AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Machine Learning for Avalanche Frequency Modeling DOI-0148 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data DOI-0135 · USGS | DOI | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Deep?learning Integrations into NEIC Operations [2024 INV#WO0000000109496] DOI-0119 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Predicting post-fire tree mortality DOI-0100 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073] DOI-0095 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Frog vocalization recognition from digital recordings DOI-0092 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Patterns in the Landscape – Analyses of Cause and Effect DOI-0090 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Estimates of Habitat Suitability of Reed Canarygrass (Phalaris arundinacea) in Upper Mississippi River Floodplain Forest Understories DOI-0084 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Submersed Aquatic Vegetation Vulnerability Evaluation Application (SAVVEA) DOI-0081 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning in remote sensing-based wildfire and natural resource risk assessments DOI-0072 · USGS | DOI | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Telemetry Analysis Learning Algorithm (TALA) DOI-0070 · USGS | DOI | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LANDFIRE DOI-0069 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Invasive Grass Mapping DOI-0068 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Evapotranspiration mapping and monitoring DOI-0067 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| National Land Cover Database (NLCD) [2024 INV#WO0000000107887] DOI-0066 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Performance Modeling / Performance Forecasting DOI-0016 · SOL | DOI | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Effects of vehicle traffic on space use and road crossings of caribou in the Arctic [2024 INV#WO0000000110111] DOI-0007 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Form 990N Machine Learning Model TREAS-125 · Internal Revenue Service (IRS) | TREAS | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Programmatic Access to LLMs via API TREASURY-OFR-002 · Office of Financial Research (OFR) | TREAS | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Insight R3095 · Chief Information Officer, Disability Information Systems | SSA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Intelligent Medical Language Analysis Generation (IMAGEN) R3096 · Chief Information Officer, Disability Information Systems | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Modernized Development Worksheet (MDW) R3099 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | Not high-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Pre-Effectuation Review (PER) / Targeted Denial Review (TDR) Models R3100 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Continuing Disability Review Model R3101 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Social Security Income (SSI) Redetermination Model R3102 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Proactive Triage and Analysis of Hearings (PATH) R3103 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| PolicyNet Plain Language Search Engine R3108 · Chief Information Officer | SSA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Benefit and Earnings Public Use File R3111 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Utility Invoices Data Extraction R3250 · MGT | STATE | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Machine Learning for geophysical data inversion DOE-195 · NETL - National Energy Technology Laboratory (FECM) | DOE | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Elucidating Genetic and Environmental Risk Factors for Antipsychotic-induced Metabolic Adverse Effects Using AI DOE-351 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) DOE-356 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Regional waste feedstock conversion to biofuels DOE-358 · PNNL - Pacific Northwest National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| OPQ-AI DOE-477 · ANL - Argonne National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Cognitive Prescreen Tool (CPT) DOE-59 · Y-12 - Consolidated Nuclear Security Y-12 (YFO) | DOE | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Invoice Scanning System DOE-632 · RL - Hanford - Richland Operations Office - Hanford (EM) | DOE | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| high level synthesis for machine learning (previously hls4ml) DOE-64 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| pComply-AI-High Risk DOE-658 · ANL - Argonne National Laboratory (SC43 OIM) | DOE | Deployed | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| READS: Real-time Edge AI for Distributed Systems DOE-70 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Risk Indicator Model NCUA_ONES-03 · NCUA|ONES | NCUA | Deployed | High-impact | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| VA CART Adenoma Detection VA-24-2008 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Care Assessment Needs (CAN) Score VA-24-5501 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VA CART SYNTAX Risk Model VA-24-5542 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VA CART Mortality Model VA-24-5583 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VA CART Nephropathy Risk Model VA-24-5624 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VA CART Bleeding Risk Model VA-24-5788 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VASQIP Mortality Calculator VA-25-4960 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| Frailty Risk Analysis VA-25-5001 · VHA: Veterans Health Administration | VA | Deployed | High-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| VA Section 508 Office URL Ownership Prediction Model VA-24-1897 · OIT: Office of Information & Technology | VA | Deployed | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| Design Optimization of Turbomachinery Rotor Blades using Neural Network Surrogate Models NASA-526 · GRC: Glenn Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |