of products provided to the public.
Topic area: Science
Development stage: Pilot
First used: 2014-06-01
US federal AI use case: Late seral forest complexity
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: To evaluate relationships between remotely collected data on forest conditions and more detailed information on forest conditions obtained through direct measurements in the forest
Purpose and benefits: A better understanding of how to accomplish and document desired forest conditions through management
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Forest Health Protection: Survey and mapping of forest damage from insects and diseases using machine learning
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Forest Health Protection is responsible for surveying ~500 million acres of forest for forest damage and mapping the risk of mortality from insects and diseases.
Purpose and benefits: Cost savings, increased work efficiency, expanded coverage over hard to reach locations
Topic area: Science
Development stage: Pilot
First used: 2015-12-01
US federal AI use case: FS UAS Classification - Fire & Natural Resources
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Web applications in Google Cloud Platform (GCP) for unmanned aerial system (UAS) allow the Forest Service to deliver user-friendly tools that meet niche program needs, supporting both fire management and timber production.
Purpose and benefits: These core functions reduce costs, streamline data delivery, reduce risks, and improve decision-making in the field
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Cone Survey123 AI Image Cone Counts
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: High-quality seed is essential to sustain long-term reforestation goals and timber production, both identified as national priorities under Executive Order 14225 (Increasing Timber Production), Secretarial Memo 1078-006, and the Trillion Trees Act. Without reliable cone survey data, nurseries cannot collect sufficient seed, which jeopardizes seedling production, reduces reforestation capacity, and constrains timber production.
Purpose and benefits: Cone survey automation's primary return on investment is the time and cost savings from reducing manual cone counts and strengthening the reforestation pipeline.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Predicting fire severity potential in future wildfires in California
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Provide information on where high fire severity (high vegetation mortality) is likely to occur during future wildfires. Provides information for planning where to conduct management activities to lower the adverse effects of future wildfire.
Purpose and benefits: Cost savings. Reducing adverse effects of wildfire on forest resources and human communities.
Topic area: Energy and Environment
Development stage: Pilot
First used: 2025-08-01
US federal AI use case: Forest Inventory and Cruising using GAIA AI
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Intended to solve the problem of collecting forest inventory data in large areas with limited staff and time.
Purpose and benefits: The AI use case has the potential to generate accurate forest inventory data with less staff and at a higher pace. Increasing the pace and scale of forest management and restoration.
Topic area: Science
Development stage: Pilot
First used: 2025-04-15
US federal AI use case: Forest demographic modelling
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of this model is to investigate how species' responses to climate and disturbance influence forest resilience.
Purpose and benefits: Project information will help land managers identify opportunities for high-impact management interventions in forests to promote resilience.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: App for predicting soil temperature during prescribed burns
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: It is a tool to assist managers with determining the predicted soil temperature based on fuel load.
Purpose and benefits: By determining the expected soil temperature, managers can determine how best to do a prescribed burn without damaging tree root systems which could lead to tree death
Topic area: Science
Development stage: Pilot
First used: 2025-09-08
US federal AI use case: Wildfire effects and postfire forest dynamics
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Predicting what a forest will be like after it burns is difficult since forests respond to disturbance in a variety of complex ways and forest conditions change over the first few decades after a fire.
Purpose and benefits: Improving our understanding of how forests in the Pacific Northwest respond in the years after a wildfire to provide information for management of fire-affected areas.
Topic area: Science
Development stage: Pilot
First used: 2025-01-06
US federal AI use case: Flight Aviation Safety system 2.0
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: AI based recommendation system to enhance flight safety by analyzing data, predicting potential safety issues, and aiding in real-time decision-making.
Purpose and benefits: Real-time data analysis reduces decision-making time for public safety
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Forest habitat mapping for marbled murrelets
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Mapping marbled murrelet nesting habitat across the Pacific Northwest over the last 30+ years.
Purpose and benefits: High accuracy maps that can be used by land managers for planning and monitoring requirements under the Northwest Forest Plan.
Topic area: Science
Development stage: Pilot
First used: 2024-09-01
US federal AI use case: Washington state forest inventory maps
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The AI use case produces informational forest maps for the state of Washington, which may be used to support environmental monitoring and planning.
Purpose and benefits: The AI system improves access and usability of forest data by creating informational maps for end users.
Topic area: Energy and Environment
Development stage: Pilot
First used: 2025-07-01
US federal AI use case: Forest Vegetation Simulator (FVS) Helpdesk Chatbot RAG
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The Chatbot will respond to questions from users of the model (for both public and federal users), reducing staff time spent responding to questions.
Purpose and benefits: Solution enhances user support, reduces staff burden, and strengthens the quality of FVS.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Long-term monitoring of northern spotted owl habitat and populations
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Machine learning methods are used to model and map forest structure and species composition as it relates to owl nesting and roosting in forests. Machine learning is also used to model and map annual time series of owl habitat that includes forest type and other variables, such as topography.
Purpose and benefits: The time series maps are used to meet legal owl monitoring requirements and avoid potential litigation if not done. They also build trust with the general public and provide easy access for anyone to explore how these forests and habitats are changing through time in response to forest disturbances and growth.
Topic area: Science
Development stage: Pilot
First used: 2011-10-01
US federal AI use case: Feral Ungulate Mitigation
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Feral ungulates (animals with hooves) are not native to Hawaii, cause agricultural and forest damage, and can also transmit disease. Camera traps help to manage feral ungulate populations moving into fenced areas, but human image processing is time consuming and inefficient.  The Hawaii AI-supported feral ungulate computer vision program was developed to process camera trap data quickly, provide managers with statistics on the feral ungulate populations, and provide real time surveillance to management operations.
Purpose and benefits: Reduce cost and human resources needed to process vast amounts of camera trap images. The program will help conservation managers strategically prioritize mitgation and control efforts.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Fire Containment Suitability (FireCON)
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Identification and assessment of appropriate wildland fire containment locations to improve the quality of information available to incident command teams.
Purpose and benefits: Improved efficiency and effectiveness of fire containment strategies.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2025-06-23
US federal AI use case: Potential Control Location Suitability
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Identification of strategic fire control locations for pre-fire planning and wildfire management
Purpose and benefits: Improving wildfire management outcomes, communication during fire planning, prioritizing investments for strategic fuel treatments.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2017-05-08
US federal AI use case: Alisha OCR Reader
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Automates data extraction from OF-286 (Emergency Equipment Use) invoice forms
Purpose and benefits: Enhanced workflow efficiency, better knowledge management, improved accuracy & consistency, frees up staff to focus on strategic tasks rather than manual document handling.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2025-03-31
US federal AI use case: Recreation One Stop use of AI
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: There is an administrative burden in booking sites and planning trips to Forest Service land. Recreation One Stop is a tool to support bookings and provide alternatives where there is no availability, provide awareness via recommendations of various inventories to support trip planning, and to capture large amounts of content into a summarized version for easier readability within ratings and reviews.
Purpose and benefits: Benefits to the public include improved customer service, increased occupancy rates at federal campgrounds, greater stewardship of natural resources and the environment, and removing barriers to the American public in support of Executive Order 14179.
Topic area: Service Delivery
Development stage: Deployed
First used: 2025-05-21
US federal AI use case: Timber products output Sample Design Evaluation and Enhancement
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Improve U.S. roundwood production estimates on the county level
Purpose and benefits: Better estimates of roundwood utilization will improve analysis of timber resource use across the U.S.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: LANDFIRE
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Improve fire behavior prediction by mapping vegetation and detecting disturbances for the United States contributing to the downstream production of current fuel types and fuel metrics.
Purpose and benefits: Increasing accuracy in fire behavior prediction models and assessing, prioritizing, and reducing risk for firefighters and land managers.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2004-01-01
US federal AI use case: NRE FS Customer Relationship Manager New Hire Experience (NRE FS CRM NHE) CSAM ID 2639
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: There is a need for assisting end users in human resources, business and finance solutions, or processes and procedures
Purpose and benefits: Provide guidance and assistance to the end user.
Topic area: HR
Development stage: Deployed
First used: 2024-01-01
US federal AI use case: NASS Data Dissemination Agent
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: National Agriculture Statistics Service (NASS) Data is relatively difficult to retrieve. The intent of this project is to make that data more accessible to users.
Purpose and benefits: More access to data should lead to more public use of the data and higher customer satisifaction scores.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Preseason Corn and Soybeans Forecasts
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Pre-season planted acreage forecasts for corn and soybeans are needed for decision-making by the National Agriculture Statistics Service (NASS) agricultural statistics board
Purpose and benefits: Improves information availability to the National Agriculture Statistics Service (NASS) agricultural statistics board for strategic decision-making.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Farm Services Agency (FSA) Acreage Coverage Evaluation
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Since not all farms sign up for Farm Service Agency (FSA) programs, total crop acreage is estimated by National Agriculture Statistics Service (NASS) for use in its programs.
Purpose and benefits: National Agriculture Statistics Service (NASS) uses predicted crop acreage as a benchmark in its annual acreage estimates programs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: VegScape/CropCASMA Data Portals
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Shows crop conditions and soil moisture availability, which serve as inputs to increase the quality of crop modeling
Purpose and benefits: Supplements weekly Crop Progress and Condition Reports, provides direct input into disaster assessments on agriculture, and provides early disaster anomaly warnings
Topic area: Science
Development stage: Pilot
First used: 2012-01-01
US federal AI use case: Public Comments Analysis
Agency: Department of Agriculture - Office of the Chief Information Officer
Problem addressed: Public comments analysts are overburdened with processing large volumes of public comments. This delays regulatory review and burdens policy staff.
Purpose and benefits: Automation of tedious and repetitive tasks reduces cost and increases speed and efficiency when addressing public comments
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Voiceover App
Agency: Department of Agriculture - Office of the Chief Information Officer
Problem addressed: Recording audio for videos (onboarding, educational materials, etc.) is costly and time consuming.
Purpose and benefits: Reduces labor hours and cost required to record and edit spoken audio
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: AI for Environmental Permitting Review
Agency: Department of Agriculture - Office of the Chief Information Officer
Problem addressed: The environmental review process is lengthy and burdensome. We are using AI at several steps in our proposed environmental review solution, including an AI evaluation stage and in comment processing.
Purpose and benefits: Faster time to environmental permit issuance, with fewer people and hours needed to get to a "break ground" phase.
Topic area: Energy and Environment
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI is not making any final decisions, only recommendations for authorized staff to review.
First used: 2025-08-19
US federal AI use case: PHIS Export Library Assistant
Agency: Department of Agriculture - Food Safety
Problem addressed: Members of the meat industry apply for export certification in Food Safety Inspection Service's (FSIS) Public Health Information System (PHIS) and then FSIS employees review and certify that the exported products meet foreign country requirements. These foreign country requirements are often complicated and text based, requiring industry applicants and FSIS employees to find and read complicated text documents to verify that proposed export products meet applicable requirements. This AI use case is intended to aid PHIS users in finding foreign country requirements applicable to a particular export application through a context-aware chat-capable AI interface integrated in the PHIS user interface.
Purpose and benefits: This tool would be expected to reduce the labor costs and time spent by industry applicants and Food Safety Inspection Service (FSIS) employees in creating, submitting, and approving meat and poultry exports. The tool should also decrease mistakes, reducing the need for replacement certificates.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Chatbot for IT Customer Help Desk
Agency: Department of Agriculture - Office of the Chief Information Officer
Problem addressed: The chatbot assists USDA staff with IT issues by answering common questions and initiating a support request if additional help is needed.
Purpose and benefits: Faster resolution of IT issues for customers, cost avoidance through fewer requests to helpdesk, decreased burden on helpdesk staff.
Topic area: IT
Development stage: Deployed
First used: 2025-06-30
US federal AI use case: AGFA DR 800 with MUSICA Dynamic
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: It can accelerate X-ray workflows. The optional image processing allows users to conveniently select image processing settings for different patient sizes and examinations. The image processing algorithms in the new device are similar to those previously cleared and used in Agfa’s radiography portfolio today which includes the DR 600 (K152639) and DR 400 (K141192). The addition of the dynamic image processing is identical to the predicate device (K140380).
Purpose and benefits: Accelerate X-Ray workflows.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Volpara Imaging Software
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Generates volumetric measurements of breast x-ray scans.
Purpose and benefits: Accelerate volumetric measurements of breast x-ray scans.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Identity Governance and Administration (IGA)
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Development stage: Retired
US federal AI use case: SafePointe WDS - OSSO
Agency: Department of Veterans Affairs - OSP: Office of Operations, Security, and Preparedness
Development stage: Retired
Risk category: High-impact
US federal AI use case: Beckman Coulter DxH 800
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Generating blood cell measurements. The DxH 800 System provides automated complete blood count, leukocyte differential, nucleated red blood cell (NRBC) enumeration and reticulocyte analysis as well as an automated method for enumeration of the Total Nucleated Cells (TNC) and Red Blood Cells (RBC) in body fluids.
Purpose and benefits: Accelerate blood cell measurements.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: UiPath Document Understanding
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: This tool has the ability to intelligentially pull data from different types of documents and transfer that to other applications so that a human does not have to.  It makes this type of input be more accurate and can be run 24/7/365 if needed leaving the staff to help with forms/documents that cannot be interpreted by the automated system.
Purpose and benefits: This tool helps with the allowing the staff to focus on higher decision making processes instead of data input.  It increases the efficiency of a process by not introducing errors.  It can be a cost avoidance tool when the process it is part of incurs extra fees for this type of work.
Topic area: Other
Development stage: Deployed
US federal AI use case: Potential Fraud or Waste
Agency: Department of Veterans Affairs - OM: Office of Management
Problem addressed: Quickly develop predictive analytics based on historical values captured and assessed within the Data Analytics Service (DAS) Dashboard.

The dashboards allow various purchase card managers to monitor purchase card spending of employees.  The dashboards help monitor for fraud, waste, and abuse, and assist in providing oversight for compliance with purchase card laws and policies. We have now incorporated advanced data analytics within the dashboard.  

The Advanced data Analytics model generates an expected received date for items.  It then flags items that have gone beyond the expected generated date by flagging items that were received late (possible waste) and/or items that have not been received yet (possible fraud).  This has the potential to save the analyst time by investigating cases that have been flagged as opposed to investigating potentially an entire dataset, which is what they have been doing up until now.

The model analyzes all purchase card transactions including ordered, delivery, and received dates as its inputs.  It outputs expected received dates.  The intended users of the product are purchase card managers.
Purpose and benefits: - Delivers high value to Customers, Partners, and Stakeholders by enhancing user efficiency and analytical capacity.
- Customers receive quick responses to data analysis questions,
- Customers can communicate requests to the Chatbot using natural langue format,
- Provides quick and accurate answers to inquiries
- Provides 24/7 support
- Reduces support team emails
- Provides support for ad hoc data analytics requests
- Improves scalability to support customers.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Xeleris V
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Xeleris V AI-enabled clinical applications provide an accurate, efficient, and reproducible way to segment organs. AI-enabled clinical applications have the potential to greatly improve efficiency and precision in nuclear oncology.
Purpose and benefits: Xeleris V leverages AI to accurately segment organs for quantitation and dosimetry calculations. The AI demonstrated a 58% average reduction in the time required for the user to process and calculate the dose. This AI-enabled lung segmentation demonstrated an overall success rate of 89% and reduction in the number of clicks by an average 57%.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Roche Digital Pathology
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Decrease manual effort in analyzing and identifying tumors on slides. AI technology will inform the pathologists how much of the neoplastic cells are staining with such-such stain and give the percentage of tumor involvement - this is quite helpful in analyzing the involvement of prostate carcinoma in each of the prostate core. The pathologist will not perform much of the “manual labor” and be more efficient.
Purpose and benefits: Reduce and prevent errors, enhance turnaround times, increase cost savings, and enhance patient care.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Activity recognition using wearable sensors for use in closed loop deep brain stimulation systems
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: VCA & PPMS Chatbot
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
US federal AI use case: AgileMD eCART Clinical Deterioration Model
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact
US federal AI use case: Verkada Camera - OSLE
Agency: Department of Veterans Affairs - OSP: Office of Operations, Security, and Preparedness
Problem addressed: Video search function inside a physical security camera solution.
Purpose and benefits: AI will aid in the search of recorded video and images. This will decrease the number of man-hours required to search through these video and images. Additionally, this will enhance outcomes when searching for persons of interest (i.e. Missing Persons/Patients).
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: GE Signa Artist
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The software used on the proposed SIGNA Artist system has been modified to include the AIR Recon DL feature. The User interface provides operators of the system with new options for selecting AIR Recon DL and adjusting the associated level of image noise reduction. The resulting images can have higher Signal to Noise Ratio (SNR) and improved sharpness compared to images reconstructed without AIR Recon DL.
Purpose and benefits: The nonclinical testing demonstrated that AIR Recon DL does improve SNR and image sharpness while maintaining low contrast detectability and having minimal impacts to noise spectral content, average signal intensity, or the appearance of motion artifacts. AIR Recon DL was also 
able to maintain image SNR and did not sacrifice sharpness for images acquired with a reduced scan time. The nonclinical testing passed the defined acceptance criteria, and did not identify any adverse impacts to image quality or other concerns related to safety and performance.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: PINGOO.AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Enhanced patient health literacy through education and patient engagement.
Purpose and benefits: A more educated patient population and improved health literacy as well as reduced physician burden.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Computer Aided Detection (CADe) of Neoplasia during Colonoscopy - “GI Genius”
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The device improves endoscopy quality by aiding neoplastic lesion detection during procedures.

Multiple studies and meta-analyses have found these devices increase adenoma detection rates (ADR), a well-established metric for colonoscopy quality that is related to colon cancer incidence and death. Randomized implementation of the devices across VHA facilities allowed for a pragmatic evaluation of the impact of these devices on ADR. The evaluation demonstrated that the provision of colonoscopy Computer Aided Detection devices resulted in a statistically significant 21% increase in the odds of adenoma detection and an absolute increase in ADR of approximately 4% compared to colonoscopy without CADe.
Purpose and benefits: These devices demonstrably improve endoscopy quality within the VHA, which in turn improves outcomes for Veterans, ultimately reducing morbidity and mortality.

Multiple studies and meta-analyses have found these devices increase adenoma detection rates (ADR), a well-established metric for colonoscopy quality that is related to colon cancer incidence and death. Randomized implementation of the devices across VHA facilities allowed for a pragmatic evaluation of the impact of these devices on ADR. The evaluation demonstrated that the provision of colonoscopy Computer Aided Detection devices resulted in a statistically significant 21% increase in the odds of adenoma detection and an absolute increase in ADR of approximately 4% compared to colonoscopy without CADe.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Google Cloud Platform - CCAI / Dialogflow
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Development stage: Retired
US federal AI use case: Digizens
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Patient compliance with treatment plans. Solves the problem of costly clinical trials modeling behavior. 

Digizens is an AI that creates a digital cohort of individuals who can then take part in clinical trials related to compliance with certain tasks. Various messaging can be relayed to the digital individuals in order to measure effectiveness of messaging in real world scenarios without the risk of harm to real individuals. Population based behavior modeling based on communication, messaging, and compliance. These models can be run prior to engaging actual participants in various studies to optimize adherence and outcomes.
Purpose and benefits: Improved patient compliance with more effective messaging. Lows risk cost effective clinical trials related to patient behavior.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: TrueFidelity CT Deep Learning Image Reconstruction
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Improve quality of imaging - process the information acquired during a CT for form images used in medical grade diagnostic imaging
Purpose and benefits: Improve quality of imaging of CT examinations
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Pangaea
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Identification of chronic disease and advanced guidelines for treatment and early intervention.
Purpose and benefits: Early intervention and treatment of chronic disease can mitigate advanced symptoms of chronic disease.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AIDOC BriefCase
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: BriefCase is a radiological computer-assisted triage and notification software device. The software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment. The BriefCase receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low quality, grayscale image that is captioned “not for diagnostic use, for prioritization only” which is  displayed as a preview function. This preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification. Presenting the users with worklist prioritization facilitates efficient triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.
https://www.accessdata.fda.gov/cdrh_docs/pdf23/K230020.pdf
Purpose and benefits: Presenting the users with worklist prioritization facilitates efficient triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Cogitativo
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Identify Veterans at risk for chronic diseases
Purpose and benefits: Early detection and treatment of chronic diseases
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Analytics, Data, and Decision Support Unified Platform (ADDSUP)
Agency: Department of Veterans Affairs - OALC: Office of Acquisitions Logistics, and Construction
Problem addressed: Reduce unmanaged and inefficient spend and duplicative contracts. Provide proper analytics of VA spend and Data Sharing across the agency.
Purpose and benefits: - Increased Spend Under Management and improved cost avoidance ($20B realized so far).
- Improved acquisition time and quality of acquisition packages.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: iCAD ProFound AI
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Detect breast malignancies. The ProFound Detection V4.0 software allows an interpreting physician to quickly identify suspicious soft tissue densities and calcifications by marking the detected areas in the tomosynthesis images.
Purpose and benefits: Accelerate detection of potential malignancies.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: VA Section 508 Office URL Ownership Prediction Model
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Inaccurate identification of URL ownership for webpages and documents leads to inefficiencies in remediation processes, delays in addressing issues, and gaps in monitoring and reporting. These issues ultimately undermine effective management and compromise the quality of electronic products for Veterans and their beneficiaries.
Purpose and benefits: The AI system reduces the level of effort and turnaround time for the OIT 508 Office to curate URLs and coordinate with VA Administrations to review and, as necessary, remediate URLs for accessibility compliance.  In the absence of the AI system both the 508 Office and VA Administrations would be required to undertake a lengthy and iterative process to identify the VA Administration that is the formal owner of a URL.
Topic area: IT
Development stage: Deployed
US federal AI use case: Circle CVI42
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Combining digital image processing, visualization, quantification, and reporting tools, cvi42 is designed to support physicians in the evaluation and analysis of cardiovascular imaging studies.
Purpose and benefits: cvi42 uses machine learning techniques to aid in semi-automatic contouring of regions of interest in cardiovascular magnetic resonance (MR) images and cardiovascular computed tomography (CT) images for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Automated Claims Processing
Agency: Department of Veterans Affairs - NCA: National Cemetery Administration
Problem addressed: The purpose of the AI is to automate aspects of NCA benefits and application processing to decrease the case processing time and increase the customer service delivered to Veterans and their next of kin. Currently, it is applicable to the Pre-Need and Presidential Memorial Certificate programs. In the future, other business lines may be included, such as the Headstones, Markers, Medallions, and Urns and Plaques programs. This benefits NCA by automating aspects of the workload (data entry, case creation, document digitization, letter creation, and status updates).
Purpose and benefits: The outcomes support expedited service delivery of benefits to Veterans and their next of kin. About 30% of Veteran applicants receive their Pre-Need eligibility determinations within 24 hours, without human intervention.
Topic area: Benefits Processing
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Axon Body Camera and DMS - OSSO
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: AI is used to aid in Redaction of recorded Video footage and recorded Audio footage. Additionally, AI is used via transcribing recorded videos.
Purpose and benefits: AI assisted redaction cuts down on the manpower costs to speed up the process of redacting recorded footage. The redaction protects individual's privacy. Additionally, AI is used in transcribing recorded audio this removes the requirements of a FTE to complete this process.
Topic area: Law Enforcement
Development stage: Deployed
US federal AI use case: VA CART Adenoma Detection
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Extracts adenoma status for CART Veterans using Natural Language Processing (NLP) of VistA colonoscopy surgical pathology reports
Purpose and benefits: Leads to more informed personalized discussion with patients
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: GE Logiq E10
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Accelerate ultrasound workflow with AI assistant tools.
Purpose and benefits: Reduce ultrasound procedure times.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Call Center Knowledge Navigator
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: 1. Inaccurate transcription of phone calls by the current system.
2. Need for enhanced time-to-effectiveness for new employees of the Education Call Center.
3. Improvements to quality review of transcribed conversations.
Purpose and benefits: Increased efficiency and increased accuracy of answers to beneficiary queries
Topic area: Benefits Processing
Development stage: Pilot
US federal AI use case: Smart AI Bot Assistant
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Development stage: Retired
Risk category: High-impact